Many leading intellectuals, technologists, commentators, and ordinary people have in recent weeks become embroiled in a fiery debate (yet to hit the pages of scholarly journals) on the alleged need to press pause on the development of generative artificial intelligence (AI). Spurred by an open letter from the Future of Life Institute (FLI) calling for just such a pause, the debate occasioned, at lightning speed, a large number of responses from a variety of sources pursuing a variety of argumentative strategies. (...) Not all of the respondents resist the calls for the pause. For example, the Distributed AI Research Institute (DAIRI) has issued a statement claiming that while the pause is indeed desirable, the FLI’s focus on predicted existential risks to the exclusion of present-day concerns is misguided. The discussion shows no signs of abating. -/- I wish to raise an objection both to the FLI’s open letter, and to the statement by DAIRI (I will refer to both collectively as “letters” or “open letters”). While in the context of the issues brought up by the two Institutes, this objection is, to my knowledge, novel, I pretend to little originality in voicing it. Rather, I offer a simple (yet unappreciated) application of some insights from political philosophy and economics to show that the recommendations contained in both missives are severely underargued. (shrink)

The proposed European Artificial Intelligence Act (AIA) is the first attempt to elaborate a general legal framework for AI carried out by any major global economy. As such, the AIA is likely to become a point of reference in the larger discourse on how AI systems can (and should) be regulated. In this article, we describe and discuss the two primary enforcement mechanisms proposed in the AIA: the _conformity assessments_ that providers of high-risk AI systems are expected to conduct, and (...) the _post-market monitoring plans_ that providers must establish to document the performance of high-risk AI systems throughout their lifetimes. We argue that the AIA can be interpreted as a proposal to establish a Europe-wide ecosystem for conducting AI auditing, albeit in other words. Our analysis offers two main contributions. First, by describing the enforcement mechanisms included in the AIA in terminology borrowed from existing literature on AI auditing, we help providers of AI systems understand how they can prove adherence to the requirements set out in the AIA in practice. Second, by examining the AIA from an auditing perspective, we seek to provide transferable lessons from previous research about how to refine further the regulatory approach outlined in the AIA. We conclude by highlighting seven aspects of the AIA where amendments (or simply clarifications) would be helpful. These include, above all, the need to translate vague concepts into verifiable criteria and to strengthen the institutional safeguards concerning conformity assessments based on internal checks. (shrink)

Recent advancements in AI have prompted a large number of AI ethics guidelines published by governments and nonprofits. While many of these papers propose concrete or seemingly applicable ideas, few philosophically sound proposals are made. In particular, we observe that the line of questioning has often not been examined critically and underlying conceptual problems not always dealt with at the root. In this paper, we investigate the nature of ethical AI systems and what their moral status might be by first (...) turning to the notions of moral agency and patience. We find that their explication would come at a too high cost which is why we, second, articulate a different approach that avoids vague and ambiguous concepts or the problem of other minds. Third, we explore the impact of our philosophical and conceptual analysis on the regulatory landscape, make this link explicit, and finally propose a set of promising policy steps. (shrink)

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

With Artificial Intelligence entering our lives in novel ways—both known and unknown to us—there is both the enhancement of existing ethical issues associated with AI as well as the rise of new ethical issues. There is much focus on opening up the ‘black box’ of modern machine-learning algorithms to understand the reasoning behind their decisions—especially morally salient decisions. However, some applications of AI which are no doubt beneficial to society rely upon these black boxes. Rather than requiring algorithms to be (...) transparent we should focus on constraining AI and those machines powered by AI within microenvironments—both physical and virtual—which allow these machines to realize their function whilst preventing harm to humans. In the field of robotics this is called ‘envelopment’. However, to put an ‘envelope’ around AI-powered machines we need to know some basic things about them which we are often in the dark about. The properties we need to know are the: training data, inputs, functions, outputs, and boundaries. This knowledge is a necessary first step towards the envelopment of AI-powered machines. It is only with this knowledge that we can responsibly regulate, use, and live in a world populated by these machines. (shrink)

The term ‘responsible AI’ has been coined to denote AI that is fair and non-biased, transparent and explainable, secure and safe, privacy-proof, accountable, and to the benefit of mankind. Since 2016, a great many organizations have pledged allegiance to such principles. Amongst them are 24 AI companies that did so by posting a commitment of the kind on their website and/or by joining the ‘Partnership on AI’. By means of a comprehensive web search, two questions are addressed by this study: (...) Did the signatory companies actually try to implement these principles in practice, and if so, how? What are their views on the role of other societal actors in steering AI towards the stated principles? It is concluded that some three of the largest amongst them have carried out valuable steps towards implementation, in particular by developing and open sourcing new software tools. To them, charges of mere ‘ethics washing’ do not apply. Moreover, some 10 companies from both the USA and Europe have publicly endorsed the position that apart from self-regulation, AI is in urgent need of governmental regulation. They mostly advocate focussing regulation on high-risk applications of AI, a policy which to them represents the sensible middle course between laissez-faire on the one hand and outright bans on technologies on the other. The future shaping of standards, ethical codes, and laws as a result of these regulatory efforts remains, of course, to be determined. (shrink)

Hastings Center Report, Volume 51, Issue 5, Page 6-7, September‐October 2021.

This article explores the cooperation of government and the private sector to tackle the ethical dimension of artificial intelligence. The argument draws on the institutionalist approach in philosophy and business ethics defending a ‘division of moral labor’ between governments and the private sector. The goal and main contribution of this article is to explain how this approach can provide ethical guidelines to the AI industry and to highlight the limits of self-regulation. In what follows, I discuss three institutionalist claims. (...) First, principles of AI ethics should be validated through legitimate democratic processes. Second, compliance with these principles should be secured in a stable way. Third, their implementation in practice should be as efficient as possible. If we accept these claims, there are good reasons to conclude that, in many cases, governments implementing hard regulation are in principle the best instruments to secure an ethical development of AI systems. Where adequate regulation exists, firms should respect the law. But when regulation does not yet exist, helping governments build adequate regulation should be businesses’ ethical priority, not self-regulation. (shrink)

Law and the legal system through which law is effected are very powerful, yet the power of the law has always been limited by the laws of nature, upon which the law has now direct grip. Human law now faces an unprecedented challenge, the emergence of a second limit on its grip, a new “species” of intelligent agents (AI machines) that can perform cognitive tasks that until recently only humans could. What happens, as a matter of law, when another species (...) interacts with us, can be integrated into human minds and bodies, makes “real-world” decisions—not through human proxies, but directly—and does all this “intelligently”, with what one could call autonomous agency or even a “mind” of its own? The article starts from the clear premise that control cannot be exercised directly on AI machines through human law. That control can only be effected through laws that apply to humans. This has several regulatory implications. The article’s first discusses what, in any attempt to regulate AI machines, the law can achieve. Having identified what the law can do, the article then canvases what the law should aim to achieve overall. The article encapsulate its analysis in a list of both doctrinal and normative principles that should underpin any regulation aimed at AI machines. Finally, the article compares three transnational options to implement the proposed regulatory approach. (shrink)

Cohen’s article (2023) on the significance of ChatGPT for bioethics suggests that little is known about the development of generative AI (“GAI”) in China and other national markets. It warns about...

As artificial intelligence technologies become increasingly prominent in our daily lives, media coverage of the ethical considerations of these technologies has followed suit. Since previous research has shown that media coverage can drive public discourse about novel technologies, studying how the ethical issues of AI are portrayed in the media may lead to greater insight into the potential ramifications of this public discourse, particularly with regard to development and regulation of AI. This paper expands upon previous research by systematically (...) analyzing and categorizing the media portrayal of the ethical issues of AI to better understand how media coverage of these issues may shape public debate about AI. Our results suggest that the media has a fairly realistic and practical focus in its coverage of the ethics of AI, but that the coverage is still shallow. A multifaceted approach to handling the social, ethical and policy issues of AI technology is needed, including increasing the accessibility of correct information to the public in the form of fact sheets and ethical value statements on trusted webpages, collaboration and inclusion of ethics and AI experts in both research and public debate, and consistent government policies or regulatory frameworks for AI technology. (shrink)

This paper navigates the relationship between the growing influence of Artificial Intelligence (AI) and the foundational principles of academic integrity. It offers an in-depth analysis of how key academic stakeholders—publishers and universities—are crafting strategies and guidelines to integrate AI into the sphere of scholarly work. These efforts are not merely reactionary but are part of a broader initiative to harness AI’s potential while maintaining ethical standards. The exploration reveals a diverse array of stances, reflecting the varied applications of AI in (...) academic settings. These range from conservative approaches emphasizing the need for stringent AI regulations to more progressive strategies that seek to leverage AI’s capabilities. The paper accentuates the importance of guidelines that are adaptable, transparent, and culturally sensitive, acknowledging the global context of academic research. It also highlights the pressing need for effective implementation and enforcement of these guidelines to ensure that the integration of AI into academia does not compromise the integrity of scholarly work. The conclusion emphasizes the dynamic balance required between embracing AI’s transformative potential and upholding the timeless values of academic rigor and ethical research. This comprehensive analysis aims to provide a detailed perspective on the evolving role of AI in academia, fostering a dialog on how best to navigate this new technological frontier while preserving the sanctity of academic scholarship. (shrink)

As the artificial intelligence (AI) ethics field is currently working towards its operationalisation, ethics review as carried out by research ethics committees (RECs) constitutes a powerful, but so far underdeveloped, framework to make AI ethics effective in practice at the research level. This article contributes to the elaboration of research ethics frameworks for research projects developing and/or using AI. It highlights that these frameworks are still in their infancy and in need of a structure and criteria to ensure AI research (...) projects advance in a way that respects norms and principles. This article proposes to draw from the European Union’s AI Act currently in development to shape these frameworks. Although, in the current form of the draft (as of August 2023), the obligations of the AI Act do not apply to scientific research, it is most likely that they will still have a strong impact on AI research considering the need to anticipate market placement or to test new tools in real world conditions. This article investigates what the risk-based approach in the AI Act implies for research ethics and highlights some AI Act obligations of particular value to implement for ethics review processes. (shrink)

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

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

In this paper, we discuss several problems with current Big data practices which, we claim, seriously erode the role of informed consent as it pertains to the use of personal information. To illustrate these problems, we consider how the notion of informed consent has been understood and operationalised in the ethical regulation of biomedical research (and medical practices, more broadly) and compare this with current Big data practices. We do so by first discussing three types of problems that can (...) impede informed consent with respect to Big data use. First, we discuss the transparency (or explanation) problem. Second, we discuss the re-repurposed data problem. Third, we discuss the meaningful alternatives problem. In the final section of the paper, we suggest some solutions to these problems. In particular, we propose that the use of personal data for commercial and administrative objectives could be subject to a ‘soft governance’ ethical regulation, akin to the way that all projects involving human participants (e.g., social science projects, human medical data and tissue use) are regulated in Australia through the Human Research Ethics Committees (HRECs). We also consider alternatives to the standard consent forms, and privacy policies, that could make use of some of the latest research focussed on the usability of pictorial legal contracts. (shrink)

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

We live in exciting times for AI and Law: technical developments are moving at a breakneck pace, and at the same time, the call for more robust AI governance and regulation grows stronger. How should we as an AI & Law community navigate these dramatic developments and claims? In this Presidential Address, I present my ideas for a way forward: researching, developing and evaluating real AI systems for the legal field with researchers from AI, Law and beyond. I will (...) demonstrate how we at the Netherlands National Police Lab AI are developing responsible AI by combining insights from different disciplines, and how this connects to the future of our field. (shrink)

AI for social good is a thriving research topic and a frequently declared goal of AI strategies and regulation. This article investigates the requirements necessary in order for AI to actually serve a public interest, and hence be socially good. The authors propose shifting the focus of the discourse towards democratic governance processes when developing and deploying AI systems. The article draws from the rich history of public interest theory in political philosophy and law, and develops a framework for (...) ‘public interest AI’. The framework consists of (1) public justification for the AI system, (2) an emphasis on equality, (3) deliberation/ co-design process, (4) technical safeguards, and (5) openness to validation. This framework is then applied to two case studies, namely SyRI, the Dutch welfare fraud detection project, and UNICEF’s Project Connect, that maps schools worldwide. Through the analysis of these cases, the authors conclude that public interest is a helpful and practical guide for the development and governance of AI for the people. (shrink)

By the early 2020s, emotional artificial intelligence will become increasingly present in everyday objects and practices such as assistants, cars, games, mobile phones, wearables, toys, marketing, insurance, policing, education and border controls. There is also keen interest in using these technologies to regulate and optimize the emotional experiences of spaces, such as workplaces, hospitals, prisons, classrooms, travel infrastructures, restaurants, retail and chain stores. Developers frequently claim that their applications do not identify people. Taking the claim at face value, this paper (...) asks, what are the privacy implications of emotional AI practices that do not identify individuals? To investigate privacy perspectives on soft non-identifying emotional AI, the paper draws upon the following: over 100 interviews with the emotion detection industry, legal community, policy-makers, regulators and NGOs interested in privacy; a workshop with stakeholders to design ethical codes for using data about emotions; a UK survey of 2068 citizens on feelings about emotion capture technologies. It finds a weak consensus among social stakeholders on the need for privacy, this driven by different interests and motivations. Given this weak consensus, it concludes that there exists a limited window of opportunity to societally agree principles of practice regarding privacy and the use of data about emotions. (shrink)

This article develops a conceptual framework for regulating Artificial Intelligence (AI) that encompasses all stages of modern public policy-making, from the basics to a sustainable governance. Based on a vast systematic review of the literature on Artificial Intelligence Regulation (AIR) published between 2010 and 2020, a dispersed body of knowledge loosely centred around the “framework” concept was organised, described, and pictured for better understanding. The resulting integrative framework encapsulates 21 prior depictions of the policy-making process, aiming to achieve gold-standard (...) societal values, such as fairness, freedom and long-term sustainability. This challenge of integrating the AIR literature was matched by the identification of a structural common ground among different approaches. The AIR framework results from an effort to identify and later analytically deduce synthetic, and generic tool for a country-specific, stakeholder-aware analysis of AIR matters. Theories and principles as diverse as Agile and Ethics were combined in the “AIR framework”, which provides a conceptual lens for societies to think collectively and make informed policy decisions related to what, when, and how the uses and applications of AI should be regulated. Moreover, the AIR framework serves as a theoretically sound starting point for endeavours related to AI regulation, from legislation to research and development. As we know, the (potential) impacts of AI on society are immense, and therefore the discourses, social negotiations, and applications of this technology should be guided by common grounds based on contemporary governance techniques, and social values legitimated via dialogue and scientific research. (shrink)

The aim of this literature review was to compose a narrative review supported by a systematic approach to critically identify and examine concerns about accountability and the allocation of responsibility and legal liability as applied to the clinician and the technologist as applied the use of opaque AI-powered systems in clinical decision making. This review questions if it is permissible for a clinician to use an opaque AI system in clinical decision making and if a patient was harmed as a (...) result of using a clinician using an AIS’s suggestion, how would responsibility and legal liability be allocated? Literature was systematically searched, retrieved, and reviewed from nine databases, which also included items from three clinical professional regulators, as well as relevant grey literature from governmental and non-governmental organisations. This literature was subjected to inclusion/exclusion criteria; those items found relevant to this review underwent data extraction. This review found that there are multiple concerns about opacity, accountability, responsibility and liability when considering the stakeholders of technologists and clinicians in the creation and use of AIS in clinical decision making. Accountability is challenged when the AIS used is opaque, and allocation of responsibility is somewhat unclear. Legal analysis would help stakeholders to understand their obligations and prepare should an undesirable scenario of patient harm eventuate when AIS were used. (shrink)

Despite the increase in the research field of ethics in artificial intelligence, most efforts have focused on the debate about principles and guidelines for responsible AI, but not enough attention has been given to the “how” of applied ethics. This paper aims to advance the research exploring the gap between practice and principles in AI ethics by identifying how companies are applying those guidelines and principles in practice. Through a qualitative methodology based on 22 semi-structured interviews and two focus groups, (...) the goal of the current study is to understand how companies approach ethical issues related to AI systems. A structured analysis of the transcripts brought out many actual practices and findings, which are presented around the following main research topics: ethics and principles, privacy, explainability, and fairness. The interviewees also raised issues of accountability and governance. Finally, some recommendations are suggested such as developing specific sector regulations, fostering a data-driven organisational culture, considering the algorithm’s complete life cycle, developing and using a specific code of ethics, and providing specific training on ethical issues. Despite some obvious limitations, such as the type and number of companies interviewed, this work identifies real examples and direct priorities to advance the research exploring the gap between practice and principles in AI ethics, with a specific focus on Spanish companies. (shrink)

This book explores the revolutionary intersection of Generative AI (GenAI) and cybersecurity. It presents a comprehensive guide that intertwines theories and practices, aiming to equip cybersecurity professionals, CISOs, AI researchers, developers, architects and college students with an understanding of GenAI’s profound impacts on cybersecurity. The scope of the book ranges from the foundations of GenAI, including underlying principles, advanced architectures, and cutting-edge research, to specific aspects of GenAI security such as data security, model security, application-level security, and the emerging fields (...) of LLMOps and DevSecOps. It explores AI regulations around the globe, ethical considerations, the threat landscape, and privacy preservation. Further, it assesses the transformative potential of GenAI in reshaping the cybersecurity landscape, the ethical implications of using advanced models, and the innovative strategies required to secure GenAI applications. Lastly, the book presents an in-depth analysis of the security challenges and potential solutions specific to GenAI, and a forward-looking view of how it can redefine cybersecurity practices. By addressing these topics, it provides answers to questions on how to secure GenAI applications, as well as vital support with understanding and navigating the complex and ever-evolving regulatory environments, and how to build a resilient GenAI security program. The book offers actionable insights and hands-on resources for anyone engaged in the rapidly evolving world of GenAI and cybersecurity. (shrink)

With the increasing prevalence of artificial intelligence (AI) and other digital technologies in healthcare, the ethical debate surrounding their adoption is becoming more prominent. Here I consider the issue of gaining informed patient consent to AI-enhanced care from the vantage point of the United Kingdom’s National Health Service setting. I build my discussion around two claims from the World Health Organization: that healthcare services should not be denied to individuals who refuse AI-enhanced care and that there is no precedence to (...) seeking patient consent to AI-enhanced care. I discus U.K. law relating to patient consent and the General Data Protection Regulation to show that current standards relating to patient consent are adequate for AI-enhanced care. I then suggest that in the future it may not be possible to guarantee patient access to non-AI-enhanced healthcare, in a similar way to how we do not offer patients manual alternatives to automated healthcare processes. Throughout my discussion I focus on the issues of patient choice and veracity in the patient–clinician relationship. Finally, I suggest that the best way to protect patients from potential harms associated with the introduction of AI to patient care is not via an overly burdensome patient consent process but via evaluation and regulation of AI technologies. (shrink)

To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems.

People’s comfort with and acceptability of artificial intelligence (AI) instantiations is a topic that has received little systematic study. This is surprising given the topic’s relevance to the design, deployment and even regulation of AI systems. To help fill in our knowledge base, we conducted mixed-methods analysis based on a survey of a representative sample of the US population (_N_ = 2254). Results show that there are two distinct social dimensions to comfort with AI: as a peer and as (...) a superior. For both dimensions, general and technological efficacy traits—locus of control, communication apprehension, robot phobia, and perceived technology competence—are strongly associated with acceptance of AI in various roles. Female and older respondents also were less comfortable with the idea of AI agents in various roles. A qualitative analysis of comments collected from respondents complemented our statistical approach. We conclude by exploring the implications of our research for AI acceptability in society. (shrink)

The development and deployment of artificial intelligence (AI) is and will profoundly reshape human society, the culture and the composition of civilisations which make up human kind. All technological triggers tend to drive a hype curve which over time is realised by an output which is often unexpected, taking both pessimistic and optimistic perspectives and actions of drivers, contributors and enablers on a journey where the ultimate destination may be unclear. In this paper we hypothesise that this journey is not (...) dissimilar to the personal journey described by the Kubler-Ross change curve and illustrate this by commentary on the potential of AI for drug discovery, development and healthcare and as an enabler for deep space exploration and colonisation. Recent advances in the call for regulation to ensure development of safety measures associated with machine-based learning are presented which, together with regulation of the rapidly emerging digital after-life industry, should provide a platform for realising the full potential benefit of AI for the human species. (shrink)

This extended abstract summarises some of the basic points of AI ethics and policy as they present themselves now. We explain the notion of AI, the main ethical issues in AI and the main policy aims and means.

Artificial intelligence and related technologies are changing both the law and the legal profession. In particular, technological advances in fields ranging from machine learning to more advanced robots, including sensors, virtual realities, algorithms, bots, drones, self-driving cars, and more sophisticated "human-like" robots are creating new and previously unimagined challenges for regulators. These advances also give rise to new opportunities for legal professionals to make efficiency gains in the delivery of legal services. With the exponential growth of such technologies, radical disruption (...) seems likely to accelerate in the near future. This collection brings together a series of contributions by leading scholars in the newly emerging field of artificial intelligence, robotics, and the law. The aim of the book is to enrich legal debates on the social meaning and impact of this type of technology. The distinctive feature of the contributions presented in this edition is that they address the impact of these technological developments in a number of different fields of law and from the perspective of diverse jurisdictions. Moreover, the authors utilize insights from multiple related disciplines, in particular social theory and philosophy, in order to better understand and address the legal challenges created by AI. Therefore, the book will contribute to interdisciplinary debates on disruptive new AI technologies and the law. (shrink)

To analyze which ethically relevant biases have been identified by academic literature in artificial intelligence algorithms developed either for patient risk prediction and triage, or for contact tracing to deal with the COVID-19 pandemic. Additionally, to specifically investigate whether the role of social determinants of health have been considered in these AI developments or not. We conducted a scoping review of the literature, which covered publications from March 2020 to April 2021. ​Studies mentioning biases on AI algorithms developed for contact (...) tracing and medical triage or risk prediction regarding COVID-19 were included. From 1054 identified articles, 20 studies were finally included. We propose a typology of biases identified in the literature based on bias, limitations and other ethical issues in both areas of analysis. Results on health disparities and SDOH were classified into five categories: racial disparities, biased data, socio-economic disparities, unequal accessibility and workforce, and information communication. SDOH needs to be considered in the clinical context, where they still seem underestimated. Epidemiological conditions depend on geographic location, so the use of local data in studies to develop international solutions may increase some biases. Gender bias was not specifically addressed in the articles included. The main biases are related to data collection and management. Ethical problems related to privacy, consent, and lack of regulation have been identified in contact tracing while some bias-related health inequalities have been highlighted. There is a need for further research focusing on SDOH and these specific AI apps. (shrink)

As the capabilities of artificial intelligence (AI) continue to expand, concerns are also growing about the ethical and social consequences of unregulated development and, above all, use of AI systems in a wide range of social areas. It is therefore indisputable that the application of AI requires social standardization and regulation. For years, innovation policy measures and the most diverse activities of European and German institutions have been directed toward this goal. Under the label “Trustworthy AI” (TAI), a promise (...) is formulated, according to which AI can meet criteria of transparency, legality, privacy, non-discrimination, and reliability. In this article, we ask what significance and scope the politically initiated concepts of TAI occupy in the current process of AI dynamics and to what extent they can stand for an independent, unique European or German development path of this technology. (shrink)

This Chapter provides a short undergraduate introduction to ethical and philosophical complexities surrounding the law’s attempt (or lack thereof) to regulate artificial intelligence. -/- Swedish philosopher Nick Bostrom proposed a simple thought experiment known as the paperclip maximizer. What would happen if a machine (the “PCM”) were given the sole goal of manufacturing as many paperclips as possible? It might learn how to transact money, source metal, or even build factories. The machine might also eventually realize that humans pose a (...) threat. Humans could turn the machine off at any point, and then it wouldn’t be able to make as many paperclips as possible! Taken to the logical extreme, the result is quite grim—the PCM might even start using humans as raw material for paperclips. -/- The predicament only deepens once we realize that Bostrom’s thought experiment overlooks a key player. The PCM and algorithms like it do not arise spontaneously (at least, not yet). Most likely, some corporation—say, Office Corp.—designed, owns, and runs the PCM. The more paperclips the PCM manufactures, the more profits Office Corp. makes, even if that entails converting some humans (but preferably not customers!) into raw materials. Less dramatically, Office Corp. may also make more money when PCM engages in other socially sub-optimal behaviors that would otherwise violate the law, like money laundering, sourcing materials from endangered habitats, manipulating the market for steel, or colluding with competitors over prices. The consequences are predictable and dire. If Office Corp. isn’t held responsible, it will not stop with the PCM. Office Corp. would have every incentive to develop more maximizers—say for papers, pencils, and protractors. -/- This chapter issues a challenge for tech ethicists, social ontologists, and legal theorists: How can the law help mitigate algorithmic harms without overly compromising the potential that AI has to make us all healthier, wealthier, and wiser? The answer is far from straightforward. (shrink)

The Morphogenetic Régulation approach (MR) contributes to the Morphogenetic Approach by explaining the material and ideational origins of change and stasis in agency, structure, and culture. In this paper, we focus on the expressive quality of ideas and systemic persistence in three research projects. The first demystifies inclusive governance and its adverse impacts. It shows how, contrary to institutions of governance, inclusiveness is not simply a norm but actually the explication of corporate agents’ ideas about rational choice institutionalism which leads (...) to adverse impacts on vulnerable groups and ecologies known as adverse inclusion. The second investigates the role of ideas as adequacy and self-explication in guiding Palestinian actors’ actions towards the deepening of neoliberalization in Palestine. The third explains the relevance of the systemic persistence problematique for understanding how three juxtaposed themes – Web2, Web3, and Artificial Intelligence (AI) – are shaping the political economy and infrastructure of the Internet. (shrink)

With the rapid advances in science and technology, Artificial Intelligence (AI) has been developing exponentially and transforming the world in ways we could never have envisioned. Its applications...

The increasing availability of large‐scale, complex data has made research into how human genomes determine physiology in health and disease, as well as its application to drug development and medicine, an attractive field for artificial intelligence (AI) approaches. Looking at recent developments, we explore how such approaches interconnect and may conflict with needs for and notions of causal knowledge in molecular genetics and genomic medicine. We provide reasons to suggest that—while capable of generating predictive knowledge at unprecedented pace and scale—if (...) and how these approaches will be integrated with prevailing causal concepts will not only determine the future of scientific understanding and self‐conceptions in these fields. But these questions will also be key to develop differentiated policies, such as for education and regulation, in order to harness societal benefits of AI for genomic research and medicine. (shrink)

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

Organizations such as the EU High-Level Expert Group on AI and the IEEE have recently formulated ethical principles and (moral) values that should be adhered to in the design and deployment of artificial intelligence (AI). These include respect for autonomy, non-maleficence, fairness, transparency, explainability, and accountability. But how can we ensure and verify that an AI system actually respects these values? To help answer this question, I propose an account for determining when an AI system can be said to embody (...) certain values. This account understands embodied values as the result of design activities intended to embed those values in such systems. AI systems are here understood as a special kind of sociotechnical system that, like traditional sociotechnical systems, are composed of technical artifacts, human agents, and institutions but—in addition—contain artificial agents and certain technical norms that regulate interactions between artificial agents and other elements of the system. The specific challenges and opportunities of embedding values in AI systems are discussed, and some lessons for better embedding values in AI systems are drawn. (shrink)

Before embarking on a discussion of the regulation of artificial intelligence (AI), it is first necessary to define the subject matter regulated. Defining artificial intelligence is a difficult endeavour, and many definitions have been proposed over the years. Although more than 70 years have passed since it was adopted, the most convincing definition is still nonetheless that proposed by Turing; in any case, it is important to be mindful of the risk of anthropomorphising artificial intelligence, which may arise in (...) particular from its very definition. Once we have established the subject matter regulated, we must ask ourselves whether lawmakers should pursue an approach that seeks to regulate artificial intelligence as a whole, or whether by contrast they should regulate applications of artificial intelligence in specific sectors or individual areas. The proposal for a regulation on artificial intelligence published on 21 April 2021 implements the former approach whilst also pursuing geopolitical goals. After providing an initial overview of the notion of artificial intelligence, this article investigates the geopolitical context to the proposal for a regulation, and then goes on to illustrate the regulatory model embraced by the proposal as well as related critical aspects. (shrink)

The notion of trustworthy AI has been proposed in response to mounting public criticism of AI systems, in particular with regard to the proliferation of such systems into ever more sensitive areas of human life without proper checks and balances. In Europe, the High-Level Expert Group on Artificial Intelligence has recently presented its Ethics Guidelines for Trustworthy AI. To some, the guidelines are an important step for the governance of AI. To others, the guidelines distract effort from genuine AI (...) class='Hi'>regulation. In this chapter, we engage in a critical discussion of the concept of trustworthy AI by probing the concept both on theoretical and practical grounds, assessing its substance and the feasibility of its intent. We offer a concise overview of the guidelines and their vision for trustworthy AI and examine the conceptual underpinnings of trustworthy AI by considering how notions of 'trust' and 'trustworthiness' have been discussed in the philosophical literature. We then discuss several epistemic obstacles and moral requirements when striving to achieve trustworthy AI in practice before concluding with an argument in support of the establishment of a trustworthy AI culture that respects and protects foundational values. (shrink)

Since Chinese scholars are playing an increasingly important role in shaping the national landscape of discussion on AI ethics, understanding their ethical concerns and preferred solutions is essential for global cooperation on governance of AI. This article, therefore, provides the first elaborated analysis on the discourse on AI ethics in Chinese academia, via a systematic literature review. This article has three main objectives. to identify the most discussed ethical issues of AI in Chinese academia and those being left out ; (...) to analyze the solutions proposed and preferred by Chinese scholars ; and to map out whose voices are dominating and whose are in the marginal. Findings suggest that in terms of short-term implications, Chinese scholars’ concerns over AI resemble predominantly the content of international ethical guidelines. Yet in terms of long-term implications, there are some significant differences needed to be further addressed in a cultural context. Further, among a wide range of solution proposals, Chinese scholars seem to prefer strong-binding regulations to those weak ethical guidelines. In addition, this article also found that the Chinese academic discourse was dominated by male scholars and those who are from elite universities, which arguably is not a unique phenomenon in China. (shrink)

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

As suicide rates increase worldwide, the mental health industry has reached an impasse in attempts to assess patients, predict risk, and prevent suicide. Traditional assessment tools are no more accurate than chance, prompting the need to explore new avenues in artificial intelligence (AI). Early studies into these tools show potential with higher accuracy rates than previous methods alone. Medical researchers, computer scientists, and social media companies are exploring these avenues. While Facebook leads the pack, its efforts stem from scrutiny following (...) suicides and suicide attempts broadcasted on Facebook Live. The company continues to face scrutiny for ethical, privacy, and safety concerns associated with the quick rollout of proprietary AI technology that seems more focused on the optics of the company than the protection of its billions of users. This paper explores some of these issues, including a lack of transparency, questionable data practices, escalation to law enforcement, little to no regulation, potential for bias, increased viewership of troubling content, and worsening thoughts of suicide among vulnerable Facebook users. Despite AI tools showing promise in improving the accuracy of suicide risk predictions over traditional methods, without regulation, external review, and increased protection of users and data, these tools have the potential to cause more harm than good in the hands of powerful companies like Facebook. (shrink)

Machine‐learning algorithms have the potential to revolutionise diagnostic and prognostic tasks in health care, yet algorithmic performance levels can be materially worse for subgroups that have been underrepresented in algorithmic training data. Given this epistemic deficit, the inclusion of underrepresented groups in algorithmic processes can result in harm. Yet delaying the deployment of algorithmic systems until more equitable results can be achieved would avoidably and foreseeably lead to a significant number of unnecessary deaths in well‐represented populations. Faced with this dilemma (...) between equity and utility, we draw on two case studies involving breast cancer and melanoma to argue for the selective deployment of diagnostic and prognostic tools for some well‐represented groups, even if this results in the temporary exclusion of underrepresented patients from algorithmic approaches. We argue that this approach is justifiable when the inclusion of underrepresented patients would cause them to be harmed. While the context of historic injustice poses a considerable challenge for the ethical acceptability of selective algorithmic deployment strategies, we argue that, at least for the case studies addressed in this article, the issue of historic injustice is better addressed through nonalgorithmic measures, including being transparent with patients about the nature of the current epistemic deficits, providing additional services to algorithmically excluded populations, and through urgent commitments to gather additional algorithmic training data from excluded populations, paving the way for universal algorithmic deployment that is accurate for all patient groups. These commitments should be supported by regulation and, where necessary, government funding to ensure that any delays for excluded groups are kept to the minimum. We offer an ethical algorithm for algorithms—showing when to ethically delay, expedite, or selectively deploy algorithmic systems in healthcare settings. (shrink)

The book examines the extent to which Chinese cyber and network security laws and policies act as a constraint on the emergence of Chinese entrepreneurialism and innovation. Specifically, how the contradictions and tensions between data localisation laws affect innovation in artificial intelligence. The book surveys the globalised R&D networks, and how the increasing use of open-source platforms by leading Chinese AI firms during 2017–2020, exacerbated the apparent contradiction between Network Sovereignty and Chinese innovation. The drafting of the Cyber Security Law (...) did not anticipate the changing nature of globalised AI innovation. It is argued that the deliberate deployment of what the book refers to as 'fuzzy logic' in drafting the Cyber Security Law allowed regulators to subsequently interpret key terms regarding data in that Law in a fluid and flexible fashion to benefit Chinese innovation. (shrink)

In this paper, we argue that patients who are subjects of Artificial Intelligence (AI)-supported diagnosis and treatment planning should have a right to a second opinion, but also that this right should not necessarily be construed as a right to a physician opinion. The right to a second opinion could potentially be satisfied by another independent AI system. Our considerations on the right to second opinion are embedded in the wider debate on different approaches to the regulation of AI, (...) and we conclude the article by providing a number of reasons for preferring a rights-based approach over a risk-based approach. (shrink)

As the capabilities of Artificial Intelligence (AI) have increased over recent years, so have the challenges of how to govern its usage. Consequently, prominent stakeholders across academia, government, industry, and civil society have called for states to devise and deploy principles, innovative policies, and best practices to regulate and oversee these increasingly powerful AI tools. Developing a robust AI governance system requires extensive collective efforts throughout the world. It also raises old questions of politics, democracy, and administration, but with the (...) new challenges posed by AI's growing influence on markets, governing structures, international relations, healthcare, science, and political activism. The Oxford Handbook of AI Governance delineates the scope of these issues and addresses the key questions of AI governance. Across forty-nine chapters, organized in nine major sections, the Handbook covers the theoretical and ethical foundations of AI governance, different frameworks for developing a governance structure for AI, practical perspectives on AI governance in different policy domains, economic analyses of AI governance, and concrete lessons about the impact of AI governance domestically and internationally. Chapter authors come from a wide set of disciplines, areas of study, and cultural backgrounds, providing a global perspective on AI governance. (shrink)

AI systems play an increasingly important role in shaping and regulating the lives of millions of human beings across the world. Calls for greater _transparency_ from such systems have been widespread. However, there is considerable ambiguity concerning what “transparency” actually means, and therefore, what greater transparency might entail. While, according to some debates, transparency requires _seeing through_ the artefact or device, widespread calls for transparency imply _seeing into_ different aspects of AI systems. These two notions are in apparent tension with (...) each other, and they are present in two lively but largely disconnected debates. In this paper, we aim to further analyse what these calls for transparency entail, and in so doing, clarify the sorts of transparency that we should want from AI systems. We do so by offering a taxonomy that classifies different notions of transparency. After a careful exploration of the different varieties of transparency, we show how this taxonomy can help us to navigate various domains of human–technology interactions, and more usefully discuss the relationship between technological transparency and human agency. We conclude by arguing that all of these different notions of transparency should be taken into account when designing more ethically adequate AI systems. (shrink)

Artificial intelligence and automation have become pervasive in news media, influencing journalism from news gathering to news distribution. As algorithms are increasingly determining editorial decisions, specific concerns have been raised with regard to the responsible and accountable use of AI-driven tools by news media, encompassing new regulatory and ethical questions. This contribution aims to analyze whether and to what extent the use of AI technology in news media and journalism is currently regulated and debated within the European Union and the (...) Council of Europe. Through a document analysis of official policy documents, combined with a data mining approach and an inductive thematic analysis, the study looks at how news media are dealt with, in particular regarding their responsibilities towards their users and society. The findings show that regulatory frameworks about AI rarely include media, but if they do, they associate them with issues such as disinformation, data, and AI literacy, as well as diversity, plurality, and social responsibility. (shrink)