We cooperate with other people despite the risk of being exploited or hurt. If future artificial intelligence (AI) systems are benevolent and cooperative toward us, what will we do in return? Here we show that our cooperative dispositions are weaker when we interact with AI. In nine experiments, humans interacted with either another human or an AI agent in four classic social dilemma economic games and a newly designed game of Reciprocity that we introduce here. Contrary to the hypothesis that (...) people mistrust algorithms, participants trusted their AI partners to be as cooperative as humans. However, they did not return AI's benevolence as much and exploited the AI more than humans. These findings warn that future self-driving cars or co-working robots, whose success depends on humans' returning their cooperativeness, run the risk of being exploited. This vulnerability calls not just for smarter machines but also better human-centered policies. (shrink)

In this chapter I consider a common refrain among critics of digital platforms: big tech "exploits" us. It gives voice to a shared sense that technology firms are somehow mistreating people—taking advantage of us, extracting from us—in a way that other data-driven harms, such as surveillance and algorithmic bias, fail to capture. Exploring several targets of this charge—gig work, algorithmic pricing, and surveillance advertising—I ask: What does exploitation entail, exactly, and how do platforms perpetrate it? Is exploitation in (...) the platform economy a new kind of exploitation, or are these old problems dressed up as new ones? What would a theory of digital exploitation add to our understanding of the platform age? First, I define exploitation and argue that critics are justified in describing many platform practices as wrongfully exploitative. Next, I focus on platforms themselves—both as businesses and technologies—in order to understand what is and isn’t new about the kinds of exploitation we are witnessing. In some cases, digital platforms perpetuate familiar forms of exploitation by extending the ability of exploiters to reach and control exploitees. In other cases, they enable new exploitative arrangements by creating or exposing vulnerabilities that powerful actors couldn't previously leverage. On the whole, I argue, the language of exploitation helps express forms of injustice overlooked or only partially captured by dominant concerns about, e.g., surveillance, discrimination, and related platform abuses, and it provides valuable conceptual and normative resources for challenging efforts by platforms to obscure or legitimate them. (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow neural (...) network with maximum of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow neural (...) network with maximum of two connected layers to overcome overfitting from the scratch 4) building a Bayesian inference based computation algorithm that intertwine with connected network and is able to create new and adjust existing data points in response to an exposure to new out-of-distribution variants of existing or new malware family which determines the extent at which model weight is adjusted which in turn triggers update on the gradient. Preliminary result of our proposed framework gave an accuracy of 81\% in the successful classification of a novel out-of-distribution malware attack, something that could not be achieve by any of the state-of-the-art algorithms on novel malware classification. (shrink)

Some recent exchanges (Gebharter 2017a,2017b; Baumgartner and Cassini, 2023) concern whether composition can have conditional independence properties analogous to causal relations. If so, composition might sometimes be detectable by the application of causal search algorithms. The discussion has focused on a particular algorithm, PC (Spirtes and Glymour, 1991). PC is but one, and in many circumstances not the best, of a host of causal search algorithms that are candidates for methods of discovering composition provided appropriate statistical relations obtain. The (...) discussion raises two issues: 1. Does the structure of the composition relation entail probability relations such that PC and like algorithms cannot discover composition from frequency data about kinds; and 2. what can be discovered—and how—about the composition of systems by PC or related causal search algorithms that exploit conditional independence relations. Baumgartner and Cassini answer the first question positively: constitution entails probability relations incompatible with discovery by PC. They do not engage the second question, but we will. (shrink)

In the privacy preserving data mining, the utility mining casts a very vital part. The objective of the suggested technique is performed by concealing the high sensitive item sets with the help of the hiding maximum utility item first (HMUIF) algorithm, which effectively evaluates the sensitive item sets by effectively exploiting the user defined utility threshold value. It successfully attempts to estimate the sensitive item sets by utilising optimal threshold value, by means of the grey wolf optimisation (GWO) (...) class='Hi'>algorithm. The optimised threshold value is then checked for its performance analysis by employing several constraints like the HF, MC and DIS. The novel technique is performed and the optimal threshold resultant item sets are assessed and contrasted with those of diverse optimisation approaches. The novel HMUIF considerably cuts down the calculation complication, thereby paving the way for the enhancement in hiding performance of the item sets. (shrink)

Description logics and other formal devices are frequently used as means for preventing or detecting mistakes in ontologies. Some of these devices are also capable of inferring the existence of inter-concept relationships that have not been explicitly entered into an ontology. A prerequisite, however, is that this information can be derived from those formal definitions of concepts and relationships which are included within the ontology. In this paper, we present a novel algorithm that is able to suggest relationships among (...) existing concepts in a formal ontology that are not derivable from such formal definitions. The algorithm exploits cross-lingual information that is implicitly present in the collection of terms used in various languages to denote the concepts and relationships at issue. By using a specific experimental design, we are able to quantify the impact of cross-lingual information in coping with underspecification in formal ontologies. (shrink)

Phishing attacks are major threats to email security and pose challenges, while cyber attackers utilize increasingly sophisticated means to deceive the user and steal away important information. Well-established ways of detecting phishing attacks, such as rule-based systems or simple machine-learning models, usually cannot deal efficiently with such advanced threats. This research proposes an approach to detect phishing attacks on email systems, which deploys natural language processing and deep learning technologies. The method proposes to improve the detection accuracies and efficiencies of (...) phishing emails, which consequently enhances the protection of emails against popular cyberattack attempts and aids in securing users from such attacks. The research involves designing an active model that utilizes the strength of NLP-based text analysis for DL-oriented pattern identification. The NLP techniques used in this study include tokenization, stop-word removal, and context- based analysis to extract significant features from the email messages. Such context information greatly helps this model differentiate between actual emails and phishing ones. Deep learning algorithms exploited here are based on CNN and LSTM networks, offering sinusoidally varying parameters for optimum recognition of patterns from different perspectives in the email data's spatial and temporal domains. The experiment results validated that the hybrid model was far superior to the conventional methods in robbing phish attack detection. The model stood at an accuracy of 97.5%, much ahead of the baseline models purely having rule-based systems or traditional machine learning algorithms. The model also performed well under real-time detection conditions, with low latency and high throughput support for deployment in an active email environment. Evidence of the model's capability to sense new variants of future phishing threats also came in the automatic updating and continuous learning, thus making it applicable to newly emerging threats. The study's practical implications are numerous in providing advances in email protection. Companies can use the model to ward off phishing attacks with reduced chances of data breaches and financial loss. Due to its low cost and scalable features, this solution can be used in organizations from small- and medium-sized to large enterprise levels. The improved detection of phishing attacks brings organizations closer to compliance with data protection and cybersecurity regulations, thus minimizing their chances of noncompliance and the fines that come with it. Future research paths include exploring even newer NLP methods consisting of transformer-based models for feature extraction about contextual understanding. Exploring hybrid approaches that can merge DL with other techniques, such as reinforcement learning in ML, will create a more robust and adaptive phishing filter. Future fields for research are multimodal data, that is, a mixture of email metadata and behavior. Merely considering hybrid techniques merging artificial intelligence with machine learning approaches such as reinforcement learning can generate an even more robust and adaptive phishing filter. Coupling all these technical solutions with user awareness and education programs would greatly enhance overall security. (shrink)

Reinforcement Learning (RL) has emerged as a powerful technique for optimizing decision-making in dynamic, uncertain, and complex environments. The ability of RL algorithms to adapt and learn from interactions with the environment enables them to solve challenging problems in fields such as robotics, autonomous systems, finance, and healthcare. In dynamic environments, where conditions change in real-time, RL must continually update its policy to maximize cumulative rewards. This paper explores the application of RL in dynamic environments, with a focus on its (...) ability to optimize real-time decision-making for complex systems. We discuss the challenges associated with these environments, such as non-stationarity, partial observability, and the trade-off between exploration and exploitation. Furthermore, we review recent advancements in RL techniques, including deep reinforcement learning (DRL), multi-agent RL, and model-based RL, and how these methods are addressing the complexity of real-time decision-making. Finally, we present a roadmap for future research, highlighting open questions and potential applications of RL in various industries. (shrink)

With advancements in quantum computing, existing public-key cryptographic standards such as RSA and Elliptic Curve Cryptography (ECC) face an impending risk of obsolescence. These foundational systems underpin critical components of secure communications, from VPNs to TLS-protected web traffic. This paper evaluates the current state of enterprise preparedness for post-quantum cryptography (PQC) and proposes a structured transition roadmap. We analyse leading post-quantum algorithms submitted to the NIST standardization process, particularly focusing on latticebased (Kyber, NTRU) and code-based (Classic McEliece) cryptosystems. Each is (...) compared in terms of key size, computational overhead, and deployment complexity. A cross-industry survey of 27 enterprise IT departments reveals a significant preparedness gap: nearly half of respondents were unaware of quantum threats, and none reported a concrete migration plan. Using simulation environments integrating OpenSSL and WireGuard, we assess performance impacts of various PQC algorithms under realistic conditions. While post-quantum solutions do introduce increased computational costs, our findings suggest they are viable for implementation in high-value and internal systems. The study concludes by presenting a Quantum-Safe Readiness Model (QSRM), identifying phases of enterprise transition, and advocating for immediate pilot testing and policy-level engagement. This research underscores the urgency of beginning the migration process now to defend against potential “harvest now, decrypt later” scenarios exploited by advanced adversaries. (shrink)

We present an algorithm for concept combination inspired and informed by the research in cognitive and experimental psychology. Dealing with concept combination requires, from a symbolic AI perspective, to cope with competitive needs: the need for compositionality and the need to account for typicality effects. Building on our previous work on weighted logic, the proposed algorithm can be seen as a step towards the management of both these needs. More precisely, following a proposal of Hampton [1], it combines (...) two weighted Description Logic formulas, each defining a concept, using the following general strategy. First it selects all the features needed for the combination, based on the logical distinc- tion between necessary and impossible features. Second, it determines the threshold and assigns new weights to the features of the combined concept trying to preserve the relevance and the necessity of the features. We illustrate how the algorithm works exploiting some paradigmatic examples discussed in the cognitive literature. (shrink)

For several years, scholars have (for good reason) been largely preoccupied with worries about the use of artificial intelligence and machine learning (AI/ML) tools to make decisions about us. Only recently has significant attention turned to a potentially more alarming problem: the use of AI/ML to influence our decision-making. The contexts in which we make decisions—what behavioral economists call our choice architectures—are increasingly technologically-laden. Which is to say: algorithms increasingly determine, in a wide variety of contexts, both the sets of (...) options we choose from and the way those options are framed. Moreover, artificial intelligence and machine learning (AI/ML) makes it possible for those options and their framings—the choice architectures—to be tailored to the individual chooser. They are constructed based on information collected about our individual preferences, interests, aspirations, and vulnerabilities, with the goal of influencing our decisions. At the same time, because we are habituated to these technologies we pay them little notice. They are, as philosophers of technology put it, transparent to us—effectively invisible. I argue that this invisible layer of technological mediation, which structures and influences our decision-making, renders us deeply susceptible to manipulation. Absent a guarantee that these technologies are not being used to manipulate and exploit, individuals will have little reason to trust them. (shrink)

Christiaens argues that digital technologies are fundamentally undermining workers’ autonomy by enacting systems of surveillance that lead to exploitation, alienation, and exhaustion. For a more sustainable future of work, digital technologies should support human development instead of subordinating it to algorithmic control.

It is becoming increasingly common for authoritarian regimes to leverage digital technologies to surveil, repress and manipulate their citizens. Experts typically refer to this practice as digital authoritarianism (DA). Existing definitions of DA consistently presuppose a politically repressive agent intentionally exploiting digital technologies to pursue authoritarian ends. I refer to this as the intention-based definition. This paper argues that this definition is untenable as a general description of DA. I begin by illustrating the current predominance of the intention-based definition (Section (...) 2). Section 3 then presents four counterexamples to this definition: benign surveillance; digital sovereignty; attention-harvesting algorithms; and tech-induced loneliness. In each case, we witness authoritarianism being promoted by digital technologies without any evidence of this being intentionally caused by politically repressive agents. Based on these observations, I contend that the intention-based definition is underinclusive and is therefore unsustainable. Section 4 outlines an improved definition of DA – what I call the promotion-based definition. Since this more expansive definition does not posit intentional, politically repressive agency as a precondition of DA, it can accommodate the counterexamples discussed in Section 3. Moreover, it enables us to catch a broader spectrum of cases of DA, such as tech-induced loneliness, which those adhering to the intention-based definition are prone to overlook. After outlining further practical benefits of the promotion-based definition, I argue that we still need to distinguish between intentional and unintentional forms of DA since they call for distinct types of remedial action. -/- . (shrink)

The aim of this project to expose the reasons behind the pandemic of misinformation (henceforth, PofM) by examining the enabling conditions of epistemic agency and the emerging technologies that threaten it. I plan to research the emotional origin of epistemic agency, i.e. on the origin of our capacity to acquire justification for belief, as well as on the significance this emotional origin has for our lives as epistemic agents in our so-called Misinformation Age. This project has three objectives. First, I (...) plan to expose the degree to which epistemic agency is made possible by an under-researched species of emotion called epistemic feelings. Perhaps, the most epistemically significant is the feeling of confidence. In particular, epistemic feelings make epistemic agency possible by making errors in reasoning salient or the potential lack thereof. -/- Second, in order to diagnose the reasons for PofM, I will analyze the emotional basis of epistemic agency in the context of emerging technologies. Epistemic feelings ought to be construed as motivators of epistemic acts, specifically acts following exposure to misinformation spread by social media. For example, a recent study found that subjects on YouTube have a 6.3% probability in five clicks to go from watching innocuous videos to misogynistic videos. Accordingly, one prominent view holds that social media algorithms lead subjects down the proverbial rabbit hole, producing content that elicits strong emotional reactions. -/- But under-researched is the degree to which these emotions are intertwined with epistemic feelings, so that subjects are likely to misjudge the information as correct due to being primed to do so by processing the AI's suggested media. Thus, when this technology is used in epistemic practices, the result is invariably epistemically bad behavior because of how this manner of producing content motivates epistemic acts through the production of erroneous feelings of confidence, which result from the perceived ease of cognitive processing: the content is easy to process and so, the subject confidently misjudges, it is true. As of yet, an account of this cognitive and emotional decision-making process has not been used to analyze how emerging technologies are threatening epistemic agency, and thus our wellbeing as knowers. -/- Finally, I plan to develop therapeutics for PofM by outlining how to reform our collective epistemic practices. Thus, this project complements existing research in virtue epistemology, specifically by articulating the challenges to cultivating epistemic virtues and warding off the vices of the mind. In summation, the project aims to account for how emerging technologies are committing a form of epistemic injustice through the exploitation of cognitive biases and the production of erroneous epistemic feelings, with the overarching goal being a framework for convalescing from the pandemic of misinformation plaguing our age. (shrink)

Contemporary philosophy and theoretical psychology are dominated by an acceptance of content-externalism: the view that the contents of one's mental states are constitutively, as opposed to causally, dependent on facts about the external world. In the present work, it is shown that content-externalism involves a failure to distinguish between semantics and pre-semantics---between, on the one hand, the literal meanings of expressions and, on the other hand, the information that one must exploit in order to ascertain their literal meanings. It is (...) further shown that, given the falsity of content-externalism, the falsity of the Computational Theory of Mind (CTM) follows. It is also shown that CTM involves a misunderstanding of terms such as "computation," "syntax," "algorithm," and "formal truth." Novel analyses of the concepts expressed by these terms are put forth. These analyses yield clear, intuition-friendly, and extensionally correct answers to the questions "what are propositions?, "what is it for a proposition to be true?", and "what are the logical and psychological differences between conceptual (propositional) and non-conceptual (non-propositional) content?" Naively taking literal meaning to be in lockstep with cognitive content, Burge, Salmon, Falvey, and other semantic externalists have wrongly taken Kripke's correct semantic views to justify drastic and otherwise contraindicated revisions of commonsense. (Salmon: What is non-existent exists; at a given time, one can rationally accept a proposition and its negation. Burge: Somebody who is having a thought may be psychologically indistinguishable from somebody who is thinking nothing. Falvey: somebody who rightly believes himself to be thinking about water is psychologically indistinguishable from somebody who wrongly thinks himself to be doing so and who, indeed, isn't thinking about anything.) Given a few truisms concerning the differences between thought-borne and sentence-borne information, the data is easily modeled without conceding any legitimacy to any one of these rationality-dismantling atrocities. (It thus turns out, ironically, that no one has done more to undermine Kripke's correct semantic points than Kripke's own followers!). (shrink)

The aim of this paper is to present and discuss the issue of the adequacy of the Minimum Intelligent Signal Test (MIST) as an alternative to the Turing Test. MIST has been proposed by Chris McKinstry as a better alternative to Turing’s original idea. Two of the main claims about MIST are that (1) MIST questions exploit commonsense knowledge and as a result are expected to be easy to answer for human beings and difficult for computer programs; and that (2) (...) the MIST design aims at eliminating the problem of the role of judges in the test. To discuss these design assumptions we will present Peter D. Turney’s PMI-IR algorithm which allows for MIST-type questions to be answered. We will also present and discuss the results of our own study aimed at the judge problem for MIST. (shrink)

In this chapter I present the thesis of Proper Embodiment: the claim that (at least some of) the details of our physiology matter to cognition and consciousness in a fundamental way. This thesis is composed of two sub-claims: (1) if we are to design, build, or evolve artificial systems that are cognitive in the way that we are, these systems will have to be internally embodied, and (2) the exploitation of the particular internal embodiment that allows systems to evolve (...) solutions with greatly decreased computational complexity – and thus to be flexible and adaptive in the ways that are characteristic of cognitive systems – means that the orthodox distinction between algorithm and implementation is undermined. Evolved cognitive systems are therefore likely not to instantiate the distinction between phenomenology, algorithm, and implementation. The leaky levels evident in evolved cognitive systems motivate an extension of orthodox embodied cognitive science to the internal, affective, “gooey” realm that has so far only been embraced by those in the enactive tradition. This suggests that if we are to build artificial systems that will be genuinely cognitive they will have to incorporate parts of the gooey realm currently considered to be “mere implementation.”. (shrink)

This dissertation compares the notions of homo rationalis in Philosophy and homo oeconomicus in Economics. Particularly, in Part I, we claim that both notions are close methodological substitutes. Accordingly, we show that the constraints involved in the notion of economic rationality apply to the philosophical notion of rationality. On these premises, we explore the links between the notions of Kantian and Humean rationality in Philosophy and the constructivist and ecological approaches to rationality in economics, respectively. Particularly, we show that the (...) constraints involved in both approaches to rationality in economics apply to the notions of Kantian and Humean rationality. That is why we introduce the notion of minimal global rationality. In a nutshell, the latter links one’s rationality to one’s ability to make good use of their cognitive apparatus to process contextual variables. On this basis, in Part II, we present two empirical tests showing that minimal global rationality is at work in philosophical experiments because systematic manipulations of contextual variables predictably affect philosophical judgment in the Trolley Problem and in some Knobe-like vignettes. In the first case, the manipulation of the information provided to subjects predictably causes inconsistent judgments. More precisely, subjects’ response to information shocks predictably causes them to issue inconsistent judgments. Instead, in the second case, our experimental results reveal that the Knobe Effect activates only if the perpetrator of an action is likely to be perceived as evil and unfriendly. Besides, when the Knobe-like vignettes include probabilistic outcomes, the Knobe Effect does not activate or is reduced in magnitude. For these reasons, we maintain that the availability heuristic bias arguably affects one’s judgment in Knobe-like experiment settings. Eventually, in Part III, we survey the general implications of the findings presented in Part II. First and foremost, we show that when manned social media algorithms produce wide-ranging availability cascades, agents predictably respond to those cascades by herding around biased claims. Interestingly, we find that the exposure to similarly redundant online content makes new social groups emerge unhindered. That is why populists and conspiracy theorists obtain such a large follow-up online. Secondly, we find that manned social media algorithms can exploit the flaws of minimal global rationality at a faster pace than humans. Yet, given that latter algorithms cannot do so on their own, we argue that the Turing Test is still an open game. Specifically, we argue so because state-of-the-art technology can request a service to be supplied (e.g., Google Duplex) but cannot operate direct sales. This constraint of state-of-the-art technology is linked to its inability to exploit the flaws of global minimal rationality as well as humans can. Importantly, we show that if algorithms were able to do so, the involved sociopolitical risk would be high. Therefore, we argue that only some form of regulation over state-of-the-art technology can prevent and reduce such sociopolitical risks. -/- In case you want a copy of this dissertation, please email me. (shrink)

The advent of generative artificial intelligence (AI) in the financial technology (FinTech) sector has created unprecedented opportunities for automating and enhancing financial advisory systems. This research focuses on the application of generative AI to develop automated financial advisory platforms, integrating natural language processing (NLP) and reinforcement learning (RL) for the formulation of personalized investment strategies. Traditional financial advisory models, often characterized by manual processes, human bias, and limited scalability, are increasingly unable to meet the demands of a fast-paced and diverse (...) investment landscape. In response, AI-driven systems present a transformative approach, leveraging the power of generative models to process vast amounts of data and provide real-time, tailored financial recommendations to both retail and institutional investors. This study delves into the technical mechanisms underpinning the integration of generative models with NLP and RL frameworks. Generative models, including variational autoencoders (VAEs) and generative adversarial networks (GANs), play a critical role in simulating complex financial scenarios and generating investment strategies that reflect dynamic market conditions. By synthesizing vast amounts of historical market data, these models create high-dimensional representations of financial environments, which are then used to train reinforcement learning agents. The RL agents learn optimal investment strategies through continuous interaction with these simulated environments, dynamically adjusting to new market information and user preferences. This ability to simulate and optimize investment decisions allows for more sophisticated, personalized strategies, as compared to conventional rule-based systems. Natural language processing enhances the system by enabling it to process unstructured data from various sources, including financial news, reports, and social media, which can significantly impact market trends. NLP models, particularly transformer-based architectures like BERT and GPT, are employed to extract, interpret, and summarize relevant textual information in real-time, feeding it into the generative and RL models. This integration allows the financial advisory system to understand and respond to both quantitative and qualitative factors affecting financial markets. Moreover, the NLP component supports direct interaction between the AI-driven system and users, facilitating personalized communication and user-specific strategy recommendations. This two-way communication is pivotal in enhancing customer engagement, as users can input preferences, risk tolerance, and financial goals, which the system continuously refines and incorporates into its investment strategy formulation. Reinforcement learning plays a pivotal role in the adaptive learning process, allowing the system to improve its decision-making over time by receiving feedback from the environment, such as market performance and user satisfaction. Specifically, model-free RL approaches like Q-learning and policy gradient methods are applied to optimize investment strategies. These approaches enable the system to evaluate multiple potential actions in real-time and select those with the highest expected return, given the current market state and individual user profile. Over time, the RL agent learns to maximize cumulative returns by balancing exploration of new strategies with the exploitation of known profitable actions. By leveraging RL in tandem with generative models, the system can autonomously adjust its strategy in response to changing market conditions and user requirements, thereby delivering a highly customized investment plan that evolves with the investor’s financial landscape. The potential of these AI-driven advisory systems lies not only in their technical sophistication but also in their ability to democratize financial planning. Traditionally, high-quality financial advisory services have been accessible primarily to affluent individuals or large institutions due to the high cost of personalized financial advice. By automating the advisory process through AI, these systems can provide personalized financial planning at scale, making high-quality investment strategies accessible to a broader range of users, including those with limited financial literacy or smaller investment portfolios. This democratization of financial services is particularly significant in the context of retail investors, who can now access sophisticated financial insights and recommendations that were previously reserved for institutional clients. Furthermore, this paper explores the broader implications of AI-driven financial advisory systems on investor behavior and decision-making. By providing real-time, data-driven insights and personalized investment strategies, these systems have the potential to mitigate common cognitive biases in financial decision-making, such as overconfidence, loss aversion, and herd behavior. Through continuous learning and adaptation, AI-driven systems can guide users towards more rational, objective investment decisions, potentially improving overall financial outcomes for both retail and institutional investors. However, the paper also addresses the challenges associated with the deployment of AI in financial advisory systems, including issues of data privacy, algorithmic transparency, and the need for robust regulatory frameworks to ensure the ethical and responsible use of AI in financial decision-making. (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties (...) of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) . (shrink)

Fully Homomorphic Encryption (FHE) represents a transformative approach to securing payment transactions, particularly Card-Not-Present (CNP) transactions in e-commerce environments. This article explores how FHE addresses the fundamental vulnerability in current payment security frameworks: the necessity to decrypt sensitive data for processing. By enabling computation on encrypted data, FHE maintains complete data privacy throughout the transaction lifecycle, eliminating exposure points that hackers traditionally exploit. The synergistic integration of FHE with existing tokenization technologies creates a multi-layered security approach that significantly enhances protection (...) for payment data while enabling previously impossible collaborative fraud detection across organizational boundaries. Despite its compelling security benefits, FHE implementation faces substantial challenges including integration complexity with legacy systems, performance overhead, standardization requirements, and interoperability concerns across heterogeneous payment ecosystems. This article analyzes these challenges alongside emerging optimization techniques and implementation strategies that show promise for overcoming current adoption barriers. As computational efficiency continues to improve through hardware acceleration and algorithmic innovations, FHE stands poised to revolutionize payment security by fundamentally altering how sensitive financial data is processed in distributed environments. (shrink)

Cyber espionage campaigns pose significant challenges to global security, exploiting vulnerabilities in network infrastructures. This research paper explores advanced network traffic analysis models tailored for detecting sophisticated cyber espionage operations. The study focuses on leveraging machine learning algorithms, anomaly detection systems, and hybrid threat detection frameworks to identify subtle yet malicious activities within network traffic. Through a review of research, this paper synthesizes key findings and outlines practical applications, offering a roadmap for enhancing cybersecurity frameworks. Findings highlight the efficacy of (...) deep learning and unsupervised models in mitigating espionage-related risks, paving the way for more robust detection systems. (shrink)

-/- Title: The Psychological Foundations of Political Attachment and Strategies for Countering Mass Brainwashing Author: Angelito Malicse -/- Abstract: This paper explores the psychological and neurological mechanisms underlying the intense emotional attachment individuals form toward political figures, often without personal interaction. It examines how such attachments can be manipulated to facilitate mass brainwashing, resulting in cognitive rigidity, social polarization, and the erosion of democratic values. The paper proposes a multi-faceted strategy to counter these effects through foundational educational reform, media literacy, (...) civic awareness, and institutional safeguards, all rooted in a scientific understanding of human behavior and natural law. -/- 1. Introduction -/- In democratic and authoritarian societies alike, the deep emotional connection between political supporters and their chosen leaders has increasingly influenced national discourse and governance. These attachments frequently transcend rational evaluation and persist despite evidence of incompetence, corruption, or ethical breaches. This phenomenon, while often attributed to blind loyalty or ignorance, has scientifically identifiable roots in evolutionary psychology, neuroscience, and cognitive theory. Understanding these mechanisms is essential for developing effective strategies to counter mass manipulation and preserve rational public discourse. -/- 2. Psychological Mechanisms of Political Attachment -/- 2.1 Tribalism and Social Identity Theory 2.2 Human beings evolved in cooperative tribal units where group cohesion was essential for survival. This evolutionary trait persists in the form of social identity, wherein individuals derive self-esteem and belonging from affiliations with larger collectives—such as political parties or ideologies. As posited by Social Identity Theory (Tajfel & Turner, 1979), individuals often perceive their political group as superior, viewing political opposition as a threat to their personal and collective identity. -/- 2.3 Parasocial Relationships and Emotional Bonding 2.4 Through repeated media exposure, supporters often develop parasocial relationships with political figures—one-sided emotional bonds that mimic genuine interpersonal relationships. These relationships are reinforced by charismatic communication, personal storytelling, and the strategic use of shared values or backgrounds (Horton & Wohl, 1956). -/- 2.3 Limbic System Activation and Emotional Conditioning -/- Emotional stimuli such as fear, anger, pride, or hope activate the limbic system, particularly the amygdala, which plays a central role in emotional processing and memory formation (LeDoux, 1996). Political rhetoric is frequently designed to elicit these reactions, bypassing rational evaluation and appealing directly to emotional instincts. -/- 2.5 Cognitive Dissonance and Motivated Reasoning 2.6 Once an emotional attachment is established, individuals are less likely to accept information that challenges their beliefs about a political figure. This phenomenon, known as cognitive dissonance (Festinger, 1957), produces psychological discomfort when new evidence contradicts existing attitudes. To alleviate this discomfort, individuals engage in motivated reasoning (Kunda, 1990), selectively interpreting or dismissing information to preserve their beliefs. -/- 2.5 Neurological Reward Systems and Habit Formation -/- Political activity, such as attending rallies, defending a leader online, or participating in collective actions, often results in dopamine release—the brain’s reward signal. These neurological rewards reinforce behaviors and create psychological dependence on political engagement (Schultz, 1998). -/- 3. The Dynamics and Consequences of Mass Brainwashing -/- When the above psychological processes are deliberately exploited—through propaganda, disinformation, algorithmic manipulation, or emotionally charged messaging—they culminate in what can be termed mass brainwashing. The dangers include: -/- Social polarization, fostering hostility between groups. -/- Suppression of critical thinking, as individuals adhere to emotionally comforting but factually erroneous beliefs. -/- Democratic erosion, where public accountability is diminished. -/- Systemic dysfunction, as governance becomes rooted in loyalty rather than performance. -/- 4. Counteracting Mass Brainwashing: A Scientific and Educational Approach -/- 4.1 Foundational Educational Reform 4.2 The most effective defense against manipulation lies in basic educational reform that incorporates: -/- Critical thinking and logic -/- Media and information literacy -/- Emotional intelligence and self-regulation -/- Ethics and philosophy -/- This approach aligns with the universal law of balance in nature, which asserts that sustainable decisions arise from harmony between internal cognition and external reality. -/- 4.3 Self-Awareness and Psychological Insight 4.4 Individuals must be taught to recognize their own emotional and cognitive biases through self-reflection, mindfulness, and meta-cognition. These skills empower them to remain vigilant against manipulation. -/- 4.3 Civic Literacy and Responsible Citizenship -/- A well-informed citizenry that understands governance structures, democratic processes, and accountability mechanisms is better equipped to evaluate political figures based on evidence and ethics, not emotional loyalty. -/- 4.5 Transparent Media and Ethical Technology Regulation 4.6 Social media platforms and news outlets must be held accountable to uphold truthfulness, algorithmic transparency, and public responsibility, ensuring that emotional manipulation is minimized. -/- 4.5 Institutional Integrity and Independent Oversight -/- To preserve public trust, institutions must function autonomously and ethically, shielded from political influence and equipped with scientific frameworks to monitor and counter disinformation campaigns. -/- 5. Conclusion -/- The emotional attachment of citizens to political figures they have never met is a scientifically explainable phenomenon rooted in tribal instincts, emotional conditioning, cognitive bias, and neurological reward systems. While natural, these mechanisms are vulnerable to exploitation through organized mass brainwashing. The consequences—social division, diminished reason, and weakened democratic structures—necessitate a coordinated response. Through holistic education and ethical institution-building, grounded in a scientific understanding of natural laws, society can be safeguarded from emotional manipulation and restored to balanced, rational civic engagement. -/- References -/- Festinger, L. (1957). A Theory of Cognitive Dissonance. Stanford University Press. -/- Horton, D., & Wohl, R. R. (1956). Mass communication and para-social interaction: Observations on intimacy at a distance. Psychiatry, 19(3), -/- Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108(3) -/- LeDoux, J. E. (1996). The Emotional Brain: The Mysterious Underpinnings of Emotional Life. Simon & Schuster. -/- Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80(1) -/- Tajfel, H., & Turner, J. C. (1979). An integrative theory of intergroup conflict. In W. G. Austin & S. Worchel (Eds.), The Social Psychology of Intergroup Relations (pp. 33–47). Brooks/Cole. -/- . (shrink)

The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am guided by a specific (...) theoretical goal associated with the need to indicate those conditions that should be met by the non-trivial theory of normative mechanisms and the specific models proposed by PP supporters. In this work, I use classical philosophical methods, such as conceptual analysis and critical reflection. I also analyze selected studies in the field of cognitive science, cognitive psychology, neurology, information theory and biology in terms of the methodology, argumentation and language used, in accordance with their theoretical importance for the issues discussed in this study. In this sense, the research presented here is interdisciplinary. My research framework is informed by the mechanistic model of explanation, which defines the necessary and sufficient conditions for explaining a given phenomenon. The research methods I chose are therefore related to the problems that I intend to solve. In the introductory chapter, “The concept of predictive processing”, I discuss the nature of PP as well as its main assumptions and theses. I also highlight the key concepts and distinctions for this research framework. Many authors argue that PP is a contemporary version of Kantianism and is exposed to objections similar to those made against the approach of Immanuel Kant. I discuss this thesis and show that it is only in a very general sense that the PP framework is neo-Kantian. Here we are not dealing with transcendental deduction nor with the application of transcendental arguments. I argue that PP is based on reverse engineering and abduction inferences. In the second part of this chapter, I respond to the objection formulated by Dan Zahavi, who directly accuses this research framework of anti-realistic consequences. I demonstrate that the position of internalism, present in the so-called conservative PP, does not imply anti-realism, and that, due to the explanatory role played in it by structural representations directed at real patterns, it is justified to claim that PP is realistic. In this way, I show that PP is a non-trivial research framework, having its subject, specific methods and its own epistemic status. Finally, I discuss positions classified as the socalled radical PP. In the chapter “Predictive processing as a Bayesian explanatory model” I justify the thesis according to which PP offers Bayesian modeling. Many researchers claim that the brain is an implemented statistical probabilistic network that is an approximation of the Bayesian 7 rule. In practice, this means that all cognitive processes are to apply Bayes' rule and can be described in terms of probability distributions. Such a solution arouses objections among many researchers and is the subject of wide criticism. The purpose of this chapter is to justify the thesis that Bayesian PP is a non-trivial research framework. For this purpose, I argue that it explains certain phenomena not only at the computational level described by David Marr, but also at the level of algorithms and implementation. Later in this chapter I demonstrate that PP is normative modeling. Proponents of the use of Bayesian models in psychology or decision theory argue that they are normative because they allow the formulation of formal rules of action that show what needs to be done to make a given action optimal. Critics of this approach emphasize that such thinking about the normativity of Bayesian modeling is unjustified and that science should shift from prescriptive to descriptive positions. In a polemic with Shira Elqayam and Jonathan Evans (2011), I show that the division they propose into prescriptivism and Bayesian descriptivism is apparent, because, as I argue, there are two forms of prescriptivism, i.e. the weak and the strong. I argue that the weak version is epistemic and can lead to anti-realism, while the strong version is ontic and allows one to justify realism in relation to Bayesian models. I argue that a weak version of prescriptivism is valid for PP. It allows us to adopt anti-realism in relation to PP. In practice, this means that you can explain phenomena using Bayes' rule. This does not, however, imply that they are Bayesian in nature. However, the full justification of realism in relation to the Bayesian PP presupposes the adoption of strong prescriptivism. This position assumes that phenomena are explained by Bayesian rule because they are Bayesian as such. If they are Bayesian in nature, then they should be explained using Bayesian modeling. This thesis will be substantiated in the chapters “Normative functions and mechanisms in the context of predictive processing” and “Normative mechanisms and actions in predictive processing”. In the chapter “The Free Energy Principle in predictive processing”, I discuss the Free Energy Principle (hereinafter FEP) formulated by Karl Friston and some of its implications. According to this principle, all biological systems (defined in terms of Markov blankets) minimize the free energy of their internal states in order to maintain homeostasis. Some researchers believe that PP is a special case of applying this principle to cognition, and that predictive mechanisms are homeostatic mechanisms that minimize free energy. The discussion of FEP is important due to the fact that some authors consider it to be important for explanatory purposes and normative. If this is the case, then FEP turns out to be crucial in explaining normative predictive mechanisms and, in general, any normative biological mechanisms. To define the explanatory possibilities of this principle, I refer to the discussion of its supporters on the issue they define as the problem of continuity and discontinuity between life and mind. A critical analysis of this discussion and the additional arguments I have formulated have allowed me to revise the explanatory ambitions of FEP. I also reject the belief that this principle is necessary to explain the nature of predictive mechanisms. I argue that the principle formulated and defended by Friston is an important research heuristic for PP analysis. 8 In the chapter “Normative functions and mechanisms in predictive processing”, I start my analyzes by formulating an answer to the question about the normative nature of homeostatic mechanisms. I demonstrate that predictive mechanisms are not homeostatic. I defend the view that a full explanation of normative mechanisms presupposes an explanation of normative functions. I discuss the most important proposals for understanding the normativity of a function, both from a systemic and teleosemantic perspective. I conclude that the non-trivial concept of a function must meet two requirements which I define as explanatory and normative. I show that none of the theories I have invoked satisfactorily meets both of these requirements. Instead, I propose a model of normativity based on Bickhard's account, but supplemented by a mechanistic perspective. I argue that a function is normative when: (1) it allows one to explain the dysfunction of a given mechanism; (2) it contributes to the maintenance of the organism's stability by shaping and limiting possible relations, processes and behaviors of a given system; and when (3) (according to the representational and predictive functions) it enables explaining the attribution of logical values of certain representations / predictions. In such an approach, a mechanism is normative when it performs certain normative functions and when it is constitutive for a specific action or behavior, despite the fact that for some reason it cannot realize it either currently or in the long-term. Such an understanding of the normativity of mechanisms presupposes the acceptance of the epistemic hypothesis. I argue that this hypothesis is not cognitively satisfactory, and therefore the ontic hypothesis should be justified, which is directly related to adopting the position of ontic prescriptivism. For this reason, referring to the mechanistic theory of scientific explanations, I formulate an ontical interpretation of the concept of a normative mechanism. According to this approach, a mechanism or a function is normative when they perform such and such causal roles in explaining certain actions and behaviors. With regard to the normative properties of predictive mechanisms and functions, this means that they are the causes of specific actions an organism carries out in the environment. In this way, I justify the necessity of accepting the ontic hypothesis and rejecting the epistemic hypothesis. The fifth chapter, “Normative mechanisms and actions in predictive processing”, is devoted to the dark room problem and the related exploration-exploitation trade-off. A dark room is the state that an agent could be in if it minimized the sum of all potential prediction errors. I demonstrate that, in accordance with the basic assumption of PP about the need for continuous and long-term minimization of prediction errors, such a state should be desirable for the agent. Is it really so? Many authors believe it is not. I argue that the test of the value of PP is the possibility of a non-trivial solution of this problem, which can be reduced to the choice between active and uncertainty-increasing exploration and safe and easily predictable exploitation. I show that the solution proposed by PP supporters present in the literature does not enable a fully satisfactory explanation of this dilemma. Then I defend the position according to which the full explanation of the normative mechanisms, and, subsequently, the solution to the dilemma of exploration and exploitation, involves reference to the existence of constraints present in the environment. The constraints 9 include elements of the environment that make a given mechanism not only causal but also normative. They are therefore key to explaining the predictive mechanisms. They do not only play the role of the context in which the mechanism is implemented, but, above all, are its constitutive component. I argue that the full explanation of the role of constraints in normative predictive mechanisms presupposes the integration of individual models of specific cognitive phenomena, because only the mechanistic integration of PP with other models allows for a non-trivial explanation of the nature of normative predictive mechanisms that would have a strong explanatory value. The explanatory monism present in many approaches to PP makes it impossible to solve the problem of the dark room. Later in this chapter, I argue that the Bayesian PP is normative not because it enables the formulation of such and such rules of action, but because the predictive mechanisms themselves are normative. They are normative because they condition the choice of such and such actions by agents. In this way, I justify the hypothesis that normative mechanisms make it possible to explain the phenomenon of agent motivation, which is crucial for solving the dark room problem. In the last part of the chapter, I formulate the hypothesis of distributed normativity, which assumes that the normative nature of certain mechanisms, functions or objects is determined by the relations into which these mechanisms, functions or objects enter. This means that what is normative (in the primary sense) is the relational structure that constitutes the normativity of specific items included in it. I suggest that this hypothesis opens up many areas of research and makes it possible to rethink many problems. In the “Conclusion”, I summarize the results of my research and indicate further research perspectives. (shrink)

Beyond Capitalism: Designing a New Economic System for Humanity -/- Introduction -/- For centuries, capitalism has been the dominant economic system, driving technological innovation, wealth creation, and global trade. However, its flaws—extreme inequality, environmental destruction, and economic instability—have made many question whether it is still the best model for humanity. Instead of reforming capitalism, perhaps the time has come to replace it entirely with a new system that better aligns with modern challenges, human well-being, and sustainability. -/- This essay explores (...) a new economic model that moves beyond capitalism and socialism, aiming for a system that is fair, sustainable, and technologically advanced. -/- Why Capitalism Must Be Replaced -/- Capitalism’s fundamental flaws make it unsustainable in the long run: -/- 1. Wealth Inequality and Economic Injustice -/- The richest 1% own more than half of the world’s wealth, while billions struggle in poverty. -/- The capitalist system prioritizes profits over people, leading to worker exploitation and economic instability. -/- 2. Environmental Collapse -/- Capitalism’s endless growth model is destroying the planet through deforestation, pollution, and climate change. -/- Corporations prioritize short-term profits over long-term sustainability. -/- 3. Automation and AI Disrupting Jobs -/- Millions of jobs are being replaced by artificial intelligence and automation. -/- The capitalist system does not account for a future where human labor is no longer essential. -/- 4. Psychological and Social Problems -/- Consumerism creates stress, anxiety, and social isolation by equating happiness with material wealth. -/- The fear of financial instability keeps people trapped in low-quality jobs and debt cycles. -/- Given these issues, capitalism is no longer fit for the future. A completely new economic system must be built—one that values sustainability, equality, and technology-driven efficiency. -/- Introducing a New Economic System: The Post-Capitalist Model -/- A new economic model must prioritize human well-being, technological efficiency, and ecological balance over profit. Below is a blueprint for a post-capitalist society. -/- 1. A Resource-Based Economy (RBE) -/- A Resource-Based Economy (RBE) is an economic model where all goods and services are freely available based on need, rather than money or trade. -/- Technology and AI manage resources efficiently, eliminating the need for money. -/- Automation handles labor, allowing people to pursue education, research, and creativity. -/- No private ownership of land or resources, only shared community access based on sustainability. -/- 2. Universal Basic Infrastructure (UBI 2.0) -/- Instead of just giving people money (UBI), a new system would provide universal basic needs, including: -/- Free housing, healthcare, education, food, and clean energy for all. -/- Advanced AI-driven logistics to distribute goods and services efficiently. -/- 3. Decentralized AI Governance -/- Rather than governments being controlled by corporations or corrupt politicians, AI and collective intelligence could ensure fair and transparent decision-making. -/- AI algorithms could optimize resource distribution, prevent waste, and eliminate financial corruption. -/- Direct democratic participation, where people vote on policies using secure blockchain systems. -/- 4. Sustainable, Circular Economy -/- Zero waste policies, where everything is recycled, reused, or repurposed. -/- Focus on degrowth—reducing unnecessary production while increasing quality of life. -/- Energy-efficient smart cities, where transportation, housing, and food production are automated. -/- 5. Post-Work Society: Emphasizing Purpose Over Jobs -/- In a post-capitalist economy, people would not be forced to work meaningless jobs just to survive. Instead: -/- People could focus on science, philosophy, arts, personal growth, and social well-being. -/- Education would be lifelong and freely available. -/- AI and robots would handle most labor, leaving humans free to explore their full potential. -/- How This System Would Solve Capitalism’s Problems -/- Challenges in Transitioning to a Post-Capitalist Economy -/- While this system solves capitalism’s biggest problems, transitioning away from capitalism would face major challenges: -/- 1. Resistance from the Wealthy Elite -/- Billionaires and corporations control most of the world’s resources and political systems. -/- They would fight to maintain power, delaying or sabotaging systemic change. -/- 2. Psychological and Cultural Shifts -/- People have been conditioned to associate money with survival and success. -/- A new system would require re-educating society on cooperation, sustainability, and shared abundance. -/- 3. Technology and Infrastructure Development -/- Large-scale AI, automation, and sustainable energy infrastructure would need global investment. -/- Governments and private companies must be convinced to prioritize long-term sustainability over short-term profit. -/- The Path Forward: Steps to Build a Post-Capitalist World -/- While the transition will not happen overnight, we can begin moving toward a post-capitalist future by taking the following steps: -/- 1. Implement Universal Basic Income (UBI) Now -/- Governments can start by providing a safety net while transitioning to a money-free economy. -/- Countries like Finland and Canada are already experimenting with UBI programs. -/- 2. Expand AI and Automation for Public Good -/- AI should be used to reduce labor burdens, not to enrich corporations. -/- Governments should invest in open-source AI that benefits all of society. -/- 3. Develop Smart Cities and Circular Economies -/- Cities should be designed for zero waste, renewable energy, and efficient transportation. -/- Governments must ban planned obsolescence and require full recycling of consumer products. -/- 4. Decentralize Power Using Blockchain and Direct Democracy -/- Blockchain voting systems can eliminate election fraud and political corruption. -/- AI-assisted governance can help make fair, data-driven policy decisions. -/- 5. Redefine Education and Human Purpose -/- Schools should move away from job training and focus on creativity, science, philosophy, and social intelligence. -/- Societies should emphasize happiness, purpose, and sustainability over financial success. -/- Conclusion: A New Civilization Beyond Capitalism -/- Capitalism was a necessary stage in human evolution, but it has outlived its usefulness. The future demands a system that is more intelligent, just, and sustainable—one where: ✅ No one is forced to work just to survive. ✅ Resources are managed intelligently, not wasted for profit. ✅ Technology is used for human well-being, not corporate greed. ✅ The planet is protected, not exploited. -/- This is not utopian fantasy—it is a logical step forward in human evolution. By combining AI, automation, decentralized governance, and sustainability, we can create a post-capitalist society where everyone thrives. -/- The question Is no longer “Should we replace capitalism?” The question is: “How soon can we build a better system before capitalism destroys us all?” -/- . (shrink)

The Future of AI-Human Brain Integration: A Path to Balanced Intelligence -/- The rapid advancement of artificial intelligence (AI) has opened the possibility of integrating AI with the human brain, a development that could redefine intelligence, decision-making, and even the nature of consciousness. However, to ensure that AI-brain integration enhances human potential without disrupting the natural order, a holistic and ethical approach must be followed. This essay explores how AI can be merged with human cognition while maintaining balance with nature, (...) preserving free will, and ensuring ethical decision-making. -/- The Role of AI in Human Cognition -/- AI-brain integration is primarily being developed through brain-computer interfaces (BCIs), which allow direct communication between neural activity and AI systems. Companies like Neuralink, BrainGate, and DARPA are pioneering technologies that could enable thought-controlled devices, real-time access to information, and even restoration of lost brain functions. With AI-enhanced intelligence, humans could potentially process information at machine speeds, enhance memory, and achieve new levels of problem-solving ability. -/- However, while AI can serve as an intellectual amplifier, it should not replace human cognition or override natural decision-making processes. Instead, AI should act as a collaborative tool that augments human intelligence rather than diminishing human autonomy. This requires designing AI systems that work in harmony with the human mind rather than dictating human choices. -/- The Universal Law of Balance in AI-Human Integration -/- A fundamental principle that must guide AI-human brain integration is the universal law of balance in nature. This law states that any system—whether biological, technological, or societal—must function without defects or imbalances to remain stable and effective. Applying this principle to AI means ensuring that AI enhances, rather than disrupts, human cognition. -/- To achieve balance, AI should: ✔ Respect natural cognitive processes and avoid overwhelming the brain with excessive data. ✔ Enhance human intuition rather than replacing it with pure algorithmic logic. ✔ Support ethical decision-making rather than introducing biases or manipulation. -/- For example, an AI-enhanced leader should use AI-generated insights as guidance, but the final decision should always involve human ethical reasoning. AI should never dictate human actions but rather present balanced, logical, and moral options. -/- Preserving Free Will and Conscious Decision-Making -/- One of the greatest concerns in AI-human integration is the potential loss of free will. If AI influences human thoughts directly, it could manipulate decision-making in ways that compromise individual autonomy. To prevent this, AI systems should: -/- ✔ Act as advisors, not controllers of human decisions. ✔ Provide transparent reasoning for recommendations rather than imposing outcomes. ✔ Allow users to control the level of AI assistance, ensuring voluntary participation. -/- For example, an AI-enhanced medical assistant could suggest treatment options based on a patient’s medical history but should never override a doctor’s professional judgment. By maintaining human oversight, AI can serve as a powerful tool without becoming a controlling force. -/- The Role of Ethics and Moral Intelligence in AI -/- AI lacks true emotions, empathy, and moral reasoning, which are essential for balanced decision-making. While AI can analyze vast amounts of data, it does not possess human intuition, compassion, or ethical values. To prevent AI from introducing harmful biases or unethical decisions, it must be programmed with moral safeguards. -/- AI should be designed to: ✔ Align with universal ethical principles, ensuring fairness and justice. ✔ Prevent the spread of misinformation, propaganda, or ideological bias. ✔ Respect human emotions and psychological well-being. -/- For instance, if AI is used in education, it should teach critical thinking and ethical reasoning rather than simply providing information. This ensures that AI strengthens human morality rather than weakening it. -/- Using AI to Minimize Human Suffering, Not Exploit It -/- If AI-brain integration is to benefit society, it must be used to reduce suffering, inequality, and social instability. AI should never be used to exploit, manipulate, or divide populations. Instead, AI should be employed to: -/- ✔ Prevent war, poverty, and social conflict through balanced decision-making. ✔ Ensure global stability and economic fairness. ✔ Provide equal access to AI technology, preventing an elite monopoly. -/- For example, AI-enhanced education should be available to all people, not just the wealthy. If only a select group gains access to AI-powered intelligence, it could create a new class divide, where only the elite benefit from AI-driven advantages. Ethical regulations must ensure fair distribution of AI’s benefits. -/- Ethical Regulations for AI-Human Integration -/- To maintain ethical AI development, clear legal and moral guidelines must be established. These include: -/- ✔ No forced AI integration – AI brain interfaces should always be voluntary. ✔ Privacy protection – AI must not access personal thoughts without explicit consent. ✔ Regulated AI power – AI should never have autonomous control over major decisions in government, military, or the economy. -/- For instance, if AI assists in law enforcement, it should analyze crime data but never have the authority to make arrests—that decision must always be made by a human. This ensures that AI remains a tool rather than a ruler. -/- Maintaining Balance Between Organic and Synthetic Life -/- The human experience is deeply connected to nature, emotions, and organic intelligence. AI-brain integration must not disconnect humanity from these fundamental experiences. Instead, AI should: -/- ✔ Enhance human connection with nature and philosophy, not replace it. ✔ Strengthen creativity, spirituality, and emotional intelligence. ✔ Respect the biological limitations of the human mind rather than forcing unnatural digital transformation. -/- For example, AI should help humans develop deeper philosophical insights rather than simply focusing on data processing. This ensures that human wisdom remains the guiding force, with AI acting as a supportive tool. -/- Conclusion: AI as a Tool for Balanced Intelligence -/- The future of AI-human brain integration holds immense potential to enhance intelligence, decision-making, and problem-solving. However, to ensure that AI serves humanity rather than controlling it, we must follow the universal law of balance in nature. AI should always function as a collaborative tool, enhancing human intelligence without overriding free will, ethical reasoning, or natural balance. -/- By integrating AI with careful ethical considerations, transparent decision-making, and a commitment to human dignity, we can create a future where AI and humanity evolve in harmony—a future where intelligence is not just artificial, but truly balanced. -/- . (shrink)

The Dangers of Unfiltered Social Media: A Threat to Society -/- Introduction -/- In the digital age, social media has revolutionized communication, providing an open platform for information sharing, networking, and public discourse. However, the lack of a comprehensive filtering mechanism on social media apps poses significant risks to individuals and society as a whole. Without proper content moderation, social media can become a breeding ground for misinformation, manipulation, social division, and various forms of harm. This essay explores the dangers (...) of unregulated social media and the potential consequences for the global population. -/- The Spread of Misinformation and Disinformation -/- One of the most pressing concerns of unfiltered social media is the rapid spread of misinformation and disinformation. False or misleading content, whether intentional or accidental, can significantly impact public opinion and decision-making. Health misinformation, for example, can lead to individuals rejecting vaccines or adopting unsafe medical practices. Similarly, political disinformation can manipulate elections and undermine democratic processes. The absence of effective content moderation allows misleading narratives to thrive, ultimately destabilizing societies. -/- Manipulation and Propaganda -/- Without a robust filtering system, social media can be weaponized for mass manipulation. Political groups, corporations, and even foreign actors can exploit these platforms to spread propaganda, influence elections, or shift public perception in favor of certain agendas. The use of algorithms to amplify divisive content further exacerbates this issue, making individuals more susceptible to manipulation and reducing critical thinking among the public. -/- The Rise of Hate Speech and Violence -/- Social media platforms that lack adequate moderation become hotspots for hate speech, extremism, and violence incitement. Unfiltered content can encourage racial discrimination, xenophobia, and ideological radicalization, leading to real-world consequences such as hate crimes, riots, and domestic terrorism. The ability to freely share harmful ideologies without oversight increases societal tensions and endangers vulnerable communities. -/- Cyberbullying and Mental Health Crisis -/- The unrestricted nature of social media allows cyberbullying to flourish. Online harassment, threats, and character defamation can have severe psychological effects, especially on young users. Studies have shown that exposure to toxic online interactions correlates with increased anxiety, depression, and even suicide rates. Without effective filters, social media remains a dangerous environment for mental well-being, particularly for impressionable individuals. -/- Scams and Financial Exploitation -/- Another significant threat posed by unregulated social media is the rise of online scams, fraudulent schemes, and identity theft. Fake investment opportunities, phishing links, and impersonation tactics exploit unsuspecting users, leading to financial losses. The absence of content moderation makes it easier for cybercriminals to operate, resulting in widespread economic harm and loss of trust in digital platforms. -/- Social Division and Polarization -/- Social media algorithms often create echo chambers, where users are only exposed to information that aligns with their existing beliefs. This phenomenon reinforces biases, deepens societal divisions, and reduces open-mindedness. Unfiltered content further fuels polarization by amplifying extreme viewpoints and discouraging constructive dialogue. As a result, societies become more fragmented, making cooperation and progress increasingly difficult. -/- Addiction and Behavioral Conditioning -/- The design of social media platforms encourages addictive behaviors, keeping users engaged for prolonged periods. This addiction is exacerbated by unregulated content, which may include sensationalized news, clickbait, and psychologically manipulative material. Excessive social media use can reduce productivity, damage real-world relationships, and lead to cognitive overload, negatively impacting overall quality of life. -/- Privacy Violations and Data Exploitation -/- Without proper content moderation, personal data is at greater risk of misuse. Social media platforms collect vast amounts of user information, which can be exploited for targeted advertising, political influence, or even criminal activities. The lack of stringent privacy measures enables data breaches and unauthorized access to sensitive information, putting users at risk of fraud and surveillance. -/- The Undermining of Democratic Processes -/- Unfiltered social media presents a significant threat to democracy. The spread of fake news, voter suppression tactics, and coordinated misinformation campaigns can erode trust in electoral systems and governance. Political instability can arise when citizens base their decisions on manipulated information rather than objective facts. Without a proper filtering mechanism, social media can be used as a tool for undermining democratic institutions and fostering distrust in government. -/- Conclusion -/- The dangers of unregulated social media are vast and far-reaching. From misinformation and cyberbullying to political manipulation and social division, the absence of effective content filtering poses serious threats to individuals and society. While freedom of speech is a fundamental right, it must be balanced with responsible content moderation to ensure a safer and more informed digital space. Governments, tech companies, and the public must work together to implement solutions that protect users while preserving open discourse. Without action, the negative consequences of unfiltered social media will continue to escalate, jeopardizing the stability and well-being of societies worldwide. -/- . (shrink)

The Violation of the Absolute Law of Free Will: The Consequences of Misinformation and the Flaws in Freedom of Speech -/- Introduction -/- Free will is often regarded as humanity’s defining characteristic—the ability to make choices based on conscious thought, personal experience, and available information. However, free will is not merely about the freedom to choose; it is intrinsically tied to the accuracy and reliability of the information upon which those choices are made. The absolute law of free will, when (...) correctly understood, dictates that human decision-making must be 100 percent dependent on correct information. Any deviation from this principle, through misinformation, disinformation, or ignorance, constitutes a violation of this law and results in imbalanced, often detrimental outcomes for individuals and society as a whole. -/- Complicating this issue is the modern interpretation and implementation of freedom of speech and expression. While these freedoms are fundamental to democratic societies, they come with inherent flaws when unchecked. The unregulated dissemination of false or misleading information under the guise of free speech creates significant challenges in maintaining the integrity of information and, consequently, free will. -/- The Foundation of Free Will: Correct Information -/- The exercise of genuine free will requires access to accurate, reliable information. Decisions made under false pretenses or incomplete knowledge are not truly free but are instead manipulated or constrained by external distortions. The mind operates like a system that processes inputs (information) to generate outputs (decisions and actions). If the input is flawed, the output will inevitably be compromised. -/- For instance, when individuals base their choices on misinformation—such as health-related decisions influenced by false medical advice—they act under the illusion of free will. However, their actions, though seemingly voluntary, are dictated by incorrect data. This demonstrates that true free will can only exist when individuals are fully informed by verifiable facts. -/- The Law of Karma and the Feedback Mechanism of Information -/- The universal law of karma, which involves the principle of cause and effect, also applies to the relationship between free will and information. Every decision and action causes a ripple effect that influences future outcomes. When decisions are made based on falsehoods, the resulting negative consequences, such as social conflict, health crises, and societal instability, reflect the karmic response to the imbalance created by misinformation. -/- Information functions as a feedback mechanism within societal systems. When this feedback loop is polluted with inaccuracies, the entire system becomes unstable. For example, the propagation of false information about climate change has delayed necessary global actions, causing environmental deterioration. The violation of free will through the spread of misinformation disrupts the natural balance of societal decision-making, leading to long-term consequences that extend beyond individual choices. -/- The Flaws In the Laws of Freedom of Speech and Expression -/- Freedom of speech and expression is a cornerstone of democratic societies, allowing individuals to voice their opinions, share ideas, and engage in open discourse. However, this freedom is not absolute and, when exercised irresponsibly, can directly violate the absolute law of free will. The flaws within these legal frameworks become evident when misinformation, disinformation, and harmful propaganda are disseminated without accountability. -/- 1. The Spread of Misinformation and Disinformation The unrestricted nature of freedom of speech has led to the proliferation of misinformation across social, political, and scientific domains. False narratives regarding health, such as anti-vaccine propaganda, have endangered public health and undermined free will by providing individuals with inaccurate information on which to base their decisions. -/- 2. The Manipulation of Public Opinion Political propaganda, fake news, and conspiracy theories exploit the principles of free speech to manipulate public perception. Authoritarian regimes and interest groups often use misinformation to influence elections, sway public opinion, and maintain power. When people make decisions based on manipulated narratives, their free will is compromised. -/- 3. The Role of Social Media and Digital Platforms Social media platforms, while fostering global communication, have amplified the spread of misinformation. Algorithms designed to maximize engagement often prioritize sensational or misleading content over factual information. The lack of regulatory measures on these platforms creates an environment where misinformation spreads more rapidly than truth, resulting in collective decision-making that violates the law of balance. -/- 4. The Challenge of Regulation Regulating freedom of speech without infringing upon individual liberties presents a significant challenge. Striking a balance between protecting free expression and preventing the spread of false information requires well-defined legal frameworks, global cooperation, and ethical considerations. -/- 5. Hate Speech and Its Societal Impact The misuse of freedom of speech to propagate hate speech, incite violence, and promote division disrupts societal harmony. Misinformation about certain groups, ethnicities, or religions fosters discrimination and social unrest, further violating the balance required for the proper exercise of free will. -/- The Societal Impact of Misinformation -/- Misinformation has far-reaching effects on societal structures, particularly in democratic systems where public decision-making relies heavily on the availability of correct information. The manipulation of information through propaganda, conspiracy theories, and biased media distorts collective free will, leading to decisions that do not reflect the genuine, informed will of the people. -/- Elections, for instance, are a prime example. When voters are misled by false narratives or deceptive political strategies, their electoral decisions are no longer expressions of true free will. The outcomes of such manipulated decisions often result in societal discord, poor governance, and disillusionment with democratic processes. In this way, misinformation violates the collective free will of entire populations. -/- The Role of Education in Upholding the Absolute Law of Free Will -/- To protect and uphold the absolute law of free will, society must prioritize a holistic educational system that emphasizes critical thinking, information literacy, and scientific inquiry. Education should equip individuals with the ability to discern fact from fiction, understand the sources of information, and recognize the potential biases inherent in various media channels. -/- An educational system grounded in the universal law of balance in nature can act as a safeguard against misinformation. By teaching individuals how to process information through rational, evidence-based methods, we can foster a societal environment where decisions are consistently made based on truth. This, in turn, ensures that free will is exercised in its purest form. -/- The Role of Artificial General Intelligence (AGI) in Preserving Free Will -/- The rapid advancement of AGI presents both challenges and opportunities regarding the preservation of free will. If AGI systems are designed based on the universal formula that emphasizes accurate information, natural balance, and critical thinking, they can become powerful tools for protecting free will. These systems can help filter misinformation, verify facts, and provide individuals with reliable data upon which to base their decisions. -/- However, the potential misuse of AGI, such as the intentional spread of disinformation for political or economic gain, represents a significant risk. Ensuring that AGI adheres to principles grounded in the universal law of balance and the law of cause and effect is essential to maintaining the integrity of human decision-making. -/- Conclusion -/- The absolute law of free will is inseparable from the requirement for correct information. Any decision made under the influence of misinformation, disinformation, or ignorance represents a fundamental violation of this law. The consequences of such violations are evident in societal conflicts, poor health outcomes, environmental degradation, and governance failures. -/- The flaws Inherent in the laws surrounding freedom of speech and expression further complicate this issue. While these freedoms are essential for societal growth and the exchange of ideas, their misuse creates significant obstacles to the proper exercise of free will. To safeguard free will, society must invest in education, promote information literacy, implement responsible regulations for free speech, and ensure the ethical development of AGI systems. -/- By adhering to the principle that free will is 100 percent dependent on correct information, humanity can create a more stable, balanced, and harmonious society, where individuals and communities alike can make decisions that align with the natural laws governing human behavior and societal development. -/- . (shrink)

The Universal Law of Balance as the Key to Ethical AI and Internet Governance -/- The rapid advancement of internet technology and artificial intelligence (AI) has brought unprecedented opportunities for innovation, communication, and economic growth. However, the dominant profit-driven model of capitalism has also led to severe imbalances, particularly in the spread of black propaganda and misinformation. These issues arise because many websites, apps, and AI systems prioritize engagement and revenue over truth and societal well-being. If left unchecked, these imbalances (...) can distort human decision-making, manipulate public opinion, and destabilize societies. To correct this, the principles of the universal law of balance must be applied to both internet governance and AI development. A transition to a socialist or mixed economic system may be necessary to ensure that technological progress aligns with ethical and natural laws. -/- The Problem: Profit Motive and the Spread of Misinformation -/- In today’s digital economy, many technology companies generate revenue through engagement-based advertising. This creates a powerful incentive for social media platforms, news aggregators, and AI-driven content generators to promote sensational, misleading, or emotionally charged content—often at the expense of truth. Black propaganda, misinformation, and divisive narratives thrive in such an environment because they capture attention and increase user interaction. As a result, companies continue to profit while the public becomes more misinformed, leading to societal instability. -/- From the perspective of the universal law of balance, this system is defective. Just as a machine with faulty components will eventually break down, a society built on distorted information cannot function properly. For stability to be restored, the design of internet algorithms, content moderation policies, and economic incentives must be realigned with natural balance principles. -/- The Solution: A Balanced Economic Model for AI and Internet Governance -/- To address these systemic flaws, a shift away from pure capitalism toward a socialist or mixed economic system is necessary. This approach would introduce ethical guidelines, regulatory oversight, and alternative business models that prioritize balance over profit maximization. Some key solutions include: -/- 1. Regulated AI and Algorithms – Governments and international organizations should enforce ethical AI policies that prioritize factual, balanced content over engagement-driven misinformation. AI systems must be designed to detect and filter black propaganda while maintaining free speech. -/- 2. Publicly Funded Information Platforms – Just as public broadcasting services like BBC and PBS exist to provide reliable news, governments or independent organizations should develop non-profit digital platforms where truth and ethical journalism are prioritized. -/- 3. Accountability Measures for Tech Companies – Heavier fines, stricter regulations, and legal consequences for companies that knowingly spread misinformation could discourage profit-driven manipulation. -/- 4. Alternative Revenue Models – Encouraging subscription-based or non-profit AI platforms could reduce reliance on engagement-driven advertising, minimizing the incentive for misinformation. -/- 5. International Collaboration – The UN, World Economic Forum, or a new global body should establish a universal framework for AI ethics, ensuring all nations regulate technology in a way that aligns with the universal law of balance. -/- By restructuring the digital economy with these principles, internet governance can function more like a self-correcting natural system, reducing errors and restoring social stability. -/- AGI and the Universal Law of Balance -/- Beyond internet governance, the universal law of balance is also crucial for AGI development. If AGI is designed purely for corporate profit or national interests, it will likely be used for economic exploitation, political control, or even large-scale misinformation campaigns. However, if AGI is built on the principles of balance, it can serve as a force for truth and ethical decision-making. -/- An AGI aligned with the universal formula would: -/- Validate Information Using Natural Laws – Assess the accuracy of information based on objective, universal principles, rejecting misinformation. -/- Prevent Exploitation – Detect and counter propaganda, ensuring AI is not misused by corporations or governments. -/- Optimize Human Progress – Enhance decision-making for individuals and leaders, fostering rational and ethical societal development. -/- Self-Correct and Adapt – Continuously refine its understanding, eliminating systemic biases over time. -/- For AGI to truly function as a rational, human-level intelligence, it must be programmed with the universal law of balance. This will ensure that AI operates in harmony with nature and truth, rather than as a tool for profit-driven deception. -/- The Need for Global Political Will -/- The transition to a balanced internet and AI system will not happen voluntarily. Many governments and corporations benefit from misinformation and may resist ethical changes that limit their influence. Therefore, global political pressure is necessary to enforce these reforms. The UN, national governments, and independent oversight bodies must develop the political will to regulate AI and internet technology according to natural laws. Without this effort, profit-driven systems will continue to create instability, misinformation, and societal decay. -/- To prevent this, AI researchers, policymakers, and global institutions must adopt the universal formula as the guiding principle for future technological developments. Only by restoring balance—both in information systems and economic models—can AI and digital technology serve humanity rather than manipulate it. -/- Conclusion -/- The current digital economy, driven by profit at all costs, has created a system where misinformation spreads unchecked, causing severe societal imbalances. The universal law of balance offers a solution by ensuring that AI and internet technology function without defects or errors, just like any other natural system. To achieve this, a transition to a socialist or mixed economic model is necessary, along with strict regulations and ethical oversight. -/- Furthermore, AGI must be programmed with the universal formula to prevent it from becoming another tool for manipulation. If governments, the UN, and AI researchers embrace this framework, technology can be realigned with truth, ethical governance, and natural balance—leading to a more stable and just society. -/- . (shrink)

Counteracting Evil Influence in the Mind and Society Through Balance -/- Evil influence exists both in the human mind and in society, shaping destructive behaviors, corrupting leadership, and creating suffering. Understanding its roots and impact is essential to restoring balance, which aligns with the universal law of balance—the principle that all decisions must maintain harmony within nature and human systems. This essay explores the presence of evil influence in the mind and society and provides solutions to counteract its effects through (...) education, ethical leadership, and systemic reforms. -/- The Existence of Evil Influence in the Mind -/- The human mind is highly susceptible to external influences, biases, and distortions. Several factors contribute to the emergence of evil or destructive tendencies in individuals: -/- 1. Psychological and Cognitive Distortions -/- Cognitive biases, such as confirmation bias and groupthink, cause individuals to accept false beliefs and justify harmful actions. Additionally, unresolved trauma and mental disorders can influence destructive thought patterns, leading to behaviors that disrupt personal and social harmony. -/- 2. Manipulation and External Conditioning -/- Society constantly bombards individuals with propaganda, misinformation, and ideological indoctrination. Media, political narratives, and social institutions can shape people’s perceptions in ways that distort reality and encourage harmful actions. Without awareness, individuals become pawns in larger manipulative systems that perpetuate imbalance and suffering. -/- 3. Moral and Ethical Corruption -/- Lack of self-reflection, weak moral foundations, and societal pressures can lead people to justify unethical behavior. Over time, greed, power-seeking, and self-interest override empathy, reinforcing evil influences within the mind. -/- To combat these influences, individuals must develop critical thinking, emotional resilience, and ethical reasoning to resist manipulation and maintain balance in their decision-making. -/- The existence of Evil Influence in Society -/- At the societal level, evil influence manifests through corruption, oppression, and systemic injustice. Several key factors contribute to its existence: -/- 1. Cultural and Historical Prejudices -/- Societal beliefs often stem from historical injustices that persist through generations. Racism, sexism, and class discrimination are examples of deeply ingrained biases that create division and suffering. Without intervention, these biases continue to shape laws, social structures, and economic opportunities. -/- 2. Political and Economic Corruption -/- Governments and corporations often prioritize power and profit over ethical responsibility. Corrupt leaders manipulate laws, suppress dissent, and exploit resources without regard for the well-being of the people. The imbalance caused by extreme wealth concentration leads to poverty, crime, and social instability. -/- 3. Religious and Ideological Extremism -/- Dogmatic religious and ideological beliefs can be used to justify violence, oppression, and discrimination. When people blindly follow unproven doctrines without critical examination, society becomes vulnerable to extremism, which disrupts balance and fuels conflicts. -/- 4. Misinformation and Media Manipulation -/- The rise of social media and biased news sources has made misinformation more powerful than ever. Fake news, propaganda, and algorithm-driven echo chambers influence public opinion, leading to irrational decision-making and social division. Without proper media literacy, individuals fall victim to manipulation, perpetuating societal instability. -/- Given these widespread influences, restoring balance in society requires systemic changes, ethical leadership, and an informed citizenry. -/- Solutions to Counteract Evil Influence -/- To neutralize the effects of evil influence in the mind and society, a multi-level approach is necessary. The key solutions align with the universal law of balance and focus on education, governance, and social reform. -/- 1. Strengthening the Mind Against Evil Influence -/- A well-informed and emotionally resilient mind is less susceptible to manipulation. The following steps can help individuals develop resistance to evil influences: -/- Holistic Education – Schools should teach philosophy, ethics, and critical thinking from an early age to cultivate independent reasoning. -/- Cognitive Bias Awareness – People must learn to recognize and counteract biases that distort their perception of reality. -/- Mindfulness and Emotional Intelligence – Practices such as meditation, self-reflection, and emotional regulation strengthen an individual’s ability to make balanced decisions. -/- Media Literacy – Understanding how misinformation spreads prevents individuals from becoming victims of propaganda. -/- 2. Reforming Society to Minimize Evil Influence -/- Since societal structures often reinforce harmful behaviors, systemic changes are necessary to create a just and balanced environment. -/- A. Ethical Leadership and Governance -/- Governments must implement policies based on the universal law of balance, ensuring that decisions benefit all rather than a select few. Specific measures include: -/- Strict anti-corruption laws and transparent governance. -/- Democratic participation to prevent power from being concentrated in the hands of a few. -/- Policies that prioritize long-term sustainability over short-term profits. -/- B. Reforming Religious and Ideological Systems -/- Encouraging interfaith and intercultural understanding to reduce religious and ideological conflicts. -/- Promoting critical examination of beliefs, allowing individuals to refine their faith based on reason rather than blind adherence. -/- Separating religious dogma from governance to prevent laws based on unverified beliefs from creating injustice. -/- C. Ensuring Economic and Social Justice -/- Extreme economic inequality creates conditions for crime, exploitation, and suffering. Solutions include: -/- Fair wealth distribution through progressive taxation and universal basic services. -/- Ethical labor practices to prevent exploitation in the workforce. -/- Social welfare programs to ensure access to education, healthcare, and basic needs. -/- The Role of AGI in Preventing Evil Influence -/- Since AGI (Artificial General Intelligence) can be programmed with the universal formula, it has the potential to detect, prevent, and counteract evil influences on a large scale. AGI can contribute by: -/- Identifying Corruption and Misinformation – AI-powered monitoring systems can detect and expose political and corporate manipulation. -/- Optimizing Governance – AI-driven policy recommendations can ensure laws align with ethical and sustainable balance. -/- Enhancing Global Education – AI-powered educational tools can teach critical thinking, ethics, and rational decision-making to individuals worldwide. -/- By integrating AGI into governance, education, and decision-making processes, humanity can minimize the spread of misinformation, corruption, and imbalance. -/- Conclusion: Achieving Balance is the Key -/- Evil influence in the mind and society is ultimately a reflection of imbalance—whether in thoughts, beliefs, political systems, or economic structures. By applying education, ethical governance, media literacy, and AGI-based solutions, individuals and societies can restore balance and minimize human suffering. -/- The key lies In awareness, critical thinking, and responsible decision-making. When people recognize the importance of the universal law of balance, they can resist destructive influences and contribute to a more harmonious and just world. -/- . (shrink)

Scientific Explanation of Political Polarization and Mind Control Bias Techniques -/- Political polarization and mind control bias techniques are interconnected phenomena that shape public opinion and influence societal divisions. These processes can be explained scientifically through psychology, neuroscience, and mass communication studies. This essay will explore both concepts in depth, including their cognitive, social, and technological underpinnings. -/- I. Political Polarization: The Science Behind Division -/- Definition of Political Polarization -/- Political polarization is the process by which political attitudes and (...) opinions become increasingly divided, leading to two or more opposing camps with little room for compromise. It results in an “us vs. them” mentality, where political groups become hostile to each other. -/- A. Psychological and Cognitive Biases Driving Polarization -/- 1. Confirmation Bias -/- People tend to seek and believe information that aligns with their pre-existing views while rejecting contradictory evidence. -/- Example: A conservative individual may only watch right-wing news, reinforcing their perspective, while a liberal person may do the same with left-wing news. -/- 2. Motivated Reasoning -/- Individuals process political information in a way that aligns with their desired conclusions, ignoring facts that contradict their beliefs. -/- Example: A person might dismiss credible reports about a political leader’s corruption because they support that leader. -/- 3. Groupthink and Social Identity Theory -/- Humans naturally categorize themselves into groups, forming a strong loyalty to their own group while viewing outsiders with suspicion. -/- Example: Political parties, religious organizations, and ideological movements foster an “us vs. them” mentality, where criticism of the group is seen as betrayal. -/- 4. Cognitive Dissonance -/- When people encounter information that contradicts their beliefs, they experience discomfort and resolve this by rejecting or rationalizing the new information. -/- Example: A supporter of a leader accused of misconduct may justify the actions as necessary rather than accepting the wrongdoing. -/- B. Social and Technological Factors Reinforcing Polarization -/- 1. Echo Chambers and Filter Bubbles -/- Social media platforms use algorithms to show users content that aligns with their views while filtering out opposing perspectives. -/- Result: People only see information that reinforces their existing beliefs, further deepening polarization. -/- 2. Misinformation and Political Propaganda -/- Governments, media outlets, and online influencers manipulate public opinion by spreading biased or false information. -/- Example: Fake news campaigns during elections that distort facts to manipulate voters. -/- 3. Fear and Emotional Manipulation -/- Political leaders and media often use fear-based narratives to shape public opinion. -/- Example: “If the opposition wins, the country will collapse,” creating panic and emotional bias rather than rational decision-making. -/- 4. Media Framing and Selective Reporting -/- News outlets emphasize certain stories and ignore others to shape public perception. -/- Example: One news channel focuses only on economic success under a leader, while another highlights only corruption scandals. -/- 5. Political and Economic Interests -/- Corporations, interest groups, and governments use media to maintain control over public opinion and policy. -/- Example: Politicians funding biased media outlets to spread their narratives. -/- II. Mind Control Bias Techniques: The Science of Influence -/- Mind control techniques (also known as psychological manipulation) exploit cognitive biases and behavioral psychology to shape public opinion and influence individual thinking. These techniques are widely used in propaganda, advertising, and political campaigns. -/- A. Persuasion and Influence Tactics -/- 1. Repetition Effect (Illusory Truth Effect) -/- The more a message is repeated, the more likely people are to believe it, even if it is false. -/- Example: A false claim about a politician being corrupt is repeated so often that people accept it as fact. -/- 2. Framing Effect -/- The way information is presented influences perception. -/- Example: “Tax relief for hardworking citizens” sounds positive, while “tax cuts for the wealthy” sounds negative—yet they refer to the same policy. -/- 3. Priming -/- Exposure to specific stimuli influences future decisions. -/- Example: Repeated association of an ethnic group with crime leads people to unconsciously stereotype them. -/- 4. Bandwagon Effect -/- People are more likely to believe something if they see many others believing it. -/- Example: A political leader’s approval ratings appear high in polls, influencing undecided voters to support them. -/- B. Psychological Control Techniques -/- 1. Fear and Scapegoating -/- Creating fear and blaming an external group as the cause of problems. -/- Example: “Immigrants are taking jobs,” creating hostility toward a group to manipulate public sentiment. -/- 2. Dissonance Reduction -/- People modify their beliefs when they experience psychological discomfort from contradictory information. -/- Example: A person who supports strict law enforcement but dislikes police brutality may justify excessive force as “necessary for safety.” -/- 3. Authority Bias -/- People tend to obey authority figures and accept their statements without questioning. -/- Example: A celebrity or leader endorses a political claim, leading followers to accept it as truth. -/- C. Mass Media and Propaganda Methods -/- 1. Controlled Narratives -/- Governments and corporations control what information is released to shape public perception. -/- Example: State-controlled media omitting news about protests or scandals. -/- 2. Pacing and Leading -/- Gradual introduction of ideas to make extreme positions seem normal over time. -/- Example: Media slowly shifting narratives on controversial issues to make them acceptable. -/- 3. Subliminal Messaging -/- Unconscious cues in advertisements, political speeches, and media that influence thoughts without awareness. -/- Example: Hidden symbols or phrases in campaign ads that create positive associations with a candidate. -/- III. The Connection Between Political Polarization and Mind Control -/- Political polarization and mind control techniques are interrelated. As society becomes more polarized, individuals are more susceptible to manipulation. Here’s how they interact: -/- 1. Polarization Creates a Fertile Ground for Mind Control -/- The more divided a society is, the easier it is for political actors to use manipulation techniques. -/- Example: In a polarized election, misinformation spreads more easily because people are already inclined to believe negative stories about the opposition. -/- 2. Mind Control Techniques Deepen Polarization -/- Media framing, misinformation, and psychological manipulation create a feedback loop where divisions grow stronger. -/- Example: Social media algorithms push divisive content, reinforcing people’s biases and making compromise impossible. -/- 3. Fear and Emotional Manipulation Maintain Division -/- Political leaders exploit fear to keep their supporters loyal. -/- Example: “If the other party wins, your freedoms will be taken away,” preventing rational debate. -/- 4. Echo Chambers and Filter Bubbles Block Critical Thinking -/- People exposed only to biased information become increasingly radicalized. -/- Example: A person who only follows one-sided news sources will believe their political opponents are evil rather than fellow citizens with different opinions. -/- Conclusion: How to Resist Polarization and Mind Control -/- Understanding the scientific principles behind political polarization and mind control techniques can help individuals resist manipulation. Some strategies include: -/- Critical Thinking – Question sources of information and seek diverse viewpoints. -/- Media Literacy – Recognize propaganda techniques in news and social media. -/- Emotional Awareness – Avoid reacting emotionally to political content and analyze information logically. -/- Avoiding Echo Chambers – Engage with different perspectives and avoid isolating oneself in ideological bubbles. -/- By applying these principles, societies can move toward a more rational, less divided political landscape, where decisions are based on reason rather than manipulation. -/- . (shrink)

-/- Restructuring Profit-Oriented Social Media to Align with the Three Universal Laws of Nature -/- By Angelito Malicse -/- In today’s world, social media platforms have become powerful tools for shaping thought, behavior, and society itself. However, the dominance of profit-oriented models in these platforms has led to widespread harm—ranging from misinformation and emotional manipulation to mental health crises and societal division. When assessed through the lens of my Three Universal Laws of Nature—the Law of Karma, the Law of Feedback, (...) and the Law of Balance—it becomes clear that the current structure of these platforms is deeply flawed. If we are to use technology for the benefit of all, these platforms must be restructured in accordance with natural law. -/- I. Violation and Restoration of the Law of Karma: System Integrity and Cause-Effect -/- Violation: Profit-driven social media platforms prioritize engagement metrics—clicks, views, shares—regardless of content quality or truth. Sensationalism, fake news, and emotionally charged posts dominate feeds, encouraging addictive behavior and eroding trust. Moreover, these systems exploit user data without transparency, creating cause-effect chains that result in individual suffering and social disorder. -/- Restoration Path: -/- 1. Algorithmic Integrity: Redesign algorithms to promote verified, constructive, and educational content over sensational or divisive material. -/- 2. Accountability Mechanisms: Implement fact-checking, content quality metrics, and platform accountability for misinformation. -/- 3. Transparent Data Ethics: Respect user privacy by clearly explaining how data is collected and used, allowing users to opt out of unnecessary surveillance. -/- This ensures the cause (algorithm and system design) leads to positive and intended effects (user well-being, social harmony), upholding the law of karma in its systemic form. -/- II. Violation and Restoration of the Law of Feedback: Mutual Influence and Learning -/- Violation: Instead of fostering meaningful dialogue, social media creates echo chambers. Feedback systems (likes, shares, comments) are manipulated to maximize emotional extremes—anger, fear, outrage—rather than thoughtful exchange. Users are rewarded for triggering reactions, not for encouraging reflection or empathy. -/- Restoration Path: -/- 1. Reform Feedback Loops: Replace superficial metrics with systems that reward thoughtful discourse, critical thinking, and empathy. -/- 2. Encourage Diversity of Thought: Algorithms must expose users to differing viewpoints to foster intellectual humility and societal tolerance. -/- 3. Balance Emotional Engagement: Prioritize content that promotes psychological well-being and reduces toxic interactions. -/- By enabling healthy, reciprocal influence between users and content, platforms become tools for collective wisdom, not division. -/- III. Violation and Restoration of the Law of Balance: Homeostasis in Individual and Collective Experience -/- Violation: Excessive screen time, comparison culture, and the constant pursuit of attention disturb the mental, emotional, and even physiological balance of users. Social media has led to increased anxiety, depression, addiction, and cognitive fatigue. At a societal level, it promotes imbalance by amplifying extremes, creating instability in public opinion and behavior. -/- Restoration Path: -/- 1. Digital Well-being Tools: Introduce features that limit addictive usage, promote breaks, and encourage offline reflection. -/- 2. Mental Health Support Integration: Incorporate wellness resources like guided meditation, calming prompts, and emotional check-ins after intense content exposure. -/- 3. Balanced Content Curation: Provide a mix of uplifting, informative, and reflective content to restore inner equilibrium. -/- Platforms should evolve from being sources of imbalance to becoming instruments of holistic well-being, mirroring nature’s homeostasis. -/- Actionable Implementation Strategies -/- To fully bring social media into harmony with the Three Universal Laws, the following actionable reforms are essential: -/- User Control Dashboards: Give users deeper control over what content they see based on psychological needs and ethical preferences. -/- Content Credibility Scores: Rate creators and posts based on truthfulness, depth, and positive contribution to societal discourse. -/- Incentives for Balance: Reward users and creators who engage in ethical, empathetic, and thoughtful behavior. -/- Conclusion: Building Ethical Technology for a Balanced Future -/- Profit-oriented social media, in its current form, violates the natural laws that govern healthy systems. It distorts cause-effect relationships, undermines constructive feedback, and throws individuals and societies into imbalance. However, if restructured based on the Three Universal Laws of Nature, these platforms can evolve into transformative forces for truth, compassion, and equilibrium. -/- Only by restoring harmony between technology and natural law can we create a future where human flourishing, not profit, is the measure of success. -/- . (shrink)

Cyberbullying, a contemporary menace within the realm of social media, has emerged alongside the surge in social media usage, leading to the exploitation of freedom of expression. Statistics indicate that a substantial 36.5% of individuals believe they have experienced cyberbullying at some point in their lives. This figure has more than doubled since 2007, and there has been a noticeable increase from 2018 to 2023, indicating an unfavorable trend. Although existing solutions in the market aim to mitigate this issue, (...) they often come with usage limitations or inefficient algorithms. (shrink)

-/- Assessing TikTok through the Lens of the Universal Formula on the Problem of Free Will -/- In the modern digital age, social media platforms play a central role in shaping the minds and behaviors of individuals and societies. Among these platforms, TikTok stands out due to its immense popularity and powerful influence across all age groups. However, in evaluating its impact on human free will and societal well-being, it is essential to use a precise and universal standard. This essay (...) utilizes my Universal Formula—comprised of three natural laws—to assess TikTok’s function and consequences. To provide an objective and practical evaluation, a numerical grading system is also applied. -/- My Universal Formula is based on the understanding that human decision-making, whether individual or collective, follows fixed natural laws. These laws are: -/- 1. The Law of Karma (Cause and Effect in Systems) – Any system, to function properly, must be free of defects or errors. Whether organic like the human body or artificial like a digital platform, its effectiveness depends on system integrity. -/- 2. The Law of Balance in Nature – All thoughts, decisions, and behaviors must maintain internal and external equilibrium. Any imbalance—emotional, social, environmental, or moral—leads to disorder. -/- 3. The Law of Feedback Mechanism – Human minds operate within feedback loops. Our actions influence and are influenced by others and our environment. Free will is exercised within this dynamic exchange. -/- To assess TikTok fairly, I assign a numerical value to its performance under each law, using the following scale: -/- 0–2 = Severely Violates -/- 3–4 = Partially Violates -/- 5 = Neutral -/- 6–7 = Partially Aligns -/- 8–10 = Fully Aligns -/- 1. The Law of Karma (Systems Integrity) TikTok functions efficiently as a technological system. Its algorithm is advanced, and its user interface is smooth. However, behind this efficiency lies a significant defect: the algorithm manipulates content visibility based on engagement rather than truth, meaning, or user well-being. It prioritizes viral content that may be misleading, superficial, or even harmful. Furthermore, it allows the spread of misinformation and is designed to foster addictive use. As a result, TikTok fails to operate as a healthy, error-free system in the context of human and social development. Grade: 4/10 (Partially Violates) -/- 2. The Law of Balance in Nature One of the most serious concerns about TikTok is its impact on mental and emotional balance. It promotes short-form, dopamine-driven content that often leads to overstimulation and short attention spans. Many users spend hours scrolling without awareness, disrupting their internal balance. Externally, the platform contributes to trends that often favor consumerism, vanity, or extremism rather than critical thinking, harmony, or meaningful interaction. Although TikTok can host educational or creative content, these are often drowned out by viral trends driven by quantity of engagement rather than quality of content. Grade: 3/10 (Partially Violates) -/- 3. The Law of Feedback Mechanism The feedback systems on TikTok—likes, shares, comments, and follower counts—are highly engaging. However, they are designed primarily to maximize user retention and advertising revenue rather than to cultivate truth or social awareness. The feedback users receive is often superficial and addictive. Moreover, TikTok’s algorithm decides what users see, reducing genuine interaction and fostering echo chambers. This distorts the natural feedback mechanism between conscious minds and the world around them, weakening critical thinking and self-regulation. Grade: 2/10 (Severely Violates) -/- Overall Evaluation and Conclusion TikTok receives an average score of 3.0/10 under the Universal Formula. This indicates that the platform mostly violates the three natural laws that guide healthy human and societal functioning. While TikTok succeeds as a digital tool in terms of performance and popularity, it fails to align with the natural laws that uphold true free will, internal balance, and healthy systems. Its algorithmic design, addictive nature, and manipulation of feedback loops contribute more to human imbalance than to harmony. -/- If platforms like TikTok are to contribute to human development and not destruction, their design must be reoriented to follow the three Universal Laws. By doing so, they can become powerful tools not for exploitation but for education, balance, and social evolution. Until then, critical awareness and holistic education will remain necessary to protect individuals—especially the youth—from the negative consequences of platforms that violate the very laws of nature that sustain life and liberty. -/- . (shrink)

-/- Assessing Facebook through the Lens of the Universal Formula on the Problem of Free Will -/- Facebook, once the world’s most prominent digital town square, continues to play a major role in how people communicate, consume information, and form identities. As society grapples with rising mental health issues, polarization, and disinformation, the question arises: does Facebook help uphold or violate the natural laws that support human free will, balance, and systemic harmony? This essay assesses Facebook through my Universal Formula, (...) which is grounded in the Three Universal Laws of Nature, using a practical numerical grading system. -/- The Universal Formula reflects the truth that all human decision-making operates under three interdependent natural laws: -/- 1. The Law of Karma (Cause and Effect in Systems) – A system must be free of inherent defects to function beneficially. -/- 2. The Law of Balance in Nature – Human thoughts and behaviors must remain in harmony with both internal and external realities. -/- 3. The Law of Feedback Mechanism – Human minds influence and are influenced through continuous feedback from other minds and the environment. -/- The numerical grading system: -/- 0–2 = Severely Violates -/- 3–4 = Partially Violates -/- 5 = Neutral -/- 6–7 = Partially Aligns -/- 8–10 = Fully Aligns -/- 1. The Law of Karma (Systems Integrity) Facebook is structurally well-designed as a digital communication platform. However, its system has been exploited to spread misinformation, manipulate users through data profiling, and promote addictive behavior. The business model relies heavily on engagement metrics, not truth or well-being, which creates a systemic defect. Cambridge Analytica and similar scandals reveal how Facebook can become a tool for psychological manipulation and mass disinformation. Even with recent changes in oversight, the system remains flawed at its core, prioritizing revenue over ethical alignment. Grade: 3/10 (Partially Violates) -/- 2. The Law of Balance in Nature Facebook contributes to a loss of personal and social balance. Users often present idealized versions of themselves, which fosters comparison, anxiety, and depression. The platform’s design encourages emotional validation through likes, comments, and shares, leading to dependency and distorted self-worth. Politically, Facebook has played a major role in polarizing societies by amplifying divisive content. While it also supports positive connections, these benefits are outweighed by the negative effects on psychological and societal balance. Grade: 3/10 (Partially Violates) -/- 3. The Law of Feedback Mechanism Facebook’s feedback system is highly artificial. Instead of honest human interaction, users often receive algorithmically filtered content designed to increase time spent on the platform. This interferes with authentic interpersonal feedback and reduces awareness of diverse perspectives. Echo chambers are formed, where people only hear what confirms their biases. The natural feedback loop that allows for mutual growth and understanding is distorted. Although some meaningful discussions occur, the overall design undermines genuine communication and learning. Grade: 3/10 (Partially Violates) -/- Overall Evaluation and Conclusion Facebook receives an overall average score of 3.0/10 based on the Universal Formula. Like other major platforms, Facebook operates in partial violation of all three laws of nature that support human free will and balance. While it allows for large-scale social connection, its system design promotes emotional imbalance, misinformation, and distorted feedback loops. -/- To evolve into a platform that aligns with the laws of nature, Facebook must undergo deep reform—placing the pursuit of truth, internal well-being, and balanced feedback at its core. Until such transformation occurs, it poses more risks than benefits in the development of individual freedom, mental harmony, and societal integrity. -/- The only sustainable solution is a foundational educational system that equips individuals with the critical thinking skills and moral awareness to protect themselves from platforms that unconsciously violate nature’s laws. With the Universal Formula as a guide, society can navigate the digital world without sacrificing the true meaning of free will and natural balance. -/- . (shrink)

Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities and (...) future research direction. For better analysis and observation, we split machine learning techniques into Bayesian, non-Bayesian, and deep learning. We reviewed the most recent advances in Bayesian and non-Bayesian-based classifiers before exploiting their cor- responding weaknesses to indicate future research direction. While exploiting weaknesses in both Bayesian and non-Bayesian classifiers, we also compared each performance with a deep learning classifier. For a proper review of deep learning-based classifiers, we looked at Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), and Long Short Term Memory Networks (LSTMs). We did an empirical analysis to evaluate the performance of each classifier along with many of the proposed state-of-the-art anti-phishing techniques to identify future research directions, we also made a series of proposals on how the performance of the under-performing algorithm can improved in addition to a two-stage prediction model. (shrink)

The main objective of this dissertation is to philosophically assess how the use of informational concepts in the field of classical thermostatistical physics has historically evolved from the late 1940s to the present day. I will first analyze in depth the main notions that form the conceptual basis on which 'informational physics' historically unfolded, encompassing (i) different entropy, probability and information notions, (ii) their multiple interpretative variations, and (iii) the formal, numerical and semantic-interpretative relationships among them. In the following, I (...) will assess the history of informational thermophysics during the second half of the twentieth century. Firstly, I analyse the intellectual factors that gave rise to this current in the late forties (i.e., popularization of Shannon's theory, interest in a naturalized epistemology of science, etc.), then study its consolidation in the Brillouinian and Jaynesian programs, and finally claim how Carnap (1977) and his disciples tried to criticize this tendency within the scientific community. Then, I evaluate how informational physics became a predominant intellectual current in the scientific community in the nineties, made possible by the convergence of Jaynesianism and Brillouinism in proposals such as that of Tribus and McIrvine (1971) or Bekenstein (1973) and the application of algorithmic information theory into the thermophysical domain. As a sign of its radicality at this historical stage, I explore the main proposals to include information as part of our physical reality, such as Wheeler’s (1990), Stonier’s (1990) or Landauer’s (1991), detailing the main philosophical arguments (e.g., Timpson, 2013; Lombardi et al. 2016a) against those inflationary attitudes towards information. Following this historical assessment, I systematically analyze whether the descriptive exploitation of informational concepts has historically contributed to providing us with knowledge of thermophysical reality via (i) explaining thermal processes such as equilibrium approximation, (ii) advantageously predicting thermal phenomena, or (iii) enabling understanding of thermal property such as thermodynamic entropy. I argue that these epistemic shortcomings would make it impossible to draw ontological conclusions in a justified way about the physical nature of information. In conclusion, I will argue that the historical exploitation of informational concepts has not contributed significantly to the epistemic progress of thermophysics. This would lead to characterize informational proposals as 'degenerate science' (à la Lakatos 1978a) regarding classical thermostatistical physics or as theoretically underdeveloped regarding the study of the cognitive dynamics of scientists in this physical domain. (shrink)

Epistemic exploitation occurs when privileged persons compel marginalized persons to educate them about the nature of their oppression. I argue that epistemic exploitation is marked by unrecognized, uncompensated, emotionally taxing, coerced epistemic labor. The coercive and exploitative aspects of the phenomenon are exemplified by the unpaid nature of the educational labor and its associated opportunity costs, the double bind that marginalized persons must navigate when faced with the demand to educate, and the need for additional labor created by (...) the default skepticism of the privileged. I explore the connections between epistemic exploitation and the two varieties of epistemic injustice that Fricker (2007) identifies, testimonial and hermeneutical injustice. I situate epistemic exploitation within Dotson’s (2012; 2014) framework of epistemic oppression, and I address the role that epistemic exploitation plays in maintaining active ignorance and upholding dominant epistemic frameworks. (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers have (...) taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...) affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

‘Epistemic exploitation occurs when privileged persons compel marginalised knowers to educate them [and others] about the nature of their oppression’ (Berenstain, 2016, p. 569). This paper scrutinizes some of the purported wrongs underpinning this practice, so that educators might be better equipped to understand and avoid or mitigate harms which may result from such interventions. First, building on the work of Berenstain and Davis (2016), we argue that when privileged persons (in this context, educators) repeatedly compel marginalised or oppressed (...) knowers, to not only to educate them, but indeed others, about the nature of their oppression, they risk subjecting them to further epistemic-moral harms. This is due to the likelihood that at least some of their audience will assign them less/more credibility than they deserve based on pre-existing identity-based prejudices. Second, though some of these requests to ‘educate’ or ‘learn more’ masquerade as seemingly virtuous or innocuous epistemic inquiries, privileged persons underestimate or remain ignorant of secondary harms which stem from internalized epistemic obligations, oppressive double-binds (Hirji, 2021), and attendant emotional burdens oppressed knowers carry in relation to the ever-present possibility of ameliorating oppressor mindsets. After surveying each context-specific harm briefly, we then turn to an applied reading of how these exploitative practices sometimes culminate in something we refer to as ‘ontic burnout’, a form of interminable explanatory fatigue brought on by repeated requests to educate the privileged about what it means to be oppressed. (shrink)

As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered by conflations (...) of various understandings of bias, ranging from neutral deviations from a standard to morally problematic instances of injustice due to prejudice, discrimination, and disparate treatment. This terminological confusion impedes efforts to address clear cases of discrimination. -/- In this paper, we examine the promises and challenges of different approaches to disambiguating bias and designing for justice. While both approaches aid in understanding and addressing clear algorithmic harms, we argue that they also risk being leveraged in ways that ultimately deflect accountability from those building and deploying these systems. Applying this analysis to recent examples of generative AI, our argument highlights unseen dangers in current methods of evaluating algorithmic bias and points to ways to redirect approaches to addressing bias in generative AI at its early stages in ways that can more robustly meet the demands of justice. (shrink)

In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After first (...) clarifying the concept of temporal governmentality, I apply this lens to Chicago Police Department’s Strategic Subject List. This predictive algorithm operates, I argue, through a paranoid logic that aims to preempt future possibilities of crime on the basis of a criminal past codified in historical crime data. (shrink)

It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only demonstrate how (...) the philosophical conceptual framework of epistemic injustice can help pinpoint potential, systematic harms from algorithmic profiling, but we also identify a novel source of hermeneutical injustice that to date has received little attention in the relevant literature: epistemic fragmentation. Epistemic fragmentation is a structural characteristic of algorithmically-mediated environments that isolate individuals, making it more difficult to develop, uptake and apply new epistemic resources. This, in turn, can impede the identification and conceptualisation of emerging harms in these environments. We trace the occurrence of hermeneutical injustice back to the fragmentation of the epistemic experiences of individuals, who are left more vulnerable by the inability to share, compare, and learn from shared experiences. (shrink)

Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key ways in (...) which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency. (shrink)

Algorithms wield increasing control over our lives—over the jobs we get, the loans we're granted, the information we see online. Algorithms can and often do wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has been largely neglected. I investigate algorithmic neutrality, tackling three questions: What is algorithmic neutrality? Is it possible? And when we have it in mind, what can we learn about algorithmic bias?

Statistical fairness criteria are widely used for diagnosing and ameliorating algorithmic bias. However, these fairness criteria are controversial as their use raises several difficult questions. I argue that the major problems for statistical algorithmic fairness criteria stem from an incorrect understanding of their nature. These criteria are primarily used for two purposes: first, evaluating AI systems for bias, and second constraining machine learning optimization problems in order to ameliorate such bias. The first purpose typically involves treating each criterion as a (...) necessary condition for fairness. The second use involves treating criteria as sufficient conditions for fairness. Since the criteria are used for both roles, some researchers have treated them as both necessary and sufficient conditions, i.e., as definitions of algorithmic fairness. However, serious problems have been raised for the use of these fairness criteria. Under ordinary circumstances, it is impossible to satisfy multiple criteria at the same time. Moreover, there are counterexamples to both the sufficiency and necessity for fairness of each criterion. I argue that we should instead understand fairness criteria as merely providing evidence of fairness. In other words, satisfaction (or violation) of these criteria should be understood as potential evidence of fairness (or bias). Whether a criterion counts as evidence in a particular case will depend on stakeholders' background knowledge and the specific features of the system's task. This evidence account of fairness conditions provides guidance for recognizing both the appropriate uses and the limitations of fairness criteria. (shrink)

The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we will call (...) the “Simple Stakes Thesis”. The Simple Stakes Thesis, as it will turn out, is too simple. For even if the stakes associated with a specific one-off decision are low—and hence does not engender a right to an explanation—such decisions may nevertheless form part of a high stakes pattern or aggregate of decisions. In such cases, we argue, even a low stakes decision may engender a right to explanation. Not only does this show that the right to explanation is more demanding than so far recognized. It also shows that the stakes thesis is significantly harder to apply in practice. (shrink)

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. Using these (...) case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core. (shrink)