This paper offers a general theory of privacy, a theory that takes privacy to consist in being free from certain kinds of intrusions. On this understanding, privacy interests are distinct and distinguishable from those in solitude, anonymity, and property, for example, or from the fact that others possess, with neither consent nor permission, personal information about oneself. Privacy intrusions have both epistemic and psychological components, and can range in value from relatively trivial considerations to those of profound consequence for an (...) individual’s dignity, integrity, and autonomy. Thus while the focus of this theory is privacy per se— privacy as being free from certain kinds of intrusions—it has significant implications, discussed briefly, for what properly counts as the content of moral or legal rights to privacy. (shrink)

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. (...) By leveraging advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. (shrink)

Nowadays, intrusions have become a major problem faced by users. To stop these cyber attacks from happening, the development of a reliable and effective Intrusion Detection System (IDS) for cyber security has become an urgent issue to be solved. The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train (...) and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup 99) dataset has been used to train and validate. For validation, comparison for 2 dataset (CICIDS2017 and KDD Cup 99) is done. Then, to implement the Deep learning algorithms is proposed. Method Classification using SVM algorithm Model predict is done. Testing dataset for anomaly detection model classified as attack or normal. Finally, the experimental results shows that the performance metrics such as accuracy, precision, recall and confusion matrix. (shrink)

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead. (...) By leveraging advanced optimization techniques such as Grid Search and Particle Swarm Optimization (PSO), the proposed SVM-based IDS achieves superior classification results. (shrink)

With the growing sophistication and frequency of cyberattacks, there is a critical need for effective systems that can detect and prevent breaches in real time. The AI/ML-based Network Intrusion Detection System (NIDS) addresses this need by analyzing traffic patterns to identify security breaches in firewalls, routers, and network infrastructures. By integrating machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest—the system is able to detect both known cyber threats and previously unseen attack vectors. Unlike traditional methods (...) that rely heavily on predefined indicators of compromise (IoCs), this system utilizes anomaly detection techniques, allowing it to identify new and emerging threats. As cyberattacks evolve, organizations must adopt adaptive methods to secure their networks. This system achieves high accuracy in classifying network traffic, with real-time alerts that provide early warnings of suspicious activities. It also includes intuitive visualizations, helping network administrators gain insights into the nature and scope of attacks. With the rise of increasingly complex and frequent cyberattacks, this NIDS offers a robust solution for enhancing network security and response capabilities. (shrink)

The rapid proliferation of interconnected devices and the increasing complexity of digital networks in the modern era have resulted in a surge of diverse and voluminous network traffic. This growth poses considerable challenges in effectively distinguishing between normal and malicious data flows. As cyber threats continue to evolve, traditional traffic classification methods struggle to keep pace with the dynamic and multifaceted security challenges of contemporary networks. In this context, ensuring robust network security has become an imperative. This research aims to (...) address these challenges by developing a machine learning-based approach to accurately classify and segregate malicious traffic from legitimate traffic, thereby strengthening overall network defence mechanisms. (shrink)

The claim that common sense regards free will and moral responsibility as compatible with determinism has played a central role in both analytic and experimental philosophy. In this paper, we show that evidence in favor of this “natural compatibilism” is undermined by the role that indeterministic metaphysical views play in how people construe deterministic scenarios. To demonstrate this, we re-examine two classic studies that have been used to support natural compatibilism. We find that although people give apparently compatibilist responses, this (...) is largely explained by the fact that people import an indeterministic metaphysics into deterministic scenarios when making judgments about freedom and responsibility. We conclude that judgments based on these scenarios are not reliable evidence for natural compatibilism. (shrink)

In this paper, I explore Bach’s idea (Bach, 2000) that null appositives, intended as expanded qua-clauses, can resolve the puzzles of belief reports. These puzzles are crucial in understanding the semantics and pragmatics of belief reports and are presented in a section. I propose that Bach’s strategy is not only a way of dealing with puzzles, but also an ideal way of dealing with belief reports. I argue that even simple unproblematic cases of belief reports are cases of pragmatic (...) class='Hi'>intrusion, involving null appositives, or to use the words of Bach, ‘qua-clauses’. The main difference between my pragmatic approach and the one by Salmon (1986) is that this author uses the notion of conversational implicature, whereas I use the notion of pragmatic intrusion and explicature. From my point of view, statements such as ‘‘John believes that Cicero is clever’’ and ‘‘John believes that Tully is clever’’ have got distinct truth-values. In other words, I claim that belief reports in the default case illuminate the hearer on the mental life of the believer, that includes specific modes of presentation of the referents talked about. Furthermore, while in the other pragmatic approaches, it is mysterious how a mode of presentation is assumed to be the main filter of the believer’s mental life, here I provide an explanatory account in terms of relevance, cognitive effects, and processing efforts. The most important part of the paper is devoted to showing that null appositives are required, in the case of belief reports, to explain certain anaphoric effects, which would otherwise be mysterious. My examples show that null appositives are not necessitated at logical form, but only at the level of the explicature, in line with the standard assumptions by Carston and Recanati on pragmatic intrusion. I develop a potentially useful analysis of belief reports by exploiting syntactic and semantic considerations on presuppositional clitics in Romance. (shrink)

In this article we examine obsessive compulsive disorder (OCD). We examine and reject two existing models of this disorder: the Dysfunctional Belief Model and the Inference‐Based Approach. Instead, we propose that the main distinctive characteristic of OCD is a hyperactive sub‐personal signal of being in error, experienced by the individual as uncertainty about his or her intentional actions (including mental actions). This signalling interacts with the anxiety sensitivities of the individual to trigger conscious checking processes, including speculations about possible harms. (...) We examine the implications of this model for the individual's capacity to control his or her thoughts. (shrink)

Bulk collection involves the mining of large data sets containing personal data, often for a security purpose. In 2013, Edward Snowden exposed large scale bulk collection on the part of the US National Security Agency as part of a secret counter-terrorism effort. This effort has mainly been criticised for its invasion of privacy. I argue that the right moral argument against it is not so much to do with intrusion, as ineffectiveness for its official purpose and the lack of (...) oversight by security cleared legislators. It also lends itself to government "domination" (in Pettit's sense) of those who are subject to it. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with (...) Signature-Based Methods. The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (shrink)

The proliferation of the Internet of Things (IoT) has transformed various industries by enabling smart environments and improving operational efficiencies. However, this expansion has introduced numerous security vulnerabilities, making IoT systems prime targets for cyberattacks. This paper proposes a machine learning-based intrusion detection framework tailored to the unique characteristics of IoT environments. The framework leverages feature engineering, advanced machine learning algorithms, and real-time anomaly detection to identify and mitigate security threats effectively. Experimental results demonstrate the efficacy of the proposed (...) approach in detecting diverse IoT-specific attacks, including denial-of-service (DoS), man-in-the-middle (MITM), and malware-based attacks. The Internet of Things (IoT) has revolutionized modern living by interconnecting devices and enabling seamless communication. However, the increasing reliance on IoT systems has exposed significant vulnerabilities, making them a prime target for security attacks. This paper proposes a machine learning-based intrusion detection framework to detect and mitigate security attacks in IoT environments. The framework integrates diverse machine learning algorithms to identify abnormal behavior and potential threats. Through comprehensive experiments and evaluations, this research demonstrates the efficacy of the proposed framework in terms of accuracy, scalability, and robustness. (shrink)

The proliferation of Internet of Things (IoT) devices has introduced a range of opportunities for enhanced connectivity, automation, and efficiency. However, the vast array of interconnected devices has also raised concerns regarding cybersecurity, particularly due to the limited resources and diverse nature of IoT devices. Intrusion detection systems (IDS) have emerged as critical tools for identifying and mitigating security threats. This paper proposes a Multi-Layer Intrusion Detection Framework for IoT systems, leveraging Ensemble Machine Learning (EML) techniques to improve (...) the accuracy and effectiveness of detecting intrusions. The proposed framework integrates multiple layers of defense, including data preprocessing, feature extraction, classification, and post-processing, to provide a robust solution to IoT security challenges. This research explores the application of EML methods such as Random Forests, AdaBoost, and Gradient Boosting, combining them into a unified model to detect and mitigate various types of attacks in IoT environments. Experimental results demonstrate the superiority of the proposed framework in terms of detection accuracy, false positive rate, and computational efficiency. (shrink)

Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world applications. The aim of this study is to implement and compare the performances of each variant of the Bayesian classifier (Multinomial, Bernoulli, and Gaussian) on anomaly detection in network intrusion, and to investigate whether there is any association between each variant’s assumption and their performance. Our investigation showed that each variant of the Bayesian algorithm blindly follows (...) its assumption regardless of feature property, and that the assumption is the single most important factor that influences their accuracy. Experimental results show that Bernoulli has accuracy of 69.9% test (71% train), Multinomial has accuracy of 31.2% test (31.2% train), while Gaussian has an accuracy of 81.69% test (82.84% train). Going deeper, we investigated and found that each Naïve Bayes variants performances and accuracy is largely due to each classifier assumption, Gaussian classifier performed best on anomaly detection due to its assumption that features follow normal distributions which are continuous, while multinomial classifier have a dismal performance as it simply assumes discreet and multinomial distribution. (shrink)

—Industrial networks are increasingly based on open protocols and platforms that are also employed in the IT industry and Internet background. Most of the industries use wireless networks for communicating information and data, due to high cable cost. Since, the wireless networks are insecure, it is essential to secure the critical information and data during transmission. The data that transmitted is intercepted by eavesdropper can be predicted by secrecy capacity. The secrecy capacity is the difference between channel capacity of main (...) link and wiretap link of wireless transmission. When the secrecy capacity gets fades, then it is known that transmitted data is intercepted. In the event, of applying optimal sensor scheduling scheme a sensor with highest secrecy capacity is chosen and data is transmitted. It is followed with cooperative localization algorithm to enhance nodes, predict and maintain power of each sensor nodes. Through localization algorithm best sensor nodes are get predicted for transmission. Moreover, RC4 encryption and decryption algorithm is enhanced for data while transmission. This will enable protection to data to an extent. Also, an asymptotic intercept probability analysis is performed to provide an imminent into the impact of the sensor scheduling on the wireless security. (shrink)

I articulate and defend a principle governing enforcement rights in response to a non‐culpable non‐just rights‐intrusion (e.g., wrongful bodily attack by someone who falsely, but with full epistemic justification, believes that he is acting permissibly). The account requires that the use of force reduce the harm from such intrusions and is sensitive to the extent to which the intruder is agent‐responsible for imposing intrusion‐harm.

The ever-evolving landscape of cyber threats necessitates robust and adaptable intrusion detection systems (IDS) capable of identifying both known and emerging attacks. Traditional IDS models often struggle with detecting novel threats, leading to significant security vulnerabilities. This paper proposes an optimized intrusion detection model using Support Vector Machine (SVM) algorithms tailored to detect known and innovative cyberattacks with high accuracy and efficiency. The model integrates feature selection and dimensionality reduction techniques to enhance detection performance while reducing computational overhead.

Cloud computing has transformed data management by providing scalable and on-demand services, but its open and shared infrastructure makes it highly vulnerable to sophisticated cyber threats. Traditional Intrusion Detection Systems (IDS) struggle with dynamic and large-scale cloud environments due to high false positives, limited adaptability, and computational overhead. To address these challenges, this paper proposes an AI-driven Intrusion Detection System (AI-IDS) that leverages deep learning models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze (...) network traffic, detect anomalies, and identify advanced cyber threats with high accuracy. Unlike rule-based IDS, which rely on static signatures, our model continuously learns from real-time data, improving detection rates while reducing false alarms. The proposed AI-IDS is optimized for cloud infrastructure, ensuring low-latency threat detection, minimal computational burden, and real-time adaptability to emerging attack patterns. Experimental validation on benchmark datasets demonstrates significant improvements in accuracy, precision, and scalability, making AI-powered IDS a crucial innovation in cloud security. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with (...) Signature-Based Methods. The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (shrink)

attributive/referential. Pragmatic intrusion.

The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup (...) 99) dataset has been used to train and validate. For validation, comparison for 2 dataset (CICIDS2017 and KDD Cup 99) is done. Then, to implement the Deep learning algorithms is proposed. Method Classification using SVM algorithm Model predict is done. (shrink)

Nowadays, intrusions have become a major problem faced by users. To stop these cyber attacks from happening, the development of a reliable and effective Intrusion Detection System (IDS) for cyber security has become an urgent issue to be solved. The proposed IDS model is aimed at detecting network intrusions by classifying all the packet traffic in the network as benign or malicious classes. The Canadian Institute for Cyber security Intrusion Detection System (CICIDS2017) dataset has been used to train (...) and validate the proposed model. The model has been evaluated in terms of the overall accuracy, attack detection rate, false alarm rate, and training overhead. DDOS attacks based on Canadian Institute for Cyber security Intrusion Detection System (KDD Cup 99) dataset has been used to train and validate. For validation, comparison for 2 dataset (CICIDS2017 and KDD Cup 99) is done. (shrink)

Loci of pragmatic intrusion signal the tension between semantics and pragmatics. -/- .

The increasing sophistication of cyber threats necessitates innovative and proactive cybersecurity measures. This paper explores the latest trends in cybersecurity, focusing on the role of Artificial Intelligence (AI), Zero Trust security, and blockchain technology. A review of the literature highlights significant advancements and persistent challenges, including the security of Internet of Things (IoT) ecosystems and human-centric vulnerabilities. Experiments were conducted to evaluate the efficacy of machine learning-based intrusion detection systems and Zero Trust implementation in a simulated environment. Results demonstrate (...) improved detection rates and reduced insider threat risks. This paper concludes by emphasizing future directions, such as quantum-resistant cryptography and collaborative cybersecurity frameworks, to secure the digital future. (shrink)

The aggressive intrusion of science, technology and technology into the space of children as the least protected social group requires their protection from aggressive anthropologically-oriented practices. However, regularly conducted theoretical discussions of this problem do not lead to the solution of the problem of the ecology of childhood, since only one of the possible forms of understanding reality has been fixed within the framework of the international humanitarian and scientific community. We are talking about conceptual reasoning thinking, which is (...) based on classification concepts and provides for a pluralism of opinions, the multitude of which does not lead to unambiguous scientific humanitarian knowledge for everyone. The created conditions do not contribute to the emergence of an interdisciplinary research methodology, the formation of which is associated with the need for a fundamentally different way of thinking – thinking in comparative terms, which, unlike the natural sciences, is absent today in humanitarian knowledge. Whereas the new thinking proposed in this article, consisting in the ability to identify the different and distinguish the identical (one) within the natural range of comparative concepts, on the contrary, is a tool for integrating knowledge, therefore, in my opinion, it should be introduced into the sphere of traditional school education. In this connection, the main academic subjects will be reorganized around each of the comparative concepts and constructed in a certain order. As a result, students – schoolchildren or students - will learn to see the same content behind different physical or social phenomena, so the problem of memorizing large volumes of educational material disappears. The new approach will accelerate the process of formation of an interdisciplinary methodology of socio-cultural analysis, will contribute to the implementation of a comprehensive state policy, will give a new impetus to socio-cultural and anthropo-pedagogical practice. (shrink)

I propose some rules that regiment substitutions of NPs.

This paper aims to demonstrate the contemporary relevance of Adorno’s thought for business ethicists working in the critical tradition by showing how his critique of modern social life anticipated, and offers continuing illumination of, recent technological transformations of capitalism. It develops and extrapolates Adorno’s thought regarding three central spheres of modern society, which have seen radical changes in light of recent technological developments: work, in which employee monitoring has become ever more sophisticated and intrusive; leisure consumption, in which the algorithmic (...) developments of the culture industry have paved the way for entertainment products to dominate us; and political discourse, in which social media has exacerbated the anti-democratic tendencies Adorno warned of in the mid-twentieth century. We conclude by presenting, as a rejoinder to these developments, the contours of an Adornian ethics of resistance to the reification and dehumanisation of such developments. (shrink)

In her BBC Reith Lectures on Trust, Onora O’Neill offers a short, but biting, criticism of transparency. People think that trust and transparency go together but in reality, says O'Neill, they are deeply opposed. Transparency forces people to conceal their actual reasons for action and invent different ones for public consumption. Transparency forces deception. I work out the details of her argument and worsen her conclusion. I focus on public transparency – that is, transparency to the public over expert domains. (...) I offer two versions of the criticism. First, the epistemic intrusion argument: The drive to transparency forces experts to explain their reasoning to non-experts. But expert reasons are, by their nature, often inaccessible to non-experts. So the demand for transparency can pressure experts to act only in those ways for which they can offer public justification. Second, the intimate reasons argument: In many cases of practical deliberation, the relevant reasons are intimate to a community and not easily explicable to those who lack a particular shared background. The demand for transparency, then, pressures community members to abandon the special understanding and sensitivity that arises from their particular experiences. Transparency, it turns out, is a form of surveillance. By forcing reasoning into the explicit and public sphere, transparency roots out corruption — but it also inhibits the full application of expert skill, sensitivity, and subtle shared understandings. The difficulty here arises from the basic fact that human knowledge vastly outstrips any individual’s capacities. We all depend on experts, which makes us vulnerable to their biases and corruption. But if we try to wholly secure our trust — if we leash groups of experts to pursuing only the goals and taking only the actions that can be justified to the non-expert public — then we will undermine their expertise. We need both trust and transparency, but they are in essential tension. This is a deep practical dilemma; it admits of no neat resolution, but only painful compromise. (shrink)

As the science and technology of the brain and mind develop, so do the ways in which brains and minds may be surveilled and manipulated. Some cognitive libertarians worry that these developments undermine cognitive liberty, or “freedom of thought.” I argue that protecting an individual’s cognitive liberty undermines others’ ability to use their own cognitive liberty. Given that the threatening devices and processes are not relevantly different from ordinary and frequent intrusions upon one’s brain and mind, strong protections of cognitive (...) liberty may proscribe neurotechnological intrusions, but also ordinary intrusions. Thus, the cognitive libertarian position “hog-ties” others’ use of their own liberties. This problem for the cognitive libertarian is the same problem that ordinary libertarianism faces in protecting individual rights to property and person. But the libertarian strategies for resolving the problem don’t work for the cognitive libertarian. I conclude that the right to mental privacy is weaker than what cognitive libertarians want it to be. (shrink)

Why is it more violating to grab a stranger’s thigh or stroke their face than it is to grab their forearm? Why is it worse to read someone’s dream journal without permission than it is to read their bird watching field notes? Why are gestation mandates so incredibly intrusive? Intimacy is key to understanding these cases, and to explaining many of our most stringent rights. -/- I present two ways of thinking about intimacy, Relationship-First Accounts and the Intimate Zones Account. (...) I argue that only the Intimate Zones Account lets us cohesively understand intimacy’s importance, and the scope of our intimate rights. I characterize our intimate zones as meeting the Hiddenness and Importance Conditions. A feature’s meeting these conditions makes it a locus of special vulnerability by which our persons can be fundamentally altered. This special vulnerability explains why our duties to respect the intimate boundaries of others are so strict. (shrink)

In this study, we conducted large-scale experiments with novel descriptions of determinism. Our goal was to investigate the effects of desires for punishment and comprehension errors on people’s intuitions about free will and moral responsibility in deterministic scenarios. Previous research has acknowledged the influence of these factors, but their total effect has not been revealed. Using a large-scale survey of Japanese participants, we found that the failure to understand causal determination (intrusion) has limited effects relative to other factors and (...) that the conflation of determinism and epiphenomenalism (bypassing) has a significant influence, even when controlling for other variables. This leads to the increased prevalence of incompatibilist responses. Furthermore, our results demonstrated a close association between the attribution of free will/responsibility and retributive desire. While further research is needed to establish the causal relationship between these factors, this association is consistent with Cory Clark and colleagues' study that increased desire contributes to increased compatibilist responses and their claim that a definitive intuition about free will may be elusive. (shrink)

According to a chorus of authors, the human life-world is currently invaded by an avalanche of high-tech devices referred to as “emerging,” ”intimate,” or ”NBIC” technologies: a new type of contrivances or gadgets designed to optimize cognitive or sensory performance and / or to enable mood management. Rather than manipulating objects in the outside world, they are designed to influence human bodies and brains more directly, and on a molecular scale. In this paper, these devices will be framed as ‘extimate’ (...) technologies (both intimate and external; both embedded and foreign; both life-enhancing and intrusive), a concept borrowed from Jacques Lacan. Although Lacan is not commonly regarded as a philosopher of technology, the dialectical relationship between human desire and technological artefacts runs as an important thread through his work. Moreover, he was remarkably prescient concerning the blending of life science and computer science, which is such a distinctive feature of the current techno-scientific turn. Building on a series of Lacanian concepts, my aim is to develop a psychoanalytical diagnostic of the technological present. Finally, I will indicate how such an analysis may inform our understanding of human life and embodiment as such. (shrink)

Theories of free will are often measured against how well they capture everyday intuitions about free will. But what are these everyday intuitions, and what theoretical commitments do they express? Empirical methods have delivered mixed messages. In response, some free will theorists have developed error theories to undermine the credentials of countervailing intuitions. These efforts are predicated on the idea that people might misunderstand determinism in any of several ways. This paper sheds light on the comprehension problem. We first discuss (...) recent efforts to explain systematic errors in how people interpret determinism. Then, we present the alarming results of two new preregistered studies exploring three types of comprehension failure: (a) epiphenomenal bypassing, (b) fatalistic bypassing, and (c) indeterministic intrusion. Our findings suggest that misunderstanding runs deeper than others have supposed. This casts doubt on existing models of commonsense thinking about free will. Unless and until researchers properly control for the kinds of misunderstandings we identify, research on free will intuitions cannot shed light on whether ordinary thinking reflects commitments to compatibilism or incompatibilism. (shrink)

Vision is an important metaphor in ethical and political questions of knowledge. The feminist philosopher Donna Haraway points out the “perverse” nature of an intrusive, alienating, all-seeing vision (to which we might cry out “stop looking at me!”), but also encourages us to embrace the embodied nature of sight and its promises for genuinely situated knowledge. Current technologies of machine vision – surveillance cameras, drones (for war or recreation), iPhone cameras – are usually construed as instances of the former rather (...) than the latter, and for good reasons. However, although in no way attempting to diminish the real suffering these technologies have brought about in the world, I make the case for understanding technologies of computer vision as material instances of embodied seeing and situated knowing. Furthermore, borrowing from Iris Murdoch’s concept of moral vision, I suggest that these technologies direct our labor towards self-reflection in ethically significant ways. My approach draws upon paradigms in computer vision research, phenomenology, and feminist epistemology. Ultimately, this essay is an argument for directing more philosophical attention from merely criticizing technologies of vision as ethically deficient towards embracing them as complex, methodologically and epistemologically important objects. (shrink)

This article addresses the question of whether an expectation of privacy is reasonable in the face of new technologies of surveillance, by developing a principle that best fits our intuitions. A "no sense enhancement" principle which would rule out searches using technologically sophisticated devices is rejected. The paper instead argues for the "mischance principle," which proscribes uses of technology that reveal what could not plausibly be discovered accidentally without the technology, subject to the proviso that searches that serve a great (...) public good that clearly outweighs minimal intrusions upon privacy are permissible. Justifications of the principle are discussed, including reasons why we should use the principle and not rely solely on a utilitarian balancing test. The principle is applied to uses of aerial photography and heat-detection devices. (shrink)

We often remember in the company of others. In particular, we routinely collaborate with friends, family, or colleagues to remember shared experiences. But surprisingly, in the experimental collaborative recall paradigm, collaborative groups remember less than their potential, an effect termed collaborative inhibition. Rajaram and Pereira-Pasarin (2010) argued that the effects of collaboration on recall are determined by “pre-collaborative” factors. We studied the role of 2 pre-collaborative factors—shared encoding and group relationship—in determining the costs and benefits of collaborative recall. In Experiment (...) 1, we compared groups of strangers who encoded alone versus together, before collaborating to recall. In Experiment 2, we compared groups of friends who encoded alone versus together, before collaborating to recall. We found that shared encoding abolished collaborative inhibition in both Experiments 1 and 2. But prior relationship did not influence collaborative inhibition over and above the effects of shared encoding. Regardless of encoding condition, collaborative group recall contained fewer intrusions than nominal group recall, and these benefits continued in subsequent individual recall. Our findings demonstrate that pre-collaborative factors—specifically shared encoding—have flow-on benefits for group and individual recall amount, but not recall accuracy. We discuss these findings in terms of self- and cross-cuing in collaborative recall. (shrink)

Crime-preventing neurointerventions (CPNs) are increasingly being used or advocated for crime prevention. There is increasing use of testosterone-lowering agents to prevent recidivism in sexual offenders, and strong political and scientific interest in developing pharmaceutical treatments for psychopathy and anti-social behaviour. Recent developments suggest that we may ultimately have at our disposal a range of drugs capable of suppressing violent aggression, and it is not difficult to imagine possible applications of such drugs in crime prevention. But should neurointerventions be used in (...) crime prevention, and may the state ever permissibly impose CPNs as part of the criminal justice process? It is widely thought that preventing recidivism is one of the aims of criminal justice, yet existing means of pursuing this aim are often poorly effective, restrictive of basic freedoms, and harmful. Incarceration, for example, tends to be disruptive of personal relationships and careers, detrimental to physical and mental health, highly restrictive of freedom of movement and association, and rarely more than modestly effective at preventing recidivism. Neurointerventions hold the promise of preventing recidivism in ways that are more effective and more humane, but the use of CPNs in criminal justice raises several ethical concerns. CPNs could be highly intrusive and may threaten fundamental human values, such as bodily integrity and freedom of thought, and humanity has a track record of misguided, harmful, and unwarrantedly coercive use of neurotechnological ‘solutions’ to criminality. This collection brings together original contributions from emerging scholars and internationally renowned moral and political philosophers to address these issues. (shrink)

This paper considers issues raised by Elizabeth Anderson’s recent critique of the position she terms ‘luck egalitarianism’. It is maintained that luck egalitarianism, once clarified and elaborated in certain regards, remains the strongest egalitarian stance. Anderson’s arguments that luck egalitarians abandon both the negligent and prudent dependent caretakers fails to account for the moderate positions open to luck egalitarians and overemphasizes their commitment to unregulated market choices. The claim that luck egalitarianism insults citizens by redistributing on the grounds of paternalistic (...) beliefs, pity and envy, and by making intrusive and stigmatizing judgments of responsibility, fails accurately to characterize the luck egalitarian’s rationale for redistribution and relies upon luck egalitarians being insensitive to the danger of stigmatization. The luck egalitarian position is reinforced by the fact that Anderson’s favoured conception of equality, ‘democratic equality’, is counterintuitively indifferent to all unchosen inequalities, including intergenerational inequalities, once bare social minima are met. (shrink)

The rapid proliferation of Internet of Things (IoT) devices in smart cities has introduced numerous benefits, enhancing urban efficiency, sustainability, and automation. However, these interconnected systems also pose significant cybersecurity challenges, including data breaches, unauthorized access, and cyberattacks that can compromise critical infrastructure. This paper explores various cybersecurity strategies tailored for IoT environments in smart cities, focusing on encryption techniques, secure authentication mechanisms, network security protocols, and blockchain-based security models. Additionally, it discusses machine learningbased anomaly detection systems to identify potential (...) cyber threats in real time. By analyzing existing security frameworks and emerging solutions, this study provides a comprehensive approach to safeguarding IoT devices in smart urban ecosystems. The findings emphasize the need for a multi-layered security model and proactive threat mitigation strategies to ensure the resilience and integrity of smart city infrastructures. (shrink)

Several theories propose that one of the core functions of inner speech (IS) is to support subjects in the completion of cognitively effortful tasks, especially those involving executive functions (EF). In this paper we focus on two populations who notoriously encounter difficulties in performing EF tasks, namely, people diagnosed with schizophrenia who experience auditory verbal hallucinations (Sz-AVH) and people within the Autism Spectrum Conditions (ASC). We focus on these two populations because they represent two different ways in which IS can (...) fail to help in EF tasks, which can be illustrative for other mental conditions. First, we review the main components of EF (Section 1). Then we explain the functions that IS is taken to perform in the domain of EF (Section 2) and review the evidence concerning problems about EF in the two populations of our study: Sz-AVH (Section 3) and ASC (Section 4). After this we further detail our account about what a properly functioning IS can do for both populations and how different IS profiles may impact EF performance: in the case of Sz-AVH, the uncontrolled and intrusive character of IS negatively affects EF performance, whereas in ASC, EF is not sufficiently supported by IS, given the tendency in this population to present a diminished use of IS (Section 5). We finally briefly discuss Attention Deficit/Hyperactivity Disorder (ADHD) and Developmental Language Disorders (DLD). (shrink)

Drink driving causes great suffering and material destruction. The alcohol interlock promises to eradicate this problem by technological design. Traditional counter-measures to drink driving such as policing and punishment and information campaigns have proven insufficient. Extensive policing is expensive and intrusive. Severe punishment is disproportionate to the risks created in most single cases. If the interlock becomes inexpensive and convenient enough, and if there are no convincing moral objections to the device, it may prove the only feasible as well as (...) the only justifiable solution to the problem of drink driving. A policy of universal alcohol interlocks, in all cars, has been proposed by several political parties in Sweden and is supported by the National Road Administration and the 2006 Alcohol Interlock Commission. This article assesses two possible moral objections to a policy of universal interlocks: (i) that it displaces the responsibility of individual drivers and (ii) that it constitutes a paternalistic interference with drivers. The first objection is found unconvincing, while the second has only limited bite and may be neutralized if paternalism is accepted for the sake of greater net liberty. Given the expected technological development, the proposed policy seems a commendable health promotion measure for the near future. (shrink)

The development of symbolic logic is often presented in terms of a cumulative story of consecutive innovations that led to what is known as modern logic. This narrative hides the difficulties that this new logic faced at first, which shaped its history. Indeed, negative reactions to the emergence of the new logic in the second half of the nineteenth century were numerous and we study here one case, namely logic at Oxford, where one finds Lewis Carroll, a mathematical teacher who (...) promoted symbolic logic, and John Cook Wilson, the Wykeham Professor of Logic who notoriously opposed it. An analysis of their disputes on the topic of logical symbolism shows that their opposition was not as sharp as it might look at first, as Cook Wilson was not so much opposed to the « symbolic » character of logic, but the intrusion of mathematics and what he perceived to be the futility of some of its problems, for logicians and philosophers alike. (shrink)

The scarcity of livers available for transplants forces tough choices upon us. Lives for those not receiving a transplant are likely to be short. One large group of potential recipients needs a new liver because of alcohol consumption, while others suffer for reasons unrelated to their own behaviour. Should the former group receive lower priority when scarce livers are allocated? This discussion connects with one of the most pertinent issues in contemporary political philosophy; the role of personal responsibility in distributive (...) justice. One prominent theory of distributive justice, luck egalitarianism, assesses distributions as just if, and only if, people's relative positions reflect their exercises of responsibility. There is a principled luck egalitarian case for giving lower priority to those who are responsible for their need. Compared to the existing literature favouring such differentiation, luck egalitarianism provides a clearer rationale of fairness, acknowledges the need for individual assessments of responsibility, and requires initiatives both inside and outside of the allocation systems aimed at mitigating the influence from social circumstances. Furthermore, the concrete policies that luck egalitarians can recommend are neither too harsh on those who make imprudent choices nor excessively intrusive towards those whose exercises of responsibility are assessed. (shrink)

Much of the characteristic symptomatology of schizophrenia can be understood as resulting from a pervasive sense of disembodiment. The body is experienced as an external machine that needs to be controlled with explicit intentional commands, which in turn leads to severe difficulties in interacting with the world in a fluid and intuitive manner. In consequence, there is a characteristic dissociality: Others become problems to be solved by intellectual effort and no longer present opportunities for spontaneous interpersonal alignment. This dissociality goes (...) hand in hand with a progressive loss of the socially extended mind, which normally affords opportunities for co-regulation of cognitive and affective processes. However, at times people with schizophrenia report that they are confronted by the opposite of this dissociality, namely an unusual fluidity of the self-other boundary as expressed in experiences of ambiguous body boundaries, intrusions, and even merging with others. Here the person has not lost access to the socially extended mind but has instead become lost in it, possibly due to a weakened sense of self. We argue that this neglected aspect of schizophrenic social dysfunction can be usefully approached via the concept of genuine intersubjectivity: We normally participate in a shared experience with another person by implicitly co-regulating how our interaction unfolds. This co-regulation integrates our respective experience’s dynamical bases into one interpersonal process and gives the interaction an ambiguous second-person character. The upshot is that reports of abnormal self-other fluidity are not indicative of hallucinations without any basis in reality, but of a heightened sensitivity and vulnerability to processes of interpersonal alignment and mutual incorporation that form the normal basis of social life. We conclude by discussing implications of this view for both the science of consciousness as well as approaches to intervention and therapy. (shrink)

In a recent article in Respublica, Jesper Ryberg argues that CCTV can be compared to a little old lady gazing out onto the street below. This article takes issue with the claim that government surveillance can be justified in this manner. Governments have powers and responsibilities that little old ladies lack. Even if CCTV is effective at preventing crime, there may be less intrusive ways of doing so. People have a variety of legitimate interests in privacy, and protection for these (...) is important to their status as free and equal citizens. Consequently, though necessary, effectiveness is insufficient to justify CCTV in a democracy. (shrink)

This paper seeks to identify the distinctive moral wrong of stalking and argues that this wrong is serious enough to criminalize. We draw on psychological literature about stalking, distinguishing types of stalkers, their pathologies, and victims. The victimology is the basis for claims about what is wrong with stalking. Close attention to the experiences of victims often reveals an obsessive preoccupation with the stalker and what he will do next. The kind of harm this does is best understood in relation (...) to the value of privacy and conventionally protected zones of privacy. We compare anti-stalking laws in different jurisdictions, claiming that they all fail in some way to capture the distinctive privacy violation that stalking involves. Further reflection on the seriousness of the invasion of privacy it represents suggests that it is a deeply personal wrong. Indeed, it is usually more serious than obtrusive surveillance by states, precisely because it is more personal. Where state surveillance genuinely is as intrusive as stalking, it tends to adopt the tactics of the stalker, imposing its presence on the activist victim at every turn. Power dynamics —whether rooted in the power of the state or the violence of a stalker —may exacerbate violations of privacy, but the wrong is distinct from violence, threats of violence and other aggression. Nor is stalking a simple expression of a difference in power between stalker and victim, such as a difference due to gender. (shrink)