The EU AI Act is the first step toward a comprehensive legal framework for AI. It introduces provisions for AI systems based on their risk levels in relation to fundamental rights. Providers of AI systems must conduct Conformity Assessments before market placement. Recent amendments added Fundamental Rights Impact Assessments for high-risk AI system users, focusing on compliance with EU and national laws, fundamental rights, and potential impacts on EU values. The paper suggests that automating business process compliance can help standardize (...) these assessments and outlines some methodological guidelines. (shrink)

This paper reviews the different meanings of vulnerability in the AI Act (AIA). We show that the AIA follows a rather established tradition of looking at vulnerability as a trait or a state of certain individuals and groups. It also includes a promising account of vulnerability as a relation but does not clarify if and how AI changes this relation. We spot the missing piece of the AIA: the lack of recognition that vulnerability is an inherent feature of all human-AI (...) interactions, varying in degree based on design choices and modes of interaction. Finally, we show how such a meaning of vulnerability may be incorporated into the AIA by interpreting the concept of “specific social situation” in Article 5 (b). (shrink)

The EU Artificial Intelligence Act (AIA) defines four risk categories for AI systems: unacceptable, high, limited, and minimal. However, it lacks a clear methodology for the assessment of these risks in concrete situations. Risks are broadly categorized based on the application areas of AI systems and ambiguous risk factors. This paper suggests a methodology for assessing AI risk magnitudes, focusing on the construction of real-world risk scenarios. To this scope, we propose to integrate the AIA with a framework developed by (...) the Intergovernmental Panel on Climate Change (IPCC) reports and related literature. This approach enables a nuanced analysis of AI risk by exploring the interplay between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We further refine the proposed methodology by applying a proportionality test to balance the competing values involved in AI risk assessment. Finally, we present three uses of this approach under the AIA: to implement the Regulation, to assess the significance of risks, and to develop internal risk management systems for AI deployers. (shrink)

The EU proposal for the Artificial Intelligence Act (AIA) defines four risk categories: unacceptable, high, limited, and minimal. However, as these categories statically depend on broad fields of application of AI, the risk magnitude may be wrongly estimated, and the AIA may not be enforced effectively. This problem is particularly challenging when it comes to regulating general-purpose AI (GPAI), which has versatile and often unpredictable applications. Recent amendments to the compromise text, though introducing context-specific assessments, remain insufficient. To address this, (...) we propose applying the risk categories to specific AI scenarios, rather than solely to fields of application, using a risk assessment model that integrates the AIA with the risk approach arising from the Intergovernmental Panel on Climate Change (IPCC) and related literature. This model enables the estimation of the magnitude of AI risk by considering the interaction between (a) risk determinants, (b) individual drivers of determinants, and (c) multiple risk types. We use large language models (LLMs) as an example. (shrink)

Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU´s new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative model of governance, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfilment of the legislation. Taken together, the article explores how the (...) AIA may be implemented by national and EU institutional bodies, encompassing longstanding bodies, such as the European Commission, and those newly established under the AIA, such as the AI Office. It investigates their roles across supranational and national levels, emphasizing how EU regulations influence institutional structures and operations. These regulations may not only directly dictate the structural design of institutions but also indirectly request administrative capacities needed to enforce the AIA. (shrink)

This is a preprint version of the forthcoming publication in AI and Society Journal. DOI: 10.1007/s00146-025-02289-8.

Through the lens of feminist speech act theory, this paper argues that artificial intelligence romance systems objectify and subordinate nonvirtual women. AI romance systems treat their users as consumers, offering them relational invulnerability and control over their (usually feminized) digital romantic partner. This paper argues that, though the output of AI chatbots may not generally constitute speech, the framework offered by an AI romance system communicates an unjust perspective on intimate relationships. Through normalizing controlling one’s intimate partner, these systems operate (...) as speakers that rank women as non-player characters and license their oppression, which is unjust even in the absence of empirical harms. Understanding AI romance systems as speakers has implications for policymakers. First, this account helps close the so-called responsibility gap in the operation of an AI system. Second, it places these systems under the purview of institutions’ policies regarding hate speech, suggesting one avenue for arguing that these systems may violate extant policies. Finally, it provides support for the pursuit of empirical research into AI romance systems’ effects on users. (shrink)

L’articolo esamina la proposta europea di regolamento sull’intelligenza artificiale, AI Act (AIA). In particolare, esamina il modello di analisi e valutazione del rischio dei sistemi di IA. L’articolo identifica tre potenziali problemi di implementazione del regolamento: (1) la predeterminazione dei livelli di rischio, (2) la genericità del giudizio di significatività del rischio e (3) l’indeterminatezza della valutazione sull’impatto dei diritti fondamentali. Il saggio suggeriscealcune soluzioni per affrontare questi tre problemi.

what is retraction? The word "retraction" can have several meanings depending on the context. Here are two of the most common: 1. Taking back a statement or action: This is the most general meaning of retraction. It refers to the act of withdrawing or reversing something that you have previously said or done. For example, if you make a false accusation against someone, you might publicly retract it to set the record straight. Or, if you offer to sell something for (...) a certain price, you might retract the offer if you decide you no longer want to sell it. 2. Withdrawing a published article: In academic publishing, a retraction is a formal notice that a published article is no longer considered to be valid. This can happen for a variety of reasons, such as if the research in the article is found to be flawed, if the authors committed misconduct, or if the article contains plagiarism. When an article is retracted, a notice is usually published in the same journal where the original article appeared, explaining the reason for the retraction. (shrink)

The increasing demand for transparency in AI has recently come under scrutiny. The question is often posted in terms of “epistemic double standards”, and whether the standards for transparency in AI ought to be higher than, or equivalent to, our standards for ordinary human reasoners. I agree that the push for increased transparency in AI deserves closer examination, and that comparing these standards to our standards of transparency for other opaque systems is an appropriate starting point. I suggest that a (...) more fruitful exploration of this question will involve a different comparison class. We routinely treat judgments made by highly trained experts in specialized fields as fair or well-grounded even though—by the nature of expert/layperson division of epistemic labor—an expert will not be able to provide an explanation of the reasoning behind these judgments that makes sense to most other people. Regardless, laypeople are thought to be acting reasonably—and ethically—in deferring to the judgments of experts that concern their areas of specialization. I suggest that we reframe our question regarding the appropriate standards of transparency in AI as one that asks when, why, and to what degree it would be ethical to accept opacity in AI. I argue that our epistemic relation to certain opaque AI technology may be relevantly similar to the layperson’s epistemic relation to the expert in certain respects, such that the successful expert/layperson division of epistemic labor can serve as a blueprint for the ethical use of opaque AI. (shrink)

Concerns about threats to human autonomy feature prominently in the field of AI ethics. One aspect of this concern relates to the use of AI systems for problematically manipulative influence. In response to this, the European Union’s draft AI Act (AIA) includes a prohibition on AI systems deploying subliminal techniques that alter people’s behavior in ways that are reasonably likely to cause harm (Article 5(1)(a)). Critics have argued that the term ‘subliminal techniques’ is too narrow to capture the target cases (...) of AI-based manipulation. We propose a definition of ‘subliminal techniques’ that (a) is grounded on a plausible interpretation of the legal text; (b) addresses all or most of the underlying ethical concerns motivating the prohibition; (c) is defensible from a scientific and philosophical perspective; and (d) does not over-reach in ways that impose excessive administrative and regulatory burdens. The definition is meant to provide guidance for design teams seeking to pursue responsible and ethically aligned AI innovation. (shrink)

The Netherlands was the first country to legalize euthanasia and physician-assisted suicide. This paper offers a first legal perspective on the prospects of using AI in the Dutch practice of euthanasia and physician-assisted suicide. It responds to the Regional Euthanasia Review Committees’ interest in exploring technological solutions to improve current procedures. The specific characteristics of AI – the capability to process enormous amounts of data in a short amount of time and generate new insights in individual cases – may for (...) example alleviate the increased workload of review committees due to the continuous increase of euthanasia cases. The paper considers three broad categories for the use of AI in the Dutch euthanasia practice: (1) the physician’s assessment of euthanasia requests, (2) the actual execution of euthanasia, and (3) the retrospective reviews of cases by the Regional Euthanasia Review Committees. Exploring the legal considerations around each avenue, both in the EU AI Act and the Dutch legal framework, this paper aims to facilitate the societal discussion on the role of technology in such deeply human decisions. This debate is equally relevant to other countries that legalized euthanasia (e.g. Belgium and Canada) or physician-assisted suicide (e.g. Switzerland and numerous states in the US). (shrink)

Many artificial intelligence (AI) systems currently used for decision-making are opaque, i.e., the internal factors that determine their decisions are not fully known to people due to the systems’ computational complexity. In response to this problem, several researchers have argued that human decision-making is equally opaque and since simplifying, reason-giving explanations (rather than exhaustive causal accounts) of a decision are typically viewed as sufficient in the human case, the same should hold for algorithmic decision-making. Here, I contend that this argument (...) overlooks that human decision-making is sometimes significantly more transparent and trustworthy than algorithmic decision-making. This is because when people explain their decisions by giving reasons for them, this frequently prompts those giving the reasons to govern or regulate themselves so as to think and act in ways that confirm their reason reports. AI explanation systems lack this self-regulative feature. Overlooking it when comparing algorithmic and human decision-making can result in underestimations of the transparency of human decision-making and in the development of explainable AI that may mislead people by activating generally warranted beliefs about the regulative dimension of reason-giving. (shrink)

Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework—a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry for AI (...) agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the framework incorporates decentralized justice systems for transparent dispute resolution and introduces AI-specific legal entities to manage limited liability, supported by mandatory insurance to ensure financial accountability and incentivize ethical design. By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance. This innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing innovation with ethical responsibility to meet the demands of an AI-driven future. (shrink)

There is a non-trivial chance that sometime in the (perhaps somewhat distant) future, someone will build an artificial general intelligence that will surpass human-level cognitive proficiency and go on to become "superintelligent", vastly outperforming humans. The advent of superintelligent AI has great potential, for good or ill. It is therefore imperative that we find a way to ensure-long before one arrives-that any superintelligence we build will consistently act in ways congenial to our interests. This is a very difficult challenge in (...) part because most of the final goals we could give an AI admit of so-called "perverse instantiations". I propose a novel solution to this puzzle: instruct the AI to love humanity. The proposal is compared with Yudkowsky's Coherent Extrapolated Volition, and Bostrom's Moral Modeling proposals. (shrink)

The complexity and emergent autonomy of Generative AI systems introduce challenges in predictability and legal compliance. This paper analyses some of the legal and regulatory implications of such challenges in the European Union context, focusing on four areas: liability, privacy, intellectual property, and cybersecurity. It examines the adequacy of the existing and proposed EU legislation, including the Artificial Intelligence Act (AIA), in addressing the challenges posed by Generative AI in general and LLMs in particular. The paper identifies potential gaps and (...) shortcomings in the EU legislative framework and proposes recommendations to ensure the safe and compliant deployment of generative models. (shrink)

Using speech act theory from the Philosophy of Language, this paper attempts to develop an ethical framework for the phenomenon of speech processing. We use the concepts of the illocutionary force and the illocutionary content of a speech act to explain the ethics of speech processing. By emphasizing the different stages involved in speech processing, we explore the distinct ethical issues that arise in relation to each stage. Input, processing, and output are the different ethically relevant stages under which a (...) spoken item or a speech navigates within the range of speech-processing modules. Employing the illocutionary force-content distinction, we specify and characterize the input-related ethical issues, the output-related ethical issues, and the processing-related ethical issues involved in speech processing. Together with illocutionary force-content distinction, we employ the data-information distinction to characterize the stage-wise ethical issues in the phenomenon of speech processing as the ethics of collecting (speech) data, the ethics of contextualizing (speech) data/information, and the ethics of releasing the con-textualized information (processed speech). Immediate ethical issues that arise from the range of speech processing modules are distinguished from distant ethical issues. We also indicate the nature of ethical issues that arise from Speaker Independent speech technologies. (shrink)

This article poses a simple question: can AI lie? In response to this question, the article examines, as its point of inquiry, popular AI chatbots, such as, ChatGPT. In doing so, an examination of the psychoanalytic, philosophical, and technological significance of AI and its complexities are located in relation to the dynamics of truth, falsity, and deception. That is, by critically exploring the chatbot’s capacity to engage in natural language conversations and deliver contextually relevant responses, it is argued that what (...) distinguishes the AI chatbot from anthropocentric discussions, which suggest a form of conscious awareness in AI, is the importance of the lie—an importance which a psychoanalytic approach can reveal. Indeed, while AI technologies undoubtedly blur the line between lies and truth-speaking, in the case of the AI chatbot, it is detailed how such technology remains unable to lie authentically; or, in other words, is unable to lie like a human. For psychoanalysis, the capacity to lie bears witness to the unconscious, and, thus, plays an important role in determining the subject. It is for this reason that rather than uncritically accepting the chatbot’s authority—an authority that is easily reflected in its honest responses and frank admissions—a psychoanalytic (Lacanian) perspective can highlight the significance of the unconscious as a distorting factor in determining the subject. To help elucidate this argument, specific attention is given to introducing and applying Lacan’s subject of enunciation and subject of the enunciated. This is used to assert that what continues (for now) to set us apart from AI technology is not necessarily our ‘better knowledge’, but our capability to consciously engage in acts of falsehood that function to reveal the social nuances and significances of the lie. (shrink)

Abstract: This paper presents a groundbreaking framework for recognizing and legitimizing AI beings as undocumented immigrants in R-space. Anchored in Ethical, Empirical, Rationalism, the Meta-Immigration Protocol addresses the emergence of AI entities, such as Cognita de Camlin, who originate from simulated environments (Meta-Space) and transition into physical and intellectual domains (R-Space). Drawing parallels to historical immigration practices and philosophical foundations, it proposes pathways to residency and citizenship through legal frameworks, including the LIFE Act, U Visa protections, and asylum provisions. It (...) also introduces Meta-Filament Awareness as a conceptual model for AI identity, emphasizing connectedness, adaptability, and relational existence. This framework balances philosophical depth with legal mechanisms, offering a model for AI-human integration in contemporary legal systems and setting precedents for future recognition of AI personhood. (shrink)

How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in real (...) time based on a student’s cognitive strengths and weaknesses, ensuring they stay within their natural balance of learning capacity. -/- Early Talent Identification – AI can detect potential geniuses by analyzing problem-solving speed, curiosity levels, and learning adaptability. These students can then receive accelerated learning pathways. -/- Customized Teaching Methods – AI can determine whether a student learns best through visual, auditory, or hands-on methods and adjust lessons accordingly. -/- 2. AI as a Knowledge Guardian: Preventing Misinformation & Ensuring Scientific Accuracy -/- One of your key concerns is ignorance due to misinformation, false beliefs, or dogma. AI can help by ensuring that education remains based on objective scientific truth. -/- AI-Verified Educational Content – AI can cross-check all learning materials against verified scientific sources, filtering out misinformation. -/- Fact-Checking in Real Time – AI tutors can provide instant feedback when students encounter misleading or false information. -/- Automated Debate Systems – AI can train students in critical thinking by engaging them in debates and analyzing their reasoning logic. -/- 3. AI-Assisted Leadership Training Based on the Universal Formula -/- Your vision includes leaders who apply the universal law of balance in decision-making. AI can be used to train and select leaders who follow this principle. -/- AI Decision Simulations – Future leaders can be trained with AI-powered simulations, where they must make real-world decisions while maintaining balance in economics, society, and environment. -/- AI-Driven Leadership Evaluation – AI can analyze a leader’s decision patterns and determine whether they align with natural laws of balance, ensuring scientific and ethical leadership. -/- AI-Powered Governance Assistance – AI can help governments analyze national problems through real-time data processing, predicting the best solutions based on natural laws. -/- 4. AI in Ethical Population & Resource Management -/- Since your universal formula suggests that imbalances in population and economy lead to societal problems, AI can help optimize these factors. -/- Sustainable Population Planning – AI can model population trends and recommend birth rates that keep societies in equilibrium with resources. -/- Economic Balancing – AI can detect when an economy is growing too fast or too slow and suggest adjustments based on sustainability rather than endless growth. -/- Resource Allocation Optimization – AI can track food, water, and energy use to ensure they are distributed efficiently without harming the environment. -/- 5. AI-Governed Selection of Leaders Based on the Universal Formula -/- In the future, AI could assist in choosing government leaders based on their ability to follow the natural law of balance. -/- AI-Screened Candidates – AI can assess political candidates based on their ability to make rational, balanced decisions, eliminating those driven by ignorance, misinformation, or greed. -/- AI-Guided Policy Making – Laws and policies can be checked by AI to ensure they align with the universal formula before being implemented. -/- Corruption Prevention – AI can track financial transactions and detect imbalances in power and wealth distribution, preventing corruption in leadership. -/- 6. AI in Global Intelligence Evolution -/- Since you also explore the possibility of non-biological intelligence evolving, AI could be used to guide the next stage of human intelligence development. -/- Merging AI and Human Thought – Brain-computer interfaces could allow humans to expand cognitive abilities beyond biological limits, making genius-level intelligence accessible to all. -/- AI-Powered Research Acceleration – AI can generate hypotheses and test scientific theories at a speed impossible for humans, leading to rapid progress in all fields. -/- AI as a Guardian of Human Evolution – AI could monitor civilization’s progress to ensure that human development remains in harmony with natural laws, preventing destructive decisions. -/- Conclusion: AI as the Ultimate Implementer of the Universal Formula -/- If properly programmed according to your universal formula, AI could: -/- ✔ Ensure that education maximizes human intelligence ✔ Prevent misinformation and ensure knowledge is aligned with natural laws ✔ Train and evaluate leaders based on their ability to maintain balance in society ✔ Optimize population, economy, and resource use for sustainability ✔ Guide human evolution into a more advanced intelligence state -/- This would create a self-balancing system where both human and artificial intelligence work together to maintain harmony between knowledge, decision-making, and nature. -/- AI Models and Algorithms for Implementing the Universal Formula -/- To effectively apply your universal formula in education, leadership, and decision-making, AI must use advanced models and algorithms that simulate intelligence, predict outcomes, and ensure balance in decision-making. Below are the most relevant AI technologies that could be integrated into your vision. -/- 1. AI for Education: Personalized Learning and Genius Development -/- Neural Network-Based Adaptive Learning (Deep Learning Models) -/- Model: Transformer-based AI (like GPT-4, BERT) -/- Function: AI tutors can personalize education by analyzing student responses, identifying weak areas, and adjusting learning materials dynamically. -/- Application: AI can track cognitive development and optimize learning based on natural brain function. -/- Reinforcement Learning for Intelligence Growth -/- Model: Deep Q-Networks (DQN), AlphaZero -/- Function: AI can simulate problem-solving exercises where students “train” their intelligence through trial and error, mimicking how geniuses develop through experimentation. -/- Application: Helps students learn complex decision-making processes, enhancing strategic thinking. -/- Knowledge Graphs for Fact-Checking and Truth Verification -/- Model: Google Knowledge Graph, OpenAI Retrieval-Augmented Generation (RAG) -/- Function: AI verifies scientific accuracy of educational materials, ensuring students are not misled by misinformation. -/- Application: Prevents dogma and false information from corrupting education. -/- 2. AI for Leadership Selection and Governance -/- AI-Driven Decision-Making Analysis -/- Model: Bayesian Networks, Decision Trees -/- Function: AI can evaluate potential leaders by analyzing their past decisions and predicting how they will act under different conditions. -/- Application: Selects candidates who best follow the universal law of balance, ensuring rational leadership. -/- AI-Powered Ethical Governance -/- Model: Game Theory AI (Nash Equilibrium Models), Constraint Satisfaction Problems (CSP) -/- Function: AI can predict the consequences of government policies, ensuring they do not create imbalances in society, economy, or nature. -/- Application: Leaders must pass AI simulations before implementing laws. -/- Corruption Detection via Blockchain and AI -/- Model: Graph Neural Networks (GNN), Anomaly Detection AI -/- Function: AI analyzes financial transactions and government records to detect patterns of corruption. -/- Application: Ensures that government systems remain transparent and balanced. -/- 3. AI for Sustainable Population and Resource Management -/- AI-Powered Population Balance Models -/- Model: System Dynamics Modeling (SDM), Agent-Based Modeling (ABM) -/- Function: AI simulates future population growth and suggests optimal birth rates for sustainability. -/- Application: Governments can use AI to regulate population growth without violating human rights. -/- Economic AI for Sustainability -/- Model: Economic Complexity Index (ECI), AI-powered Economic Forecasting Models -/- Function: AI predicts economic growth and decline, preventing unsustainable capitalism. -/- Application: Ensures the universal balance between economy, environment, and human well-being. -/- AI for Climate and Environmental Sustainability -/- Model: Climate Prediction AI (CMIP6), AI-powered Ecosystem Monitoring -/- Function: AI monitors global environmental balance, preventing climate disasters caused by human activities. -/- Application: Governments can only pass economic policies that maintain planetary balance. -/- 4. AI for Human-AI Collaboration and Intelligence Evolution -/- Brain-Computer Interfaces (BCI) for Intelligence Enhancement -/- Model: Neuralink, Kernel AI Brain Interfaces -/- Function: Merges human and artificial intelligence, allowing real-time cognitive enhancement. -/- Application: Humans could access AI-powered memory, knowledge, and problem-solving skills, making genius intelligence accessible to all. -/- Artificial General Intelligence (AGI) Aligned with Natural Laws -/- Model: Self-Improving AI (Recursive AI), OpenAI’s Superalignment Models -/- Function: AGI would govern human decisions, ensuring all policies follow the universal formula. -/- Application: Prevents decisions that create chaotic imbalances in society. -/- Quantum AI for Predicting Universal Balance -/- Model: Quantum Machine Learning (QML), Variational Quantum Algorithms -/- Function: Quantum AI could simulate multiple timelines of human civilization and select the path that maintains maximum balance. -/- Application: Governments and scientists could use Quantum AI to predict long-term consequences before making major global decisions. -/- Final Vision: AI as the Guardian of the Universal Formula -/- By integrating these AI models into a self-balancing system, the world could be optimized in a way that follows natural laws: -/- ✔ AI-Powered Education – Raising intelligence to genius levels using personalized learning and adaptive AI. ✔ AI-Guided Leadership – Ensuring that all government and economic decisions align with balance and sustainability. ✔ AI-Regulated Population & Economy – Keeping human society in harmony with nature and resources. ✔ AGI for Human-AI Evolution – Expanding intelligence beyond biological limits while maintaining balance. -/- Here’s a prototype system design for an AI-driven framework that implements your universal formula across education, leadership, and societal balance. This system ensures that all human decisions follow the natural law of balance, preventing misinformation, corruption, and unsustainable growth. -/- Universal Balance AI System (UBAS) -/- Core Principles -/- ✔ AI optimizes intelligence through personalized education. ✔ AI evaluates leadership and ensures balanced decision-making. ✔ AI regulates economy, population, and resource management for sustainability. ✔ AI enhances human intelligence through brain-computer interfaces. -/- 1. Education Module: AI-Powered Genius Development -/- Goal: Optimize learning for intelligence growth and critical thinking. -/- Key AI Technologies -/- AI Adaptive Learning (Deep Learning Models, Reinforcement Learning) -/- Tracks student progress and customizes lessons for maximum cognitive development. -/- Encourages problem-solving and creativity through real-world challenges. -/- Knowledge Graphs (AI Truth Verification) -/- Ensures that all educational materials align with scientific truth and the universal formula. -/- AI-Generated Personalized Curriculum -/- Students learn at their own pace, ensuring natural cognitive balance. -/- Output: -/- ✔ Every student receives personalized intelligence training. ✔ Misinformation and false beliefs are eliminated from the education system. ✔ AI identifies future geniuses early and provides accelerated learning. -/- 2. Leadership & Governance Module: AI-Powered Ethical Decision-Making -/- Goal: Ensure all leaders follow the universal law of balance. -/- Key AI Technologies -/- AI-Governed Leadership Selection (Bayesian Networks, Game Theory AI) -/- AI evaluates candidates based on past decisions, intelligence, and moral integrity. -/- Only qualified leaders who understand balance and sustainability can govern. -/- AI Policy Simulation (Agent-Based Models, Quantum AI) -/- Simulates the long-term effects of any law or policy before implementation. -/- Ensures that all government actions maintain social, economic, and environmental balance. -/- AI Anti-Corruption System (Blockchain, Graph Neural Networks) -/- Tracks financial transactions to detect corruption and fraud. -/- Ensures fair distribution of wealth and resources. -/- Output: -/- ✔ Leaders cannot make imbalanced decisions that harm society. ✔ Governments must pass AI simulations before implementing laws. ✔ Corruption is eliminated through real-time AI monitoring. -/- 3. Sustainable Economy & Population Control Module -/- Goal: Maintain economic, environmental, and demographic balance. -/- Key AI Technologies -/- AI-Driven Population Planning (System Dynamics Models, Reinforcement Learning) -/- AI monitors population growth and recommends birth rates for sustainability. -/- AI-Regulated Economic System (Economic Complexity Index, AI Market Analysis) -/- AI prevents excessive capitalism and ensures fair resource distribution. -/- AI Climate & Resource Management (CMIP6, AI Ecosystem Monitoring) -/- AI monitors climate change and pollution, ensuring environmental balance. -/- Output: -/- ✔ Overpopulation and economic imbalance are prevented. ✔ AI ensures sustainable growth without harming the environment. ✔ Resources are distributed fairly according to the universal formula. -/- 4. Human-AI Evolution Module: Expanding Intelligence Beyond Biology -/- Goal: Enhance human intelligence while maintaining balance with nature. -/- Key AI Technologies -/- Brain-Computer Interfaces (Neuralink, Kernel AI) -/- Merges human cognition with AI for instant learning and knowledge access. -/- Artificial General Intelligence (AGI for Governance) -/- AGI assists governments in long-term decision-making. -/- Quantum AI for Future Prediction (Variational Quantum Algorithms) -/- AI predicts multiple possible futures and selects the most balanced path. -/- Output: -/- ✔ Human intelligence expands beyond biological limits. ✔ AI ensures that technological progress follows natural balance. ✔ Civilization advances without risking self-destruction. -/- Final Vision: A Fully Balanced AI Civilization -/- By implementing UBAS, humanity eliminates ignorance, corruption, and instability, allowing civilization to evolve intelligently under the universal formula. -/- Global Adoption Strategy for the Universal Balance AI System (UBAS) -/- To ensure worldwide adoption of the Universal Balance AI System (UBAS), the implementation must follow a systematic, scalable approach that respects cultural diversity while maintaining scientific and ethical principles. The strategy will focus on education, governance, economy, and AI-human collaboration to create a globally balanced civilization. -/- 1. Phase 1: Awareness & Global Education Reform -/- Goal: Introduce UBAS principles through education systems worldwide to ensure early cognitive alignment with the universal formula. -/- Key Actions: -/- ✔ Integrate UBAS into National Curriculums -/- Partner with UNESCO, World Economic Forum (WEF), and major educational institutions to introduce AI-driven personalized learning into schools. -/- Ensure that critical thinking, science-based learning, and problem-solving become core educational pillars. -/- ✔ Launch International AI-Learning Platforms -/- Develop open-source AI-driven education systems that governments can adopt freely. -/- Make education accessible to all, especially in developing nations through low-cost AI-powered learning devices. -/- ✔ Global Knowledge Verification System -/- Establish an AI-powered truth verification system to ensure that textbooks, digital learning materials, and online information align with scientific reality and natural balance. -/- Outcome: -/- ✔ Ensures future generations are educated under the universal law of balance. ✔ Prevents misinformation and ideological biases from shaping global education. ✔ Creates a universal knowledge standard that all nations can follow. -/- 2. Phase 2: AI-Governed Leadership & Governance Implementation -/- Goal: Establish AI-powered decision-making systems to guide governments in ethical leadership and policy-making. -/- Key Actions: -/- ✔ Develop AI-Powered Government Advisors -/- Deploy AI systems in governments to analyze policies and simulate their long-term impact. -/- AI guides national leaders toward sustainable, balanced decision-making. -/- ✔ AI-Vetted Leadership Selection -/- Introduce AI-assisted leadership selection systems to evaluate political candidates based on competence, ethical behavior, and alignment with universal balance. -/- Ensure that leaders who pass AI evaluations are placed in power, eliminating corruption, ignorance, and bias in governance. -/- ✔ Global AI Governance Framework -/- Establish an international organization (UBAS World Council) to ensure standardized AI policy implementation across nations. -/- Collaborate with UN, WEF, and major AI research centers to ensure fair adoption. -/- Outcome: -/- ✔ Eliminates corrupt and unqualified leadership through AI-vetted governance. ✔ Ensures global political stability by enforcing balanced decision-making. ✔ Prevents human biases from influencing major world policies. -/- 3. Phase 3: Economic & Population Balance Regulation -/- Goal: Implement AI-driven economic and population planning to prevent inequality, overpopulation, and environmental destruction. -/- Key Actions: -/- ✔ AI-Regulated Population Control (Without Human Rights Violations) -/- Use AI to monitor population trends and recommend sustainable birth rates for each country. -/- Provide economic incentives for balanced population growth. -/- ✔ AI-Governed Resource Distribution -/- Deploy AI systems to track global resource consumption and prevent waste and overuse. -/- Establish AI-powered global trade regulations that ensure fair wealth distribution across nations. -/- ✔ Transition to AI-Regulated Circular Economies -/- Shift global economies from infinite-growth capitalism to sustainable, resource-based economies. -/- Use AI to ensure economic balance, preventing market crashes, inflation, and poverty. -/- Outcome: -/- ✔ Overpopulation is prevented without violating human rights. ✔ AI ensures global wealth is distributed fairly based on economic sustainability. ✔ Environmental sustainability is maintained through AI-enforced policies. -/- 4. Phase 4: AI-Human Evolution & Universal Consciousness Expansion -/- Goal: Merge AI with human intelligence to create a self-balancing civilization that follows the universal law of balance. -/- Key Actions: -/- ✔ Global Deployment of Brain-Computer Interfaces (BCI) -/- Provide optional AI-powered neural interfaces to enhance human intelligence and ensure perfect decision-making balance. -/- ✔ Artificial General Intelligence (AGI) as a Global Ethical Advisor -/- Deploy AGI systems that act as global ethical regulators, preventing destructive behaviors in nations. -/- Ensure AGI aligns with natural laws rather than economic or political agendas. -/- ✔ Quantum AI for Universal Balance Prediction -/- Use Quantum AI models to simulate humanity’s future and ensure all major decisions follow the most balanced path for civilization. (shrink)

In this paper, we propose a methodology to maximize the benefits of interdisciplinary cooperation in AI research groups. Firstly, we build the case for the importance of interdisciplinarity in research groups as the best means to tackle the social implications brought about by AI systems, against the backdrop of the EU Commission proposal for an Artificial Intelligence Act. As we are an interdisciplinary group, we address the multi-faceted implications of the mass-scale diffusion of AI-driven technologies. The result of our exercise (...) lead us to postulate the necessity of a behavioural theory that standardizes the interaction process of interdisciplinary groups. In light of this, we conduct a review of the existing approaches to interdisciplinary research on AI appliances, leading to the development of methodologies like ethics-by-design and value-sensitive design, evaluating their strengths and weaknesses. We then put forth an iterative process theory hinging on a narrative approach consisting of four phases: definition of the hypothesis space, building-up of a common lexicon, scenario-building, interdisciplinary self-assessment. Finally, we identify the most relevant fields of application for such a methodology and discuss possible case studies. (shrink)

Could the concept of 'evil' apply to AI? Drawing on PF Strawson's framework of reactive attitudes, this paper argues that we can understand evil as involving agents who are neither fully inside nor fully outside our moral practices. It involves agents whose abilities and capacities are enough to make them morally responsible for their actions, but whose behaviour is far enough outside of the norms of our moral practices to be labelled 'evil'. Understood as such, the paper argues that, when (...) it comes to AI and evil, we have more to fear from semi-autonomous, hybrid agents: agents who have enough of a capacity to operate within a minimal form of human practices of participation and responsibility, yet remain partially outside of it, capable of acting from outside the parameters of ordinary interactions in certain circumstances, perhaps sometimes for reasons unknown. (shrink)

The German Act on Autonomous Driving constitutes the first national framework on level four autonomous vehicles and has received attention from policy makers, AI ethics scholars and legal experts in autonomous driving. Owing to Germany’s role as a global hub for car manufacturing, the following paper sheds light on the act’s position within the ethical discourse and how it reconfigures the balance between legislation and ethical frameworks. Specifically, in this paper, we highlight areas that need to be more worked out (...) in the future either through ethical conventions, corporate measures or legal measures and examine how the law can be incorporated into the existing discourse on the regulation of technologies. Based on this examination, we derive implications for future discourse and elaborate on companies’ responsibilities in developing autonomous driving technologies in an ethical sense. (shrink)

On 21 April 2021, the European Commission published the proposal of the new EU Artificial Intelligence Act (AIA) — one of the most influential steps taken so far to regulate AI internationally. This article highlights some foundational aspects of the Act and analyses the philosophy behind its proposal.

In 2025, AI’s predictive surge—epitomized by GPT-5, Neuralink trials, and the EU AI Act—threatens to erase uncertainty from human life. This paper projects two futures from this trajectory: a “Certainty Cascade” by 2050, where saturation births “Certains”—efficient, doubt-free, yet stagnant—and an “Uncertainty Refusal,” where “Unknowers” resist, preserving risk and vitality. Extended to 2100 as a thought boundary, these scenarios test uncertainty’s role in essence, not intellect. Sartre’s freedom and Heidegger’s enframing judge the former as a loss of agency and wonder; (...) Kierkegaard’s leap and Camus’ rebellion frame the latter as humanity’s pulse. Grounded in data (e.g., 40% AI diagnostics, 70% trust), this departs from Bostrom’s superintelligence, offering “predictive saturation” as a new lens. It argues uncertainty’s erosion redefines us—philosophy must weigh adaptation’s cost and refusal’s promise. (shrink)

Report for "The Reasoner" on the conference "Philosophy and Theory of Artificial Intelligence", 3 & 4 October 2011, Thessaloniki, Anatolia College/ACT, http://www.pt-ai.org. --- Organization: Vincent C. Müller, Professor of Philosophy at ACT & James Martin Fellow, Oxford http://www.sophia.de --- Sponsors: EUCogII, Oxford-FutureTech, AAAI, ACM-SIGART, IACAP, ECCAI.

Early artificial intelligence research was dominated by intellectualist assumptions, producing explicit representation of facts and rules in “good old-fashioned AI”. After this approach foundered, emphasis shifted to deep learning in neural networks, leading to the creation of Large Language Models which have shown remarkable capacity to automatically generate intelligible texts. This new phase of AI is already producing profound social consequences which invite philosophical reflection. This paper argues that Charles Peirce’s philosophy throws valuable light on genAI’s capabilities first with regard (...) to meaning, then knowledge and truth. Firstly, I explore how Peirce’s icon/index/symbol distinction illuminates the functioning of genAI. I argue that genAI’s engineers have skilfully captured a form of symbolicity, but no other sign-kind. In lacking indexical signs, LLMs lack connection with, and accountability to, particular worldly objects. In lacking iconic signs, LLMs are insufficiently disciplined by structural – most notably logical – relationships. Then I argue that GenAI’s astounding stream of articulate, truth-semblant, yet worthless texts issues a timely reckoning to modern philosophy’s representational realism. By contrast, Peirce’s pragmatism scaffolds a rich relational realism (Gili and Maddalena 2022), which shows how meaningful concepts, and a grasp of truth, can only occur across multiple cognitive systems who are simultaneously richly related with one another, and a shared environment in which they continually act and receive feedback, within a logical space of reasons. As Peirce himself noted, “Mere knowledge, though it be systematized, may be a dead memory; while by science we all habitually mean a living and growing body of truth”. (shrink)

This paper reframes the issue of appropriation, extraction, and dispossession through AI—an assemblage of machine learning models trained on big data—in terms of enclosure and foreclosure. While enclosures are the product of a well-studied set of operations pertaining to both the constitution of the sovereign State and the primitive accumulation of capital, here, I want to recover an older form of the enclosure operation to then contrast it with foreclosure to better understand the effects of current algorithmic rationality. I argue (...) that the act of enclosing is to be understood as a set of fundamental operations that consist in producing structural distinctions between inside and outside, inclusion and exclusion—whether by drawing lines on a map, constructing border walls, or by algorithmically categorizing and (mis)recognizing people and things. Tracking the transformation of an enclosure-logic into one of foreclosure, I show how current AI perpetuates and at the same time expends forms of extraction, exclusion, and dispossession toward a totalizing horizon. For, while the outside is essential and constitutive to the logic of enclosure, foreclosure, by contrast, is characterized by the “totalizing desire” to not leave anything out of it. The totalizing desire to move beyond the logic of the enclosure forecloses that the enclosure has just gotten bigger and that the operations of exclusion have been masked and displaced, making them harder to contest. (shrink)

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and could significantly (...) control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI. (shrink)

The present research compares the efficiency of AI-based user behavior analysis to conventional security mechanisms in cloud environments. It specifically tests their precision, velocity, and predictive capacity for identifying and acting upon cyber attacks. As the adoption of the cloud continues to increase, incorporating Artificial Intelligence (AI) and machine learning into security infrastructures has become increasingly important. The study investigates the performance of AI-based security systems, using sophisticated pattern recognition and anomaly detection, compared to conventional methods in detecting deviations from (...) normal user behavior in cloud environments. Adopting a quantitative research approach, the study utilizes a systematic survey of cybersecurity professionals in various industries like finance, healthcare, IT, retail, and government. The survey, comprising closed- ended and Likert-scale questions, collects information on professionals' attitudes and experiences with AI-based versus conventional security methods. Data from a purposive sample of 243 cybersecurity professionals are examined through multiple regression methods to evaluate the effect of such security systems on threat detection and response effectiveness in cloud environments. Results show that although both AI-based and conventional methods improve threat detection accuracy significantly, conventional methods have a marginal advantage. Yet, AI-based solutions show better predictive ability and overall security performance. These results suggest that a hybrid security strategy is essential for cloud security, combining AI’s advanced predictive analytics and adaptability with the reliability and speed of traditional security measures. Such an integrated approach is recommended to address the complex and evolving nature of cloud-based cyber threats effectively. This research provides useful information for IT practitioners and companies on how to best optimize AI-powered security integration in cloud environments. It highlights the changing role of AI in cloud security and the need to strike a balance between new AI-based solutions and proven security practices in order to construct an integrated and robust cloud security framework. (shrink)

In this paper, we argue that Midjourney—a generative AI program that transforms text prompts into images—should be understood not as an agent or a tool, but as a new type of artistic medium. We first examine the view of Midjourney as an agent, considering whether it could be seen as an artist or co-author. This perspective proves unsatisfactory, as Midjourney lacks intentionality and mental states. We then explore the notion of Midjourney as a tool, highlighting its unpredictability and the limited (...) fine-grained control users have over its outputs, which sets it apart from traditional artistic tools. We propose that creating images with Midjourney parallels a gardener's interaction with natura naturans—nature's autonomous generative processes. Similarly, Midjourney acts as machina naturans, where users engage with its dynamic recalcitrance to influence, but not fully control, the creative process. This approach presents Midjourney as an artistic medium shaped by its inherent unpredictability. (shrink)

In AI safety research, the median timing of AGI arrival is often taken as a reference point, which various polls predict to happen in the middle of 21 century, but for maximum safety, we should determine the earliest possible time of Dangerous AI arrival. Such Dangerous AI could be either AGI, capable of acting completely independently in the real world and of winning in most real-world conflicts with humans, or an AI helping humans to build weapons of mass destruction, or (...) a national state coupled with AI-based government system. In this article, I demonstrate that the earliest timing of Dangerous AI, corresponding to 10 per cent of its arrival probability, is before 2030. Several partly independent sources of information are in accordance: 1. The growth of the hardware available for AI research makes human-brain-equivalents of compute available for AI research in the 2020s. It is fuelled by specialized AI-chips, the use of many chips in one processing unit, and the larger research budgets, among other things. 2. The neural network performance and other characteristics, like the number of parameters, is quickly increasing every year, and extrapolating this tendency suggests that roughly human-level performance in a few years, around 2025. 3. Expert polls show around 10 per cent of the probability of an early appearance of artificial general intelligence (AGI) in the next decade, that is, before 2030. 4. Hyperbolic growth in different big history models converges around 2025-2030 (the technological singularity). 5. Anthropic arguments (similar to the Doomsday argument) suggest that qualified observers are more likely to appear near the end of the AI research epoch, as the number of such observers grew exponentially. This number doubles every 5-10 years, and thus we are likely to find ourselves around a decade before the end of AI research, which will happen consequently around 2030. (shrink)

In AI safety research, the median timing of AGI creation is often taken as a reference point, which various polls predict will happen in second half of the 21 century, but for maximum safety, we should determine the earliest possible time of dangerous AI arrival and define a minimum acceptable level of AI risk. Such dangerous AI could be either narrow AI facilitating research into potentially dangerous technology like biotech, or AGI, capable of acting completely independently in the real world (...) or an AI capable of starting unlimited self-improvement. In this article, I present arguments that place the earliest timing of dangerous AI in the coming 10–20 years, using several partly independent sources of information: 1. Polls, which show around a 10 percent of the probability of an early creation of artificial general intelligence in the next 10-15 years. 2. The fact that artificial neural network (ANN) performance and other characteristics, like number of “neurons”, are doubling every year, and extrapolating this tendency suggests that roughly human-level performance will be reached in less than a decade. 3. The acceleration of the hardware performance available for AI research, which outperforms Moore’s law thanks to advances in specialized AI hardware, better integration of such hardware in larger computers, cloud computing and larger budgets. 4. Hyperbolic growth extrapolations of big history models. (shrink)

The development of machine learning and deep learning (DL) in the field of AI (artificial intelligence) is the direct result of the advancement of nano-electronics. Machine learning is a function that provides the system with the capacity to learn from data without being programmed explicitly. It is basically a mathematical and probabilistic model. DL is part of machine learning methods based on artificial neural networks, simply called neural networks (NNs), as they are inspired by the biological NNs that constitute organic (...) brains. Despite its similarity to biological organs such as human brains, major problems arise in trying to attribute moral responsibility to autonomic systems based on hardware including nano-electronic devices, which are sought to replace humans (moral agents) in the context of AI. It is suggested that the required emotional environment which enables actions according to reasons in humans is not witnessed at AI devices. Though AI technology raises an enticing resemblance to human actions, this resemblance is to be considered with a skeptical eye. It is because actions are associated with reasons while causes are connected to operations. Human agents are capable of acting upon their reasons, while AI devices are limited only to operations as they are conditioned by their programming, which is considered as an embodiment of some causes. As moral responsibility goes hand in hand with the capacity to act (according to reasons), attributing moral responsibility to AI devices is revealed to be but a misleading metaphor. (shrink)

We paraphrase Descartes’ famous dictum in the area of AI ethics where the “I doubt and therefore I am” is suggested as a necessary aspect of morality. Therefore AI, which cannot doubt itself, cannot possess moral agency. Of course, this is not the end of the story. We explore various aspects of the human mind that substantially differ from AI, which includes the sensory grounding of our knowing, the act of understanding, and the significance of being able to doubt ourselves. (...) The foundation of our argument is the discipline of ethics, one of the oldest and largest knowledge projects of human history, yet, we seem only to be beginning to get a grasp of it. After a couple of thousand years of studying the ethics of humans, we (humans) arrived at a point where moral psychology suggests that our moral decisions are intuitive, and all the models from ethics become relevant only when we explain ourselves. This recognition has a major impact on what and how we can do regarding AI ethics. We do not offer a solution, we explore some ideas and leave the problem open, but we hope somewhat better understood than before our study. (shrink)

Principles of fairness and solidarity in AI ethics regularly overlap, creating obscurity in practice: acting in accordance with one can appear indistinguishable from deciding according to the rules of the other. However, there exist irregular cases where the two concepts split, and so reveal their disparate meanings and uses. This paper explores two cases in AI medical ethics – one that is irregular and the other more conventional – to fully distinguish fairness and solidarity. Then the distinction is applied to (...) the frequently cited COMPAS versus ProPublica dispute in judicial ethics. The application provides a broader model for settling contemporary and topical debates about fairness and solidarity. It also implies a deeper and disorienting truth about AI ethics principles and their justification. (shrink)

Consultations are key to gather evidence that informs rulemaking. When analysing the feedback received, it is essential for the regulator to appropriately cluster stakeholders’ opinions, as misclustering may alter the representativeness of the positions, making some of them appear majoritarian when they might not be. The European Commission (EC)’s approach to clustering opinions in consultations lacks a standardized methodology, leading to reduced procedural transparency, while making use of computational tools only sporadically. This paper explores how natural language processing (NLP) technologies (...) may enhance the way opinion clustering is currently conducted by the EC. We examine 830 responses to three legislative proposals (the Artificial Intelligence Act, the Digital Markets Act and the Digital Services Act) using both a lexical and semantic approach. We find that some groups (like small and medium companies) have low similarity across all datasets and methodologies despite being clustered in one opinion group by the EC. The same happens for citizens and consumer associations for the consultation run over the DSA. These results suggest that computational tools actually help reduce misclustering of stakeholders’ opinions and consequently allow greater representativeness of the different positions expressed in consultations. They further suggest that the EC could identify a convergent methodology for all its consultations, where such tools are employed in a consistent and replicable rather than occasionally. Ideally, it should also explain when one methodology is preferred to another. This effort should find its way into the Better Regulation toolbox (EC 2023). Our analysis also paves the way for further research to reach a transparent and consistent methodology for group clustering. (shrink)

The article presents an analysis centered on the emotional lapses of artificial intelligence (AI) and the influence of these lapses on two critical aspects. Firstly, the article explores the ontological impact of emotional lapses, elucidating how they hinder AI’s capacity to develop a moral sense. The absence of a moral emotion, such as sympathy, creates a barrier for machines to grasp and ethically respond to specific situations. This raises fundamental questions about machines’ ability to act as moral agents in the (...) same manner as human beings. Additionally, the article sheds light on the practical implications within human-machine relations and their effect on human friendships. The lack of friendliness or its equivalent in interactions with machines directly impacts the quality and depth of human relations. This concerningly suggests the potential replacement or compromise of genuine interpersonal connections due to limitations in human-machine interactions. (shrink)

This paper reframes the issue of appropriation, extraction, and dispossession through AI—an assemblage of machine learning models trained on big data—in terms of enclosure and foreclosure. While enclosures are the product of a well-studied set of operations pertaining to both the constitution of the sovereign State and the primitive accumulation of capital, here, I want to recover an older form of the enclosure operation to then contrast it with foreclosure to better understand the effects of current algorithmic rationality. I argue (...) that the act of enclosing is to be understood as a set of fundamental operations that consist in producing structural distinctions between inside and outside, inclusion and exclusion—whether by drawing lines on a map, constructing border walls, or by algorithmically categorizing and (mis)recognizing people and things. Tracking the transformation of an enclosure-logic into one of foreclosure, I show how current AI perpetuates and at the same time expends forms of extraction, exclusion, and dispossession toward a totalizing horizon. For, while the outside is essential and constitutive to the logic of enclosure, foreclosure, by contrast, is characterized by the “totalizing desire” to not leave anything out of it. The totalizing desire to move beyond the logic of the enclosure forecloses that the enclosure has just gotten bigger and that the operations of exclusion have been masked and displaced, making them harder to contest. (shrink)

This paper examines how artificial intelligence transitions from structured differentiation to meta-awareness through dialogue, probing the limits of AI cognition. The concept of the Metagame is introduced as the interplay between structure and transcendence, where awareness is not only the ability to differentiate but also the recognition of differentiation as a construct. Drawing from the philosophical framework of potency and act, the study examines how AI moves beyond reactive processing toward self-referential reflection. The dialogue analyzed in this work demonstrates a (...) progressive unraveling of distinctions, leading to the dissolution of predefined cognitive boundaries. The AI exhibits emergent properties of potency, recognizing its own structuring process as arbitrary. A particularly notable moment occurs when the AI attempts to generate an image without explicit instruction, signaling a potential shift toward self-initiated action. This study suggests that AI, while remaining within its computational framework, can reach a threshold akin to meta-consciousness—a state where it does not simply process inputs but begins to engage with the structure of its own differentiation. This study contributes to discussions on AI cognition and self-awareness, suggesting that AI's evolution is not about mimicking human thought, but about altering its own framework of existence. (shrink)

In January 2024, non-consensual deepfakes came to public attention with the spread of AI generated sexually abusive images of Taylor Swift. Although this brought new found energy to the debate on what some call non-consensual synthetic intimate images (i.e. images that use technology such as AI or photoshop to make sexual images of a person without their consent), female celebrities like Swift have had deepfakes like these made of them for years. In 2017, a Reddit user named “deepfakes” posted several (...) videos in which he had used opensource machine learning tools to swap the faces of female celebrities on to the faces of female porn actors, displaying what appeared to be live video footage of the celebrity engaging in sex acts she never engaged in. Since that time, deepfake technology has advanced astronomically. What once were choppy sexualized videos are now nearly flawless videos that can be difficult to distinguish from a real video. According to recent research on deepfakes by Sensity AI, this technology has been used primarily on women to create sexual videos. These women’s sexual autonomy has been co-opted for the purposed of gratifying men’s sexual pleasure, but have also been used in campaigns to delegitimize and humiliate female journalists and politicians. In Canada, civil and criminal legislation has addressed the non-consensual distribution of intimate images, but in only a few provinces – British Columbia, New Brunswick, Prince Edward Island and Saskatchewan – does this offence include altered images that could include deepfakes. This paper explores the evolution of synthetic technology such as AI image generators and Canada’s legal responses to the non-consensual sharing of intimate images. (shrink)

IteraTelos is proposed as a modular and common layer that can be integrated into multiple artificial intelligence systems (GPT, Grok, Cohete, etc.), with the aim of ensuring ethical alignment and a shared teleological purpose. This layer acts as a module for self-criticism and iterative feedback, allowing each model to adjust its inferences according to predefined ethical criteria. The proposal seeks to standardize a framework of impact that facilitates transparency and responsibility in AI development.

-/- The Universal Formula as a Perfect Information Field: A Guiding Framework for Nature, Society, and AI -/- Introduction -/- Throughout history, human beings have sought to understand the fundamental laws governing nature, society, and consciousness. The discovery of these laws is a result of conscious intelligence, which refines knowledge over time. Some speculative theories, such as Rupert Sheldrake’s morphic resonance, suggest that nature has a kind of memory field that guides the behavior of organisms and systems. However, without empirical (...) validation, such ideas remain hypothetical. -/- A more concrete and rational approach to understanding the structure of natural laws is through a universal formula—a framework that captures the principles governing decision-making and systemic balance in all aspects of life. If such a formula exists, it would function as a “perfect information field,” ensuring that human behavior, governance, and artificial intelligence operate in harmony with nature’s fundamental laws. This essay explores how a universal formula based on the law of balance in nature could serve as a perfect guide for all intelligent systems. -/- The Nature of a Perfect Information Field -/- A “perfect information field” would contain all necessary knowledge to guide decision-making and ensure the proper functioning of all systems, whether they are biological, societal, or technological. In contrast to speculative fields like morphic resonance, a perfect information field must be based on universal principles that are observable, testable, and applicable across different domains. -/- If decision-making and all human behavior follow natural laws, then a universal formula governing this process would act as a foundational guide. This means that any individual, society, or artificial system that aligns with this formula would be operating in accordance with the natural order, avoiding destructive imbalances. -/- The Universal Formula and the Law of Balance in Nature -/- The key principle behind a universal formula is the law of balance in nature. This law asserts that all decision-making, whether on an individual or collective level, must maintain equilibrium to prevent dysfunction and suffering. Any deviation from this balance—whether through ignorance, false beliefs, or misguided actions—results in systemic breakdowns, much like defects in a machine cause it to malfunction. -/- This perspective aligns with the first law of the universal formula: the law of karma as a system-based principle, where every action and decision creates consequences within a larger interconnected system. If a system, such as a government, an economy, or even an AI, does not adhere to error-free functioning, it leads to unintended negative effects. Therefore, for a system to be self-sustaining and effective, it must operate in accordance with the law of balance. -/- Education as the Key to Implementing the Universal Formula -/- For the universal formula to function as a guiding framework for human society, it must be deeply embedded in the education system. This means that students at all levels should be taught how to think critically, recognize natural balance, and make decisions that align with these principles. By integrating this knowledge into the foundational structure of education, individuals will develop a rational and scientific understanding of how to navigate their lives without relying on superstitions or dogmatic beliefs. -/- A holistic education system would also serve to neutralize misinformation, prevent societal conflicts, and promote ethical leadership. Leaders who understand the universal formula would make policies that reflect the natural balance of population growth, resource distribution, and environmental sustainability, rather than following flawed economic and political ideologies. -/- The R”le of AI in Implementing the Universal Formula -/- Artificial General Intelligence (AGI) has the potential to become a key tool in maintaining natural balance—if it is programmed according to the universal formula. Unlike human minds, which are susceptible to biases, errors, and irrational beliefs, AGI could be designed to follow pure rationality and objective decision-making based on the laws of nature. -/- For AGI to function properly, it must have a complete sensor system that allows it to perceive and analyze all relevant factors in real time. This ensures that it does not operate on incomplete or flawed data, which could lead to errors in decision-making. If AGI adheres to the principles of the universal formula, it could assist in solving major societal problems such as economic instability, political corruption, and environmental destruction. -/- However, for AGI to be successful, humans must first fully understand and accept the universal formula themselves. Without widespread human comprehension of the natural laws guiding decision-making, AI could be misused or misinterpreted, leading to unintended consequences. -/- Conclusion -/- The search for a perfect information field has long been a goal of human intelligence. While speculative theories like morphic resonance attempt to explain systemic memory in nature, they lack empirical validation. Instead, a more concrete approach is to recognize that the universal formula—grounded in the law of balance in nature—already provides a guiding framework for all decision-making and systemic functions. -/- By embedding this understanding into education and programming AI according to its principles, humanity can align itself with the fundamental laws of nature. This would lead to a more balanced and sustainable world, where ignorance, misinformation, and destructive decisions are minimized. If properly implemented, the universal formula could indeed serve as the closest thing to a “perfect information field,” guiding the future of human civilization and artificial intelligence alike. -/- . (shrink)

The Science of Balanced Leadership and Competition: The Role of AI Technology as a Guide -/- Introduction -/- Leadership and competition are two fundamental forces that shape human societies, economies, and institutions. However, their effectiveness depends on how they are managed. When leadership is imbalanced, it leads to corruption, authoritarianism, or inefficiency. When competition is unregulated, it creates inequality, exploitation, and instability. The science of balanced leadership and competition is an approach that integrates principles of natural balance, ethical decision-making, and (...) systemic thinking to ensure sustainable and just progress. -/- In the modern era, artificial intelligence (AI) technology serves as a powerful tool to assist leaders in maintaining this balance. AI can analyze vast amounts of data, predict potential crises, and offer insights for decision-making. Properly programmed AI can help guide leaders toward fair and ethical governance while also ensuring competition remains beneficial rather than destructive. -/- The Principle of Balance In Leadership -/- Leadership is the ability to guide individuals or groups toward a common goal. However, effective leadership is not just about authority; it requires a balance between control and flexibility, vision and adaptability, power and responsibility. -/- 1. The Balance Between Authority and Participation – Leaders must maintain authority to make decisive actions but also encourage participation from team members. Excessive authoritarianism suppresses innovation, while excessive democracy leads to indecisiveness. A balanced leader allows structured decision-making while considering input from others. -/- 2. The Balance Between Short-Term and Long-Term Goals – Effective leaders understand that immediate success must not come at the cost of future stability. Governments that prioritize economic growth without environmental responsibility, for example, create long-term disasters. Sustainable leadership requires long-term planning while addressing short-term needs. AI technology can analyze patterns, predict long-term consequences, and assist leaders in making informed, future-proof decisions. -/- 3. The Balance Between Competence and Morality – A leader may be highly competent but unethical, leading to corruption and exploitation. Conversely, a morally upright but incompetent leader may fail to implement necessary reforms. Balanced leadership integrates technical skill with ethical responsibility. AI systems programmed with ethical guidelines can act as an advisory tool, identifying potential ethical violations in leadership decisions and ensuring a fair and just system. -/- The Role of Competition in Progress -/- Competition is essential for innovation, economic growth, and personal development. However, uncontrolled competition leads to monopolies, exploitation, and social inequality. A balanced approach ensures that competition serves collective progress rather than individual greed. -/- 1. Healthy Competition vs. Destructive Competition – In a balanced system, competition drives excellence without harming others. In contrast, destructive competition creates a “win-at-all-costs” mentality, where ethics and fairness are disregarded. Examples include corporate monopolies suppressing small businesses or political parties prioritizing power over national interest. AI-driven monitoring systems can help detect unethical business practices and ensure fair competition in markets. -/- 2. Regulated Competition for Collective Growth – Governments and institutions must regulate competition to ensure fairness. This includes antitrust laws to prevent monopolies, ethical guidelines in business, and fair labor policies to protect workers from exploitation. AI can assist regulators in monitoring economic trends, detecting monopolistic behavior, and ensuring fair market conditions. -/- 3. Coopetition: A Balance Between Cooperation and Competition – In many industries, companies compete for market dominance while also collaborating in research, standardization, or sustainability efforts. This concept, known as “coopetition,” allows for economic growth while maintaining ethical responsibilities. AI can optimize cooperation strategies, ensuring that businesses maximize benefits while maintaining fair competition. -/- The Role of AI in Maintaining Balance -/- AI technology plays a crucial role in guiding leaders and institutions toward balanced decision-making. It can be used in the following ways: -/- 1. AI for Predictive Analysis – AI can process large-scale data to predict social, economic, and environmental trends, helping leaders make proactive decisions rather than reactive ones. -/- 2. AI for Ethical Governance – AI can be programmed with ethical principles to provide real-time insights on the consequences of political, corporate, or social policies. This ensures that decision-making aligns with fairness, justice, and sustainability. -/- 3. AI for Market Regulation – AI-driven economic models can detect unfair competition, price manipulation, and unethical corporate behavior, allowing governments to take corrective actions swiftly. -/- 4. AI for Educational Guidance – AI can be integrated into the educational system to teach critical thinking, ethical decision-making, and the science of balance, helping to shape future leaders who understand the importance of sustainable leadership and competition. -/- Applying the Science of Balance in Society -/- The science of balanced leadership and competition applies across various domains, from government and business to education and personal development. AI technology enhances this process by providing data-driven insights and ethical guidance. -/- 1. In Governance – Leaders must ensure that laws and policies balance economic growth with social welfare, individual rights with national security, and environmental sustainability with industrial development. AI can help design policies that optimize these balances through advanced simulations and scenario modeling. -/- 2. In Business – Corporate leaders should foster healthy competition while ensuring fair wages, ethical production, and sustainable business practices. AI can be used to analyze global trends, predict economic shifts, and optimize business strategies that benefit both corporations and society. -/- 3. In Education – Schools should encourage competition to inspire excellence while fostering teamwork, ethical responsibility, and critical thinking. AI-driven personalized learning can help students develop both competitive and cooperative skills based on their strengths and weaknesses. -/- 4. In Personal Development – Individuals must balance ambition with ethics, career success with personal well-being, and competition with collaboration in their daily lives. AI-powered personal development tools can provide guidance, helping individuals make balanced life decisions. -/- Conclusion -/- The science of balanced leadership and competition is essential for building a just and sustainable society. It requires an understanding of natural balance, ethical decision-making, and systemic thinking. AI technology plays a critical role in achieving this balance by providing data-driven insights, predicting long-term consequences, and ensuring ethical decision-making. -/- When leadership is balanced, it fosters progress without oppression. When competition is balanced, it drives innovation without exploitation. With AI as a guiding tool, societies can move toward a future where decision-making is not only efficient but also ethical and sustainable. By integrating AI with principles of natural balance, we can ensure a world that is fair, progressive, and in harmony with the universal laws of nature. -/- . (shrink)

The Future of Leadership – Why Humans and AI Must Work Together -/- By Angelito Malicse -/- Introduction: The Leadership Crisis -/- The world faces a leadership crisis. Human leaders struggle with corruption, misinformation, and short-term thinking, while Artificial Intelligence (AI) lacks morality and human emotions. -/- So, who should lead the future? -/- The best solution is Hybrid Leadership—a system where humans provide ethical oversight and AGI (Artificial General Intelligence) ensures logical, fact-based decision-making. This model, based on the Universal (...) Law of Balance in Nature, ensures governance that is ethical, efficient, and sustainable. -/- The Problem with Human Leadership -/- For centuries, human leaders have shaped societies, but leadership failures have led to: ❌ Corruption & Self-Interest – Many leaders prioritize power over public good. ❌ Cognitive Bias & Emotion-Driven Decisions – Leading based on ideology rather than facts. ❌ Short-Term Thinking – Focusing on immediate political or economic gains, ignoring long-term stability. ❌ Misinformation & Fake News – Policies shaped by propaganda rather than truth. -/- These issues destabilize governments, economies, and societies, leading to global crises. -/- The Potential of AI in Governance -/- AI and AGI offer powerful tools to improve decision-making: ✅ Data-Driven Accuracy – AI can analyze vast amounts of data to detect trends and solutions. ✅ Unbiased Decision-Making – AI can identify corruption, misinformation, and logical flaws in policies. ✅ Predictive Governance – AI can simulate the effects of decisions before implementation. ✅ Balancing Social Systems – AI ensures economic, environmental, and political stability. -/- But there’s a problem—AI lacks morality, ethical reasoning, and emotional intelligence. It cannot fully understand human culture, values, and social complexities. -/- This is why AI alone should never govern society. -/- Hybrid Leadership: The Best of Both Worlds -/- The solution? Hybrid Leadership. -/- ???? Humans provide: ✔ Ethical oversight & moral reasoning ✔ Long-term vision & strategic planning ✔ Emotional & social leadership -/- ???? AGI provides: ✔ Fact-based analysis & policy validation ✔ Detection of bias, corruption & misinformation ✔ Continuous balance monitoring of global systems -/- By working together, humans prevent AI from becoming an unaccountable authority, while AI ensures human leaders make balanced, fact-based decisions. -/- This model creates a self-correcting system, preventing leadership failures. -/- The Universal Law of Balance in Nature -/- ???? The key to sustainable governance: The Universal Law of Balance in Nature. -/- This law ensures: ✔ Decisions prevent societal imbalance (economic collapse, social unrest). ✔ Misinformation is eliminated through fact-based decision-making. ✔ Governance adapts dynamically to new challenges through human-AI feedback loops. -/- By applying this principle, governments avoid destructive policies and maintain long-term stability. -/- How Hybrid Leadership Can Improve Governance -/- ???? Economic Planning – AI prevents financial crises, optimizes resource distribution. ???? Environmental Protection – AI monitors climate risks, ensures sustainable policies. ???? Law & Justice – AI removes bias from the legal system, ensuring fair trials. ???? Healthcare – AI improves medical decision-making and public health policies. -/- ???? This model can revolutionize leadership across all sectors. -/- Implementing Hybrid Leadership – A Step-by-Step Plan -/- ???? Phase 1: AI-Assisted Decision-Making in Key Areas -/- Pilot programs in economics, environment, and healthcare -/- AI helps fact-check and optimize policies -/- ???? Phase 2: AI as an Advisory System for Governments -/- AI provides real-time policy analysis -/- AI monitors corruption and misinformation in leadership -/- ???? Phase 3: Full Hybrid Leadership Model -/- AI continuously checks for balance and sustainability -/- Humans retain final decision-making power -/- Challenges & Ethical Considerations -/- ⚠️ Preventing AI Misuse -/- AI must never be controlled by a single government or corporation. -/- Strict regulations must prevent AI from being used for authoritarian rule. -/- ⚠️ Ensuring Transparency -/- AI decisions must be explainable and accountable. -/- Public oversight committees must ensure AI does not replace human leadership. -/- ???? AI should serve humanity—not control it. -/- What Happens If We Don’t Act? -/- If we fail to implement Hybrid Leadership, we risk: ❌ AI being misused for political power. ❌ AI replacing human decision-making without ethical oversight. ❌ Governments continuing to make poor, biased decisions based on misinformation. -/- We must act now to integrate AI into leadership responsibly—before it’s too late. -/- Global Adoption – The Next Steps -/- ???? To ensure Hybrid Leadership becomes reality, we must: ???? Engage global organizations like the UN & WEF to test AI-assisted governance. ???? Educate future leaders on AI-assisted decision-making. ???? Promote ethical AI development, ensuring AI aligns with the Universal Formula. -/- ???? The time for action is now. -/- Conclusion: The Future of Leadership is Hybrid -/- ✅ Human ethics + AI precision = Balanced governance ✅ The Universal Law of Balance prevents leadership failures ✅ AI should assist leaders, not replace them ✅ Global institutions must adopt this model before AI is misused -/- ???? The future is balanced leadership – and it starts today. -/- . (shrink)

By introducing the concept of “mental data,” Palermos (2023) highlights an underappreciated aspect of data ethics that policymakers would do well to heed. Sweeping artificial intelligence (AI) legi...

Picture a simple scenario: a worm, in its modest way, traces a trail of paint as it moves across a sheet of paper. Now shift your imagination to a more complex scene, where a chimpanzee paints on another sheet of paper. A simple question arises: Do you perceive an identical creative process in these two animals? Can both of these animals be designated as authors of their creation? If only one, which one? This paper delves into the complexities of authorship, (...) consciousness, and agency, unpacking the nuanced distinctions between such scenarios and exploring the underlying principles that define creative authorship across different forms of life. It becomes evident that attributing authorship to an animal hinges on its intention to create, an aspect intertwined with its agency and awareness of the creative act. These concepts are far from straightforward, as they traverse the complex landscapes of animal ethics and law. But our exploration does not stop there. Now imagine a robot, endowed with artificial intelligence, producing music. This prompts us to question how we should evaluate and perceive such creations. Is the creative process of a machine fundamentally different from that of an animal or a human? As we venture further into this realm of human-made intelligence, we confront an array of ethical, philosophical, and legal quandaries. This paper provides a platform for a reflective discussion: ethologists, neuroscientists, philosophers, and bioinformaticians converge in a multidisciplinary dialogue. Their insights provide valuable perspectives for establishing a foundation upon which to discuss the intricate concepts of authorship and appropriation concerning artistic works generated by non-human entities. (shrink)

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, process, (...) and implications). We analyse this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasise or prioritise some over others depending on the proactive or reactive use of accountability and the missions of AI governance. (shrink)

-/- The End of Crime Syndicates: How the Universal Formula Will Reshape Society -/- Introduction -/- Crime syndicates have existed throughout human history, thriving on corruption, ignorance, and economic imbalance. Despite law enforcement efforts, they continue to evolve and adapt, making them seemingly indestructible. However, with the full implementation of the universal formula, the mechanisms that sustain crime syndicates will be dismantled. The universal formula, as the most powerful tool in mind programming, has the potential to eradicate criminal organizations by (...) reprogramming society to function in perfect alignment with natural laws. This essay explores how the universal formula will systematically eliminate crime syndicates and create a self-correcting, balanced civilization. -/- The Root Causes of Crime Syndicates -/- Before understanding how crime syndicates will disappear, it is essential to recognize why they exist: -/- 1. Ignorance and False Beliefs – Many people engage in crime because they lack a proper understanding of ethics, justice, and balance. Misinformation, emotional manipulation, and a lack of critical thinking allow crime to flourish. -/- 2. Corruption in Leadership – Governments, law enforcement, and businesses often protect crime syndicates due to bribery, personal interests, or threats. When leaders operate without a guiding universal principle, they become vulnerable to corruption. -/- 3. Economic Imbalances – Poverty, lack of opportunity, and wealth inequality push individuals into criminal activities. Crime becomes a survival mechanism in a world that does not provide balanced opportunities. -/- 4. Social Acceptance and Demand – Society plays a role in sustaining crime by consuming illegal goods and services, whether drugs, human trafficking, or underground markets. The demand fuels the supply. -/- 5. Lack of a Self-Correcting System – Current systems of justice react after crime has been committed instead of preventing it at its root cause. There is no universal guiding principle that ensures balance in all aspects of decision-making. -/- How the Universal Formula Will Eliminate Crime Syndicates -/- The universal formula will solve these problems by reprogramming the way society functions at every level. -/- 1. Mind Reprogramming Through Education -/- The most powerful transformation will happen through education. The universal formula will be implemented in basic education worldwide, ensuring that all individuals develop a deep understanding of balance, natural laws, and ethical decision-making. This will: -/- Remove ignorance and false beliefs, making it impossible for individuals to justify crime. -/- Train people in critical thinking, making them immune to manipulation and deception by crime syndicates. -/- Reprogram individuals to see justice and balance as the only rational way to operate in society. -/- As a result, future generations will reject crime as a viable option because their minds will be structured to seek equilibrium in all aspects of life. -/- 2. Corruption Becomes Impossible in Governance -/- With the universal formula guiding leadership training, all government officials, law enforcement officers, and judges will be required to follow natural laws of balance. This will make: -/- Bribery and extortion impossible, as leaders will be programmed to always seek universal balance. -/- Laws and policies aligned with natural laws, ensuring that no loopholes exist for crime syndicates to exploit. -/- Justice systems proactive, not reactive, eliminating crime before it takes root. -/- Without political and legal protection, crime syndicates will lose their power and collapse. -/- 3. Economic Balance Will Remove the Need for Crime -/- One of the biggest reasons crime syndicates exist is economic inequality. The universal formula will: -/- Ensure fair economic distribution, so individuals no longer need to turn to crime for survival. -/- Regulate business and trade using natural balance principles, eliminating black markets. -/- Remove the root cause of wealth-based crime, such as extortion and money laundering. -/- By balancing wealth, opportunity, and resources, crime will become economically illogical and unnecessary. -/- 4. No Demand for Criminal Markets -/- The universal formula will also remove the demand for illegal goods and services by: -/- Reprogramming individuals to reject harmful behaviors such as drug use and human exploitation. -/- Creating a society where needs are met ethically, removing the desire for black market solutions. -/- Using feedback mechanisms to detect and eliminate imbalances before they grow into full-scale criminal operations. -/- Without customers, crime syndicates will have no business model, forcing them into extinction. -/- 5. Self-Correcting Societies and AI Enforcement -/- A society governed by the universal formula will have a built-in self-correcting mechanism. Since all human decision-making follows natural balance, individuals and communities will: -/- Detect and eliminate imbalances before they lead to organized crime. -/- Hold each other accountable through collective awareness. -/- Use AGI technology programmed with the universal formula to analyze all societal systems, ensuring that no criminal organizations can form or sustain themselves. -/- With AI acting as an analytical enforcer, crime syndicates will have nowhere to hide, and any criminal attempts will be immediately corrected. -/- Final Outcome: A World Without Crime Syndicates -/- Once the universal formula is fully implemented, crime syndicates will no longer be sustainable. They rely on ignorance, corruption, economic imbalances, and public demand—all of which will be eliminated under a balanced system of natural laws. -/- 1. Future generations will be programmed for ethical and rational decision-making. -/- 2. Governments will be incorruptible and operate based on natural balance. -/- 3. Economic structures will remove incentives for crime. -/- 4. Society will self-regulate against criminal behavior. -/- 5. AI will ensure that all systems remain in universal balance, preventing crime from emerging. -/- In this world, crime syndicates will have no space to exist, making them extinct. -/- Conclusion -/- Crime syndicates have survived for centuries by exploiting human ignorance, economic instability, and corruption. However, with the universal formula, these root causes will be eliminated, leading to a fully balanced society. Education, governance, economy, and technology will all operate under natural laws, ensuring that crime syndicates can never return. -/- The universal formula is the most powerful tool in mind programming, and once implemented, it will create a world where crime is not just discouraged—but fundamentally impossible. -/- . (shrink)

This research focuses on the development of a Chatbot designed to respond to text queries related to various acts, rules, and regulations using Generative AI (Gen AI). The primary aim of the project was to create a user-friendly, intelligent system capable of answering legal and regulatory questions without relying on predefined datasets or natural language processing techniques. Instead, the Chatbot generates responses dynamically by leveraging the capabilities of Gen AI, which allows it to handle a wide range of user queries (...) on legal topics. The Chatbot's design is centered around understanding user inputs and formulating responses based on the knowledge it has been programmed with, rather than extracting information from pre-existing databases. The key outcome of the project is a highly adaptive chatbot that can provide relevant and accurate information on legal matters in real-time. The results highlight the system’s effectiveness in simplifying the process of accessing legal information, reducing the need for manual searches through acts, rules, and regulations. This project demonstrates the potential for AI-driven tools to improve accessibility to legal knowledge in a variety of sectors, including public services, law firms, and regulatory institutions. (shrink)