The recent progression in AI, nanomedicine and robotics have increased concerns about ethics, policy and law. The increasing complexity and hybrid nature of AI and nanotechnologies impact the functionality of “law in action” which can lead to legal uncertainty and ultimately to a public distrust. There is an immediate need of collaboration between Central Asian biomedical scientists, AI engineers and academic lawyers for the harmonization of AI, nanomedicines and robotics in Central Asian legal system.

The advent of Generative AI, particularly through Large Language Models (LLMs) like ChatGPT and its successors, marks a paradigm shift in the AI landscape. Advanced LLMs exhibit multimodality, handling diverse data formats, thereby broadening their application scope. However, the complexity and emergent autonomy of these models introduce challenges in predictability and legal compliance. This paper analyses the legal and regulatory implications of Generative AI and LLMs in the European Union context, focusing on 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)

This comprehensive volume features the proceedings of the Third International Workshop on Logics for New-Generation Artificial Intelligence and the International Workshop on Logic, AI and Law, held in Hangzhou, China on September 8-9 and 11-12, 2023. The collection offers a diverse range of papers that explore the intersection of logic, artificial intelligence, and law. With contributions from some of the leading experts in the field, this volume provides insights into the latest research and developments in the applications of logic in (...) these areas. It is an essential resource for researchers, practitioners, and students interested in the latest advancements in logic and its applications to artificial intelligence and law. (shrink)

AI companies are racing to create artificial general intelligence, or “AGI.” If they succeed, the result will be human-level AI systems that can independently pursue high-level goals by formulating and executing long-term plans in the real world. Leading AI researchers agree that some of these systems will likely be “misaligned”–pursuing goals that humans do not desire. This goal mismatch will put misaligned AIs and humans into strategic competition with one another. As with present-day strategic competition between nations with incompatible goals, (...) the result could be violent and catastrophic conflict. Existing legal institutions are unprepared for the AGI world. New foundations for AGI governance are needed, and the time to begin laying them is now, before the critical moment arrives. -/- This Article begins to lay those new legal foundations. It is the first to think systematically about the dynamics of strategic competition between humans and misaligned AGI. The Article begins by showing, using formal game-theoretic models, that, by default, humans and AIs will be trapped in a prisoner’s dilemma. Both parties’ dominant strategy will be to permanently disempower or destroy the other, even though the costs of such conflict would be high. -/- The Article then argues that a surprising legal intervention could transform the game theoretic equilibrium and avoid conflict: AI rights. Not just any AI rights would promote human safety. Granting AIs the right not to be needlessly harmed–as humans have granted to certain non-human animals–would, for example, have little effect. Instead, to promote human safety, AIs should be given those basic private law rights–to make contracts, hold property, and bring tort claims–that law already extends to non-human corporations. Granting AIs these economic rights would enable long-run, small-scale, mutually-beneficial transactions between humans and AIs. This would, we show, facilitate a peaceful strategic equilibrium between humans and AIs for the same reasons economic interdependence tends to promote peace in international relations. Namely, the gains from trade far exceed those from war. Throughout, we argue that human safety, rather than AI welfare, provides the right framework for developing AI rights. This Article explores both the promise and the limits of AI rights as a legal tool for promoting human safety in an AGI world. (shrink)

In an effort to inform the discussion surrounding existential risks from AI, we formulate Extinction-level Goodhart’s Law as “Virtually any goal specification, pursued to the extreme, will result in the extinction of humanity”, and we aim to understand which formal models are suitable for investigating this hypothesis. Note that we remain agnostic as to whether Extinction-level Goodhart’s Law holds or not. As our key contribution, we identify a set of conditions that are necessary for a model that aims to be (...) informative for evaluating specific arguments for Extinction-level Goodhart’s Law. Since each of the conditions seems to significantly contribute to the complexity of the resulting model, formally evaluating the hypothesis might be exceedingly difficult. This raises the possibility that whether the risk of extinction from artificial intelligence is real or not, the underlying dynamics might be invisible to current scientific methods. (shrink)

What does Cyberpunk and AI Ethics have to do with each other? Cyberpunk is a sub-genre of science fiction that explores the post-human relationships between human experience and technology. One similarity between AI Ethics and Cyberpunk literature is that both seek a dialogue in which the reader may inquire about the future and the ethical and social problems that our technological advance may bring upon society. In recent years, an increasing number of ethical matters involving AI have been pointed and (...) debated, and several ethical principles and guides have been suggested as governance policies for the tech industry. However, would this be the role of AI Ethics? To serve as a soft and ambiguous version of the law? I would like to promote in this article a more Cyberpunk way of doing AI Ethics, whit a more anarchic way of governance. In this study, I will seek to expose some of the deficits of the underlying power structures of our society, and suggest that AI governance be subject to public opinion, so that ‘good AI’ can become ‘good AI for all’. (shrink)

A philosophical linguistic exploration into the existence of not of AI ethics. Using Adorno's negative dialectics the author considers contemporary approaches to AI and Ethics, especially with regards to policy and law considerations. Looking at if these approaches are in fact speaking to our historical conception of AI and what the actual emergence of the latter could imply for future ethical concerns.

The law confers rights on non-human entities, namely nature, machines (AI), and animals. While doing so, the law is either viewed as progressive or sometimes as abstract and ambiguous. Despite the critique, it is undeniable that many of the rights of non-humans have come to solidify in statutory and constitutional rules of different systems. In the context of these developments, the article sheds light on the core justifications for advancing the rights of non-human entities. In addition, it discusses the conditions (...) for the emergence of these rights and the non-binding normative statements adopted by different stakeholders for advocacy. These include the Charter on the Law of the Living (2021), the Toulon Declaration (2019), and the Vienna Manifesto on Digital Humanism (2019), etc. The paper also discusses the relevant theoretical frames, namely post-humanism, digital humanism, and multi-species justice, followed by selective critical views on the subject. (shrink)

This paper presents the main topics, arguments, and positions in the philosophy of AI at present (excluding ethics). Apart from the basic concepts of intelligence and computation, the main topics of ar-tificial cognition are perception, action, meaning, rational choice, free will, consciousness, and normativity. Through a better understanding of these topics, the philosophy of AI contributes to our understand-ing of the nature, prospects, and value of AI. Furthermore, these topics can be understood more deeply through the discussion of AI; so (...) we suggest that “AI Philosophy” provides a new method for philosophy. (shrink)

Artificial Intelligence (AI) is already having a major impact on society. As a result, many organizations have launched a wide range of initiatives to establish ethical principles for the adoption of socially beneficial AI. Unfortunately, the sheer volume of proposed principles threatens to overwhelm and confuse. How might this problem of ‘principle proliferation’ be solved? In this paper, we report the results of a fine-grained analysis of several of the highest-profile sets of ethical principles for AI. We assess whether these (...) principles converge upon a set of agreed-upon principles, or diverge, with significant disagreement over what constitutes ‘ethical AI.’ Our analysis finds a high degree of overlap among the sets of principles we analyze. We then identify an overarching framework consisting of five core principles for ethical AI. Four of them are core principles commonly used in bioethics: beneficence, non-maleficence, autonomy, and justice. On the basis of our comparative analysis, we argue that a new principle is needed in addition: explicability, understood as incorporating both the epistemological sense of intelligibility (as an answer to the question ‘how does it work?’) and in the ethical sense of accountability (as an answer to the question: ‘who is responsible for the way it works?’). In the ensuing discussion, we note the limitations and assess the implications of this ethical framework for future efforts to create laws, rules, technical standards, and best practices for ethical AI in a wide range of contexts. (shrink)

Sora from Open AI has shown exceptional performance, yet it faces scrutiny over whether its technological prowess equates to an authentic comprehension of reality. Critics contend that it lacks a foundational grasp of the world, a deficiency V-JEPA from Meta aims to amend with its joint embedding approach. This debate is vital for steering the future direction of Artificial General Intelligence(AGI). We enrich this debate by developing a theory of productive imagination that generates a coherent world model based on Kantian (...) philosophy. We identify three indispensable components in the coherent world model that enable genuine world understanding: latent representations of isolated objects, an a priori law of change across space and time, and Kantian categories. Our analysis reveals that Sora is limited because of its oversight of the a priori law of change and Kantian categories, flaws that are not rectifiable through scaling up the training. V-JEPA learns the context-dependent aspect of the a priori law of change. Yet it fails to fully comprehend Kantian categories and incorporate experience, leading us to conclude that neither system currently achieves a comprehensive world understanding. Nevertheless, each system has developed components essential to advancing an integrated AI productive imagination-understanding engine. Finally, we propose an innovative training framework for an AI productive imagination-understanding engine, centered around a joint embedding system designed to transform disordered perceptual input into a structured, coherent world model. Our philosophical analysis pinpoints critical challenges within contemporary video AI technologies and a pathway toward achieving an AI system capable of genuine world understanding, such that it can be applied for reasoning and planning in the future. (shrink)

That AI will have a major impact on society is no longer in question. Current debate turns instead on how far this impact will be positive or negative, for whom, in which ways, in which places, and on what timescale. In order to frame these questions in a more substantive way, in this prolegomena we introduce what we consider the four core opportunities for society offered by the use of AI, four associated risks which could emerge from its overuse or (...) misuse, and the opportunity costs associated with its under use. We then offer a high-level view of the emerging advantages for organisations of taking an ethical approach to developing and deploying AI. Finally, we introduce a set of five principles which should guide the development and deployment of AI technologies. The development of laws, policies and best practices for seizing the opportunities and minimizing the risks posed by AI technologies would benefit from building on ethical frameworks such as the one offered here. (shrink)

Humans and artificial intelligences (AI) will increasingly participate digitally and physically in conflicts yet there is a lack of trusted communications across agents and platforms. For example, humans in disasters and conflict already use messaging and social media to share information, however, international humanitarian relief organisations treat this information as unverifiable and untrustworthy. AI may reduce the ‘fog-of-war’ and improve outcomes, however current AI implementations are often brittle, have a narrow scope of application and wide ethical risks. Meanwhile, human error (...) causes significant civilian harms even by combatants committed to complying with international humanitarian law. AI offers an opportunity to help reduce the tragedy of war and better deliver humanitarian aid to those who need it. However, to be successful, these systems must be trusted by humans and their information systems, overcoming flawed information flows in conflict and disaster zones that continue to be marked by intermittent communications, poor situation awareness, mistrust and human errors. In this paper, we consider the integration of a communications protocol (the ‘Whiteflag protocol’), distributed ledger technology, and information fusion with artificial intelligence (AI), to improve conflict communications called “Protected Assurance Understanding Situation & Entities” PAUSE. Such a trusted human-AI communication network could provide accountable information exchange regarding protected entities, critical infrastructure; humanitarian signals and status updates for humans and machines in conflicts. Trust-based information fusion provides resource-efficient use of diverse data sources to increase the reliability of reports. AI can be used to catch human mistakes and complement human decision making, while human judgment can direct and override AI recommendations. We examine several case studies for the integration of these technologies into a trusted human-AI network for humanitarian benefit including mapping a conflict zone with civilians and combatants in real time, preparation to avoid incidents and using the network to manage misinformation. (shrink)

Without doctors being able to explain medical decisions to patients, I argue their use of black box AIs would erode the effective and respectful care they provide patients. In addition, I argue that physicians should use AI black boxes only for patients in dire straits, or when physicians use AI as a “co-pilot” (analogous to a spellchecker) but can independently confirm its accuracy. I respond to A.J. London’s objection that physicians already prescribe some drugs without knowing why they work.

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing (...) should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation. (shrink)

The field of AI ethics during the current and previous decade is receiving an increasing amount of attention from all involved stakeholders: the public, science, philosophy, religious organizations, enterprises, governments, and various organizations. However, this field currently lacks consensus on scope, ethico-philosophical foundations, or common methodology. This thesis aims to contribute towards filling this gap by providing an answer to the two main research questions: first, what theory can explain moral scenarios in which AI entities are participants?; and second, what (...) theory can explain the process of moral reasoning, decision and action, for AI entities in virtual, simulated and real-life moral scenarios? This thesis answers these two research questions with its two main contributions to the field of AI ethics, a substantial and a methodological contribution. The substantial contribution is a coherent and novel theory named Ethics of Systems Framework, as well as a possible inception of a new field of study: ethics of systems. The methodological contribution is the creation of its main methodological tool, the Ethics of Systems Interface. The second part of the research effort was focused on testing and demonstrating the capacities of the Ethics of Systems Framework and Interface in modeling and managing moral scenarios in which AI and other entities participate. Further work can focus on building on top of the foundations of the Framework provided here, increasing the scope of moral theories and simulated scenarios, improving the level of detail and parameters to reflect real-life situations, and field-testing the Framework on actual AI systems. (shrink)

Law-enforcement agencies are increasingly able to leverage crime statistics to make risk predictions for particular individuals, employing a form of inference that some condemn as violating the right to be “treated as an individual.” I suggest that the right encodes agents’ entitlement to a fair distribution of the burdens and benefits of the rule of law. Rather than precluding statistical prediction, it requires that citizens be able to anticipate which variables will be used as predictors and act intentionally to avoid (...) them. Furthermore, it condemns reliance on various indexes of distributive injustice, or unchosen properties, as evidence of law-breaking. (shrink)

Techno-ethics is the area in the philosophy of technology which deals with emerging robotic and digital AI technologies. In the last decade, a new techno-ethical challenge has emerged: Autonomous Weapon Systems (AWS), defensive and offensive (the article deals only with the latter). Such AI-operated lethal machines of various forms (aerial, marine, continental) raise substantial ethical concerns. Interestingly, the topic of AWS was almost not treated in Jewish law and its research. This article thus proposes an introductory ethical-halakhic perspective on AWS, (...) in the Israeli context. The article has seven sections. Section 1 defines AWS and the main ethical concerns it evokes, while providing elementary definitions and distinctions. §2 locates AWS within the realm of Jewish laws of war (hilkhot-ẓava), which recognize the right for self-defense, as well as the status of universally accepted moral norms. §3 unfolds pragmatic ethical premises of a humane techno-ethics, which are required for the identifying AWS as a moral question: I. Relationality; II. Technology is not (completely) neutral; III. The fallaciousness of transhumanism. It is argued that these premises are compatible with halakhic tradition. §4 investigates the question of the morality of AWS, within the field of military AI ethics. It is clarified why the standard categories of war-ethics (ad bello, in bellum) do not capture the singular ethical problem of AWS, which pertains to the operation of military means, rather than their human targets. It is argued that reductive perception of the human mind is misleading about the feasibility of ‘ethical robots’, capable of independent moral discretion. To provide a thick examination of human agency from the perspective of Jewish tradition, §5 explores two stories from the biblical book of Samuel (the murder of Nob’s priests and of Uriah). The lessons about the significance of moral agency within the pubic-political military sphere are made explicit, as well as the possible costs resulting from the loss of human agency in the case of AWS. Given that realpolitik considerations are basic in halakhah, §6 considers some possible contemporary socio-political implications of the AWS, that may risk the sustainability of the democratic project. §7 concludes by pointing out humane contributions of Jewish law to contemporary techno-ethics. (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 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)

An increasing number of financial services (FS) companies are adopting solutions driven by artificial intelligence (AI) to gain operational efficiencies, derive strategic insights, and improve customer engagement. However, the rate of adoption has been low, in part due to the apprehension around its complexity and self-learning capability, which makes auditability a challenge in a highly regulated industry. There is limited literature on how FS companies can implement the governance and controls specific to AI-driven solutions. AI auditing cannot be performed in (...) a vacuum; the risks are not confined to the algorithm itself, but rather permeates the entire organization. Using the risk of unfairness as an example, this paper will introduce the overarching governance strategy and control framework to address the practical challenges in mitigating risks AI introduces. With regulatory implications and industry use cases, this framework will enable leaders to innovate with confidence. (shrink)

We argue that legal argumentation, as the subject matter as well as a special subfield of Argumentation Studies (AS), has to be examined by making skilled use of the full panoply of tools such as argumentation and story schemes which are at the forefront of current work in AS. In reviewing the literature, we make explicit our own methodological choices (particularly regarding the place of normative deliberation in practical reasoning) and then illustrate the implications of such an approach through the (...) analysis of a case study in the English law of evidence. We argue that a clear distinction must be drawn between practical argumentation and stories. Because of the institutional separation between legal judgment and fact-finding in common-law jury trials, we argue for the combination of argument and story-based analysis. (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)

This Essay discusses whether U.S. law is ready for artificial intelligence (“AI”) which is headed down the road of blurring the line between human and machine minds. Perhaps the most high-profile and recent examples of AI are Large Language Models (“LLMs”) such as ChatGPT and Google Gemini that can generate written text, reason and analyze in a manner that seems to mimic human capabilities. U.S. law is based on English common law, which in turn incorporates Christian principles that assume the (...) dominance and uniqueness of humankind. U.S. law assumes human communication skills are accompanied by attributes such as consciousness and Free Will (“FW”) that, in turn, underpin critical legal concepts such as mens rea – i.e., intent. Philosophers and others generally agree that consciousness is necessary for FW. On the assumption that human beings possess consciousness that supports FW, the law thereafter deems human beings capable of acting with legal consequences, from entering into contracts to committing crimes. With a focus on LLMs, the Essay suggests that U.S. law may struggle to respond to AI because the technology disrupts the law’s assumptions regarding the uniqueness of human traits and abilities such as consciousness, FW, written communications and reasoning. (shrink)

Protection of emergent works is impossible. Without an author, there is no expression of ideas which can be original, and thus no copyrightable work. Indeed, the whole system of copyright law, its conceptual building blocks of idea-expression dichotomy, originality, authorship, and the concept of a protectable work operate in the notation of human creativity. Emergent works fall outside of copyright’s positive ontology, being akin to ideas, facts, or subject-matter predicated by technical considerations, rather than authorial creativity. In other words, they (...) do not exist as things in law and thus cannot as such be owned. Rather, like any idea, they can be transformed through creative expression of an author–– possibly becoming works, but also not being authorless or emergent anymore. This is argued as a matter of the U.S. and EU legal doctrines, the international framework, and copyright theory. (shrink)

Digital privacy scholars tend to bemoan ordinary people’s limited knowledge of and lukewarm interest in what happens to their digital data. This general lack of interest and knowledge is often taken as a consideration in favor of legislation aiming to force internet companies into adopting more responsible data practices. While we remain silent on whether any new laws are called for, in this paper we wish to underline a neglected consequence of people’s ignorance of and apathy for digital privacy: their (...) potential to encourage capture by industry interests. In particular, we argue that such laws may be at increased risk of capture because they are unlikely to be democratically responsive. We make this claim on a twofold basis: first, well-known theoretical mechanisms explaining how the absence of responsiveness leads to capture, identified in prior political science and political philosophy literature, yield the prediction that digital privacy legislation is likely to be unresponsive and thus captured; second, empirical data concerning the European Union’s digital privacy laws, with a special focus on the General Data Protection Regulation, appears to confirm these predictions: the bloc’s (world’s?) flagship privacy protection law seems more responsive to corporate than citizen interests. (shrink)

Much scholarly attention has recently been devoted to ways in which artificial intelligence (AI) might weaken formal political democracy, but little attention has been devoted to the effect of AI on “cultural democracy”—that is, democratic control over the forms of life, aesthetic values, and conceptions of the good that circulate in a society. This work is the first to consider in detail the dangers that AI-driven cultural recommendations pose to cultural democracy. This Article argues that AI threatens to weaken cultural (...) democracy by undermining individuals’ direct and spontaneous engagement with a diverse range of cultural materials. It further contends that United States law, in its present form, is ill equipped to address these challenges, and suggests several strategies for better regulating culture-mediating AI. Finally, it argues that while such regulations might run afoul of contemporary First Amendment doctrine, the most normatively attractive interpretation of the First Amendment not only allows but encourages such interventions. (shrink)

The application of modern technologies with artificial intelligence (AI) in all spheres of human life is growing exponentially alongside concern for its controllability. The lack of public, state, and international control over AI technologies creates large-scale risks of using such software and hardware that (un)intentionally harm humanity. The events of recent month and years, specifically regarding the Russian Federation’s war against its democratic neighbour Ukraine and other international conflicts of note, support the thesis that the uncontrolled use of AI, especially (...) in the military sphere, may lead to deliberate disregard for the moral standards of controlled AI or the spontaneous emergence of aggressive autonomous AI. The development of legal regulation for the use of technologies with AI is prolonged concerning the rapid development of these artefacts, which simultaneously cover all areas of public relations. Therefore, control over the creation and use of AI should be carried out not only by purely technical regulation (e.g., technical standards and conformance assessments, corporate and developer regulations, requirements enforced through industry-wide ethical codes); but also by comprehensive legislation and intergovernmental oversight bodies that codify and enforce specific changes in the rights and duties of legal persons. This article shall present the “Morality Problem” and “Intentionality Problem” of AI, and reflect upon various lacunae that arise when implementing AI for military purposes. (shrink)

This paper summarizes the seminar AI in Medicine in Context: Hopes? Nightmares? that was held at the Centre for Ethics at the University of Toronto on October 17, 2017, with special guest assistant professor and neurosurgeon Dr. Sunit Das. The paper discusses the key points from Dr. Das' talk. Specifically, it discusses about Dr. Das' perspective on the ethical and moral issues that was experienced from applying artificial intelligence (AI) in law and how such issues can also arise when applying (...) AI to medicine, and concludes with a brief discussion on how such issues can be prevented. (shrink)

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)

For more than two decades, police in the United States have used facial recognition to surveil civilians. Local police departments deploy facial recognition technology to identify protestors’ faces while federal law enforcement agencies quietly amass driver’s license and social media photos to build databases containing billions of faces. Yet, despite the widespread use of facial recognition in law enforcement, there are neither federal laws governing the deployment of this technology nor regulations settings standards with respect to its development. To make (...) matters worse, the Fourth Amendment—intended to limit police power and enacted to protect against unreasonable searches—has struggled to rein in new surveillance technologies since its inception. -/- This Article examines the Supreme Court’s Fourth Amendment jurisprudence leading up to Carpenter v. United States and suggests that the Court is reinterpreting the amendment for the digital age. Still, the too-slow expansion of privacy protections raises challenging questions about racial bias, the legitimacy of police power, and ethical issues in artificial intelligence design. This Article proposes the development of an algorithmic auditing and accountability market that not only sets standards for AI development and limitations on governmental use of facial recognition but encourages collaboration between public interest technologists and regulators. Beyond the necessary changes to the technological and legal landscape, the current system of policing must also be reevaluated if hard-won civil liberties are to endure. (shrink)

In this paper, I set out some basic elements of a deontic logic with an implementation appropriate for handling conflicting legal obligations for purposes of programming autonomous machine agents. Kantian justice demands that the prescriptive system of enforceable public laws be consistent, yet statutes or case holdings may often describe legal obligations that contradict; moreover, even fundamental constitutional rights may come into conflict. I argue that a deontic logic of the law should not try to work around such conflicts but, (...) instead, identify and expose them so that the rights and duties that generate inconsistencies in public law can be explicitly qualified and the conflicts resolved. I then argue that a credulous, non-monotonic deontic logic can describe inconsistent legal obligations while meeting Kant’s demand for consistency in the prescriptive system of public law. Finally, I propose an implementation of this logic via a modified form of “answer set programming,” which I demonstrate with some simple examples. (shrink)

This article argues that criminal law should not, in general, prevent the creation of artificially intelligent servants who achieve humanlike moral status, even though it may well be immoral to construct such beings. In defending this claim, a series of thought experiments intended to evoke clear intuitions is proposed, and presuppositions about any particular theory of criminalization or any particular moral theory are kept to a minimum.

Argumentation schemes can be described as abstract structures representing the most generic types of argument, constituting the building blocks of the ones used in everyday reasoning. This paper investigates the structure, classification, and uses of such schemes. Three goals are pursued: 1) to describe the schemes, showing how they evolved and how they have been classified in the traditional and the modern theories; 2) to propose a method for classifying them based on ancient and modern developments; and 3) to outline (...) and show how schemes can be used to describe and analyze or produce real arguments. To this purpose, we will build on the traditional distinctions for building a dichotomic classification of schemes, and we will advance a modular approach to argument analysis, in which different argumentation schemes are combined together in order to represent each step of reasoning on which a complex argument relies. Finally, we will show how schemes are applied to formal systems, focusing on their applications to Artificial Intelligence, AI & Law, argument mining, and formal ontologies. (shrink)

Recent advances in natural language processing have given rise to a new kind of AI architecture: the language agent. By repeatedly calling an LLM to perform a variety of cognitive tasks, language agents are able to function autonomously to pursue goals specified in natural language and stored in a human-readable format. Because of their architecture, language agents exhibit behavior that is predictable according to the laws of folk psychology: they function as though they have desires and beliefs, and then make (...) and update plans to pursue their desires given their beliefs. We argue that the rise of language agents significantly reduces the probability of an existential catastrophe due to loss of control over an AGI. This is because the probability of such an existential catastrophe is proportional to the difficulty of aligning AGI systems, and language agents significantly reduce that difficulty. In particular, language agents help to resolve three important issues related to aligning AIs: reward misspecification, goal misgeneralization, and uninterpretability. (shrink)

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

An increasing number of regulations propose the notion of ‘AI audits’ as an enforcement mechanism for achieving transparency and accountability for artificial intelligence (AI) systems. Despite some converging norms around various forms of AI auditing, auditing for the purpose of compliance and assurance currently have little to no agreed upon practices, procedures, taxonomies, and standards. We propose the ‘criterion audit’ as an operationalizable compliance and assurance external audit framework. We model elements of this approach after financial auditing practices, and argue (...) that AI audits should similarly provide assurance to their stakeholders about AI organizations’ ability to govern their algorithms in ways that mitigate harms and uphold human values. We discuss the necessary conditions for the criterion audit, and provide a procedural blueprint for performing an audit engagement in practice. We illustrate how this framework can be adapted to current regulations by deriving the criteria on which ‘bias audits’ for hiring algorithms can be performed, as required by the recently effective New York City Local Law 144 of 2021. We conclude by offering critical discussion on the benefits, inherent limitations, and implementation challenges of applying practices of the more mature financial auditing industry to AI auditing where robust guardrails against quality assurance issues are only starting to emerge. Our discussion as informed by experiences in performing these audits in practice highlights the critical role that an audit ecosystem plays in ensuring the effectiveness of such methodology. (shrink)

Recent research shows – somewhat astonishingly – that people are willing to ascribe moral blame to AI-driven systems when they cause harm [1]–[4]. In this paper, we explore the moral- psychological underpinnings of these findings. Our hypothesis was that the reason why people ascribe moral blame to AI systems is that they consider them capable of entertaining inculpating mental states (what is called mens rea in the law). To explore this hypothesis, we created a scenario in which an AI system (...) runs a risk of poisoning people by using a novel type of fertilizer. Manipulating the computational (or quasi-cognitive) abilities of the AI system in a between-subjects design, we tested whether people’s willingness to ascribe knowledge of a substantial risk of harm (i.e., recklessness) and blame to the AI system. Furthermore, we investigated whether the ascription of recklessness and blame to the AI system would influence the perceived blameworthiness of the system’s user (or owner). In an experiment with 347 participants, we found (i) that people are willing to ascribe blame to AI systems in contexts of recklessness, (ii) that blame ascriptions depend strongly on the willingness to attribute recklessness and (iii) that the latter, in turn, depends on the perceived “cognitive” capacities of the system. Furthermore, our results suggest (iv) that the higher the computational sophistication of the AI system, the more blame is shifted from the human user to the AI system. (shrink)

The use of machine learning algorithms has become ubiquitous in hiring decisions. Recent studies have shown that many of these algorithms generate unlawful discriminatory effects in every step of the process. The training phase of the machine learning models used in these decisions has been identified as the main source of bias. For a long time, discrimination cases have been analyzed under the banner of disparate treatment and disparate impact, but these concepts have been shown to be ineffective in the (...) context of AI. This paper examines the possibility of studying algorithmic discrimination from the perspective of negligence law. Negligent selection and validation of datasets and classifiers is a salient cause of improper training in machine learning. It is a breach of an employer’s duty to protect others, a failure of its exercise of due care in the manner of choosing employees. Adopting negligence to algorithmic discrimination requires a reformulation of what is foreseeable and reasonable within the context of black box algorithms, and a shift in the burden of proof to the employer, who must present evidence of its efforts to avoid bias. (shrink)

Our reflections on LAWS issues are the result of the work of our research group on AI and ethics at the Informatics Center in partnership with the Information Science Department, both from the Federal University of Pernambuco, Brazil. In particular, our propositions and provocations are tied to Bianca Ximenes’s ongoing doctoral thesis, advised by Prof. Geber Ramalho, from the area of computer science, and co-advised by Prof. Diego Salcedo, from the humanities. Our research group is interested in answering two tricky (...) questions: What would an ethical AI be? And how can one guarantee that a given intelligent system will follow intercultural human ethical principles? (shrink)

Abstract: As the integration of Artificial Intelligence (AI) continues to permeate various sectors, the legal domain stands on the cusp of a transformative era. This research paper delves into the multifaceted relationship between AI and the law, scrutinizing the profound implications and innovative applications that emerge at the intersection of these two realms. The study commences with an examination of the current landscape, assessing the challenges and opportunities that AI presents within legal frameworks. With an emphasis on efficiency, accuracy, and (...) accessibility, AI technologies are reshaping traditional legal processes, ranging from document analysis and contract review to predictive legal analytics. Furthermore, the paper scrutinizes the ethical considerations and potential biases inherent in AI algorithms, exploring the delicate balance between technological advancements and the preservation of legal principles such as fairness, accountability, and transparency. The research also delves into the evolving role of legal professionals in navigating and overseeing AI applications, emphasizing the importance of responsible AI deployment. Drawing on case studies and real-world examples, this paper showcases instances where AI has already demonstrated its efficacy in legal contexts, highlighting successful implementations and identifying areas for improvement. The discussion extends to the evolving regulatory landscape, as legal systems grapple with the need to adapt and establish frameworks that ensure the responsible and ethical use of AI technologies. In conclusion, this research contributes to the growing discourse on the dynamic interplay between AI and the legal domain. By illuminating the potential benefits, ethical considerations, and regulatory challenges, it provides a comprehensive overview for legal practitioners, policymakers, and technologists alike, fostering a nuanced understanding of the evolving landscape where artificial intelligence intersects with the law. (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)

The human mind has been a subject matter of study in psychology, law, science, philosophy and other disciplines. By definition, its potential is power, abilities and capacities including perception, knowledge, sensation, memory, belief, imagination, emotion, mood, appetite, intention, and action (Pardo, Patterson). In terms of role, it creates and shapes societal morality, culture, peace and democracy. Today, a rapidly advancing science–technology–artificial intelligence (AI) landscape is able to reach into the inner realms of the human mind. Technology, particularly neurotechnology enables access (...) to the human mind for research, treatment and other purposes. This enabling feature is now a growing concern. In the field of human rights, this human-science–technology interface is leading to articulation of new human rights to safeguard against modern threats. The wider usage of expressions like neurolaw and mental autonomy reflects on the emerging field of standards to protect the human mind from interference, manipulation and control. Growing literature on the subject sheds light on the human-rights-based approach to the challenge. (shrink)

When this article was first planned, writing was going to be exclusively about two things - the origin of life and human evolution. But it turned out to be out of the question for the author to restrict himself to these biological and anthropological topics. A proper understanding of them required answering questions like “What is the nature of the universe – the home of life – and how did it originate?”, “How can time travel be removed from fantasy and (...) science fiction, to be made scientific and practical?”, and “How can the proposed young age of genus Homo be made to actually be reasonable – when simply stating it would be solid ground for instant rejection and dismissal?” The result is that the article also talks about subjects like Artificial Intelligence, General Relativity, and cosmology. -/- From where did life originate? God? Evolution? Panspermia? If the tendency of humans and scientists to regard undiscovered science as pseudoscience can be overcome, Einstein gave another alternative to consider when he introduced General Relativity. Time isn’t linear – progressing in a straight line from past to present to future. That assumption ignores Relativity which states that space AND TIME are curved. Where did life and the genetic code come from? Can the answer build AI? -/- The first question can be answered by the section of this article titled SETI, Evolution, and Time which says life (possibly multicellular and intelligent) and the genetic code came from humans acquiring knowledge of these things over the centuries, then applying that knowledge – via terraforming, accumulation of raw materials like amino acids and nucleic acids, genetic engineering - to a time in the past when life didn’t exist. From that origin, life evolved through innumerable mutations and adaptations, with humans once again acquiring knowledge of it in cyclic (nonlinear) time. -/- The second question is answered by saying artificial intelligence (AI) as the product of life is only half of the equation. The other half refers to Relativity’s curved space-time and violation of the notion that time always travels from past to future. We have always lived in an artificially intelligent, non-probabilistic universe where everything in time and space is connected into one thing by quantum entanglement – making the brain and genes products of binary-digit activity or artificial intelligence (life is not merely dependent on biology’s “lock and key” mechanisms but also possesses AI). -/- The earliest documented representative of the genus Homo is Homo habilis, which evolved around 2.8 million years ago. Scientists used to believe there was a straight line from H. habilis to us, Homo sapiens. This article will use the “advanced” waves loved by Physics Nobel laureate Richard Feynman, view the history of science through the lens of Conic Sections applied to Relativity’s curved space-time, and incorporate the necessity of so-called imaginary time * – popularized by Prof. Stephen Hawking. While the evolutionary proposals are more in agreement with this early straight line than with modern theories, Albert Einstein’s General Relativity is used to transform the straight line into a curved line, ultimately concluding that Homo habilis (H. habilis) originated only (and unbelievably, as far as today’s science and technology is concerned) ~250,000 years ago. Other branches and dead ends of Homo – e.g. Neanderthals – are the result of mutations and adaptations, with the resultant modifications to anatomy and physiology. The surprisingly young age of H. habilis allows nearly 200,000 years for habilis, or one of its descendants, to reach Australia … if this country’s indigenous Aboriginal population did, as claimed, reach this “island continent” 60,000 years ago. -/- * The ultraviolet catastrophe, also called the Rayleigh–Jeans catastrophe, is a failure of classical physics to predict observed phenomena: it can be shown that a blackbody - a hypothetical perfect absorber and radiator of energy - would release an infinite amount of energy, contradicting the principles of conservation of energy and indicating that a new model for the behaviour of blackbodies was needed. At the start of the 20th century, physicist Max Planck derived the correct solution by making some strange (for the time) assumptions. In particular, Planck assumed that electromagnetic radiation can only be emitted or absorbed in discrete packets, called quanta. Albert Einstein postulated that Planck's quanta were real physical particles (what we now call photons), not just a mathematical fiction. From there, Einstein developed his explanation of the photoelectric effect (when quanta or photons of light shine on certain metals, electrons are released and can form an electric current). So it appears entirely possible that another supposed mathematical trickery (imaginary time and the y-axis of Wick rotation) will find practical application in the future. -/- The article includes mathematical references to cosmology (spoiler alert – you’ll read about things like Vector-Tensor-Scalar Geometry, topology, the “eternal present”, Einstein’s Unified Field, the inverse-square law, and there being no Big Bang and no multiverse - but there will also be no equations). -/- The other subheadings in this essay are – -/- NONLINEAR TIME AND ELECTRICAL ENGINEERING (about a 2009 electrical engineering experiment at America’s Yale University, and cosmic wormholes) -/- BITS AND TOPOLOGY (base-2 maths aka Binary digiTS, Mobius strips, and figure-8 Klein bottles) -/- WICK ROTATION, CAUSALITY, AND UNITING TIME (do past, present and future co-exist in an “eternal present”?) -/- DIGITAL BRAIN, DIGITAL UNIVERSE (if the brain and the universe are ultimately composed of binary digits, we'll someday be able to do the same things with the brain and universe that we now do with computers) -/- PROPOSAL: HUMAN AND ANIMAL INSTINCTS ARE THE RESULT OF THE UNIVERSE BEING UNIFIED BY BINARY DIGITS (AND TOPOLOGY) (If everything in the universe is ultimately composed of electronic BITS, then the universe must possess Artificial Intelligence - some prefer the term Cosmic Consciousness) -/- INFORMATION THEORY CONQUERS A RED GIANT (preserving Earth by keeping the Sun near today’s level of activity forever). 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Artificial Intelligence (AI) is a diverse technology. It is already having significant effects on many jobs and sectors of the economy and over the next ten to twenty years it will drive profound changes in the way New Zealanders live and work. Within the workplace AI will have three dominant effects. This report (funded by the New Zealand Law Foundation) addresses: Chapter 1 Defining the Technology of Interest; Chapter 2 The changing nature and value of work; Chapter 3 AI and (...) the employment relationship; Chapter 4 Consumers, professions and society. The report includes recommendations to the New Zealand Government. (shrink)

O Judiciário moderno constitui um sistema hierarquizado e centralizado, em que cortes superiores, com um número pequeno de membros, têm a função de “dar a última palavra” para resolver desacordos doutrinários entre instâncias inferiores – e inclusive rever decisões das demais autoridades. Essa estrutura hierárquica implica uma fonte peculiar de instabilidade: o tribunal superior se torna como que um “ponto único de falha” suscetível à captura, à pressão indevida, à sobrecarga, e até a súbitas mudanças doutrinárias – pondo em risco (...) seu alegado papel de estabilizador do regime democrático. Isso é salientado quando tais cortes sofrem pressão, ou quando há reviravoltas decorrentes de uma maioria frágil no tribunal – a exemplo do debate sobre a prisão em segunda instância no Brasil. Analiso como o desenvolvimento tecnologias de informação e comunicação (TICs) poderia mitigar este problema por meio de um sistema que agregue decisões judiciais de instâncias inferiores a fim de resolver conflitos de jurisprudência numa determinada jurisdição; isso equivaleria a um sistema de votação que substituiria a atuação da corte de última instância na unificação de jurisprudência – como se a última instância fosse, na realidade, composta pelo judiciário como um todo. Isso alteraria a estrutura de decisão hierarquizada do judiciário, no que diz respeito a divergências entre magistrados, aumentando a descentralização do sistema – associada ao ideal da tradição de common law. Embora original, a proposta é modesta, e poderia ser atingida por meio de mudanças incrementais, sem ruptura institucional, a partir de práticas e tecnologias já existentes – e uso como exemplo ferramentas de legal analytics e o sistema de “repercussão geral de recursos extraordinários” do Brasil, no qual se utiliza processamento de linguagem natural para filtrar e classificar processos de acordo com o tema. Ao final, discutem-se possíveis vantagens e desvantagens dessa proposta, como características epistêmicas da descentralização e o risco de “rigidez” na evolução da doutrina jurídica – em especial no que se refere aos longos mandatos de membros de tribunais superiores. Concluímos que a descentralização da unificação da jurisprudência pode contribuir para mitigar o risco de, com o desenvolvimento da tecnologia, maior concentração de poder. (shrink)

Over the years, companies have adopted hiring algorithms because they promise wider job candidate pools, lower recruitment costs and less human bias. Despite these promises, they also bring perils. Using them can inflict unintentional harms on individual human rights. These include the five human rights to work, equality and nondiscrimination, privacy, free expression and free association. Despite the human rights harms of hiring algorithms, the AI ethics literature has predominantly focused on abstract ethical principles. This is problematic for two reasons. (...) First, AI principles have been criticized for being vague and not actionable. Second, the use of vague ethical principles to discuss algorithmic risks does not provide any accountability. This lack of accountability creates an algorithmic accountability gap. Closing this gap is crucial because, without accountability, the use of hiring algorithms can lead to discrimination and unequal access to employment opportunities. This paper makes two contributions to the AI ethics literature. First, it frames the ethical risks of hiring algorithms using international human rights law as a universal standard for determining algorithmic accountability. Second, it evaluates four types of algorithmic impact assessments in terms of how effectively they address the five human rights of job applicants implicated in hiring algorithms. It determines which of the assessments can help companies audit their hiring algorithms and close the algorithmic accountability gap. (shrink)

Artificial intelligence research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal acts, term in this article AI-Crime. AIC is theoretically feasible thanks to published experiments in automating fraud targeted at social media users, as well as demonstrations of AI-driven manipulation of simulated markets. However, because AIC is still a relatively young and inherently (...) interdisciplinary area—spanning socio-legal studies to formal science—there is little certainty of what an AIC future might look like. This article offers the first systematic, interdisciplinary literature analysis of the foreseeable threats of AIC, providing ethicists, policy-makers, and law enforcement organisations with a synthesis of the current problems, and a possible solution space. (shrink)

The value sensitive design (VSD) approach to designing transformative technologies for human values is taken as the object of study in this chapter. VSD has traditionally been conceptualized as another type of technology or instrumentally as a tool. The various parts of VSD’s principled approach would then aim to discern the various policy requirements that any given technological artifact under consideration would implicate. Yet, little to no consideration has been given to how laws, regulations, policies and social norms engage within (...) VSD practices. Similarly, how the interactive nature of the VSD approach can, in turn, influence those directives. This is exacerbated when we consider machine ethics policy that have global consequences outside their development spheres. What constructs and models will position AI designers to engage in policy concerns? How can the design of AI policy be integrated with technical design? How might VSD be used to develop AI policy? How might law, regulations, social norms, and other kinds of policy regarding AI systems be engaged within value sensitive design? This chapter takes the VSD as its starting point and aims to determine how laws, regulations and policies come to influence how value trade-offs can be managed within VSD practices. It shows that the iterative and interactional nature of VSD both permits and encourages existing policies to be integrated both early on and throughout the design process. The chapter concludes with some potential future research programs. (shrink)