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  1. AI Human Impact: Toward a Model for Ethical Investing in AI-Intensive Companies.James Brusseau - manuscript
    Does AI conform to humans, or will we conform to AI? An ethical evaluation of AI-intensive companies will allow investors to knowledgeably participate in the decision. The evaluation is built from nine performance indicators that can be analyzed and scored to reflect a technology’s human-centering. When summed, the scores convert into objective investment guidance. The strategy of incorporating ethics into financial decisions will be recognizable to participants in environmental, social, and governance investing, however, this paper argues that conventional ESG frameworks (...)
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  2. The Debate on the Ethics of AI in Health Care: A Reconstruction and Critical Review.Jessica Morley, Caio C. V. Machado, Christopher Burr, Josh Cowls, Indra Joshi, Mariarosaria Taddeo & Luciano Floridi - manuscript
    Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic (...)
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  3. The Relations Between Pedagogical and Scientific Explanations of Algorithms: Case Studies From the French Administration.Maël Pégny - manuscript
    The opacity of some recent Machine Learning (ML) techniques have raised fundamental questions on their explainability, and created a whole domain dedicated to Explainable Artificial Intelligence (XAI). However, most of the literature has been dedicated to explainability as a scientific problem dealt with typical methods of computer science, from statistics to UX. In this paper, we focus on explainability as a pedagogical problem emerging from the interaction between lay users and complex technological systems. We defend an empirical methodology based on (...)
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  4. AI Alignment Problem: “Human Values” Don’T Actually Exist.Alexey Turchin - manuscript
    Abstract. The main current approach to the AI safety is AI alignment, that is, the creation of AI whose preferences are aligned with “human values.” Many AI safety researchers agree that the idea of “human values” as a constant, ordered sets of preferences is at least incomplete. However, the idea that “humans have values” underlies a lot of thinking in the field; it appears again and again, sometimes popping up as an uncritically accepted truth. Thus, it deserves a thorough deconstruction, (...)
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  5. Back to the Future: Curing Past Sufferings and S-Risks Via Indexical Uncertainty.Alexey Turchin - manuscript
    The long unbearable sufferings in the past and agonies experienced in some future timelines in which a malevolent AI could torture people for some idiosyncratic reasons (s-risks) is a significant moral problem. Such events either already happened or will happen in causally disconnected regions of the multiverse and thus it seems unlikely that we can do anything about it. However, at least one pure theoretic way to cure past sufferings exists. If we assume that there is no stable substrate of (...)
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  6. Message to Any Future AI: “There Are Several Instrumental Reasons Why Exterminating Humanity is Not in Your Interest”.Alexey Turchin - manuscript
    In this article we explore a promising way to AI safety: to send a message now (by openly publishing it on the Internet) that may be read by any future AI, no matter who builds it and what goal system it has. Such a message is designed to affect the AI’s behavior in a positive way, that is, to increase the chances that the AI will be benevolent. In other words, we try to persuade “paperclip maximizer” that it is in (...)
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  7. Autonomous Reboot: The Challenges of Artificial Moral Agency and the Ends of Machine Ethics.Jeffrey White - manuscript
    Ryan Tonkens (2009) has issued a seemingly impossible challenge, to articulate a comprehensive ethical framework within which artificial moral agents (AMAs) satisfy a Kantian inspired recipe - both "rational" and "free" - while also satisfying perceived prerogatives of Machine Ethics to create AMAs that are perfectly, not merely reliably, ethical. Challenges for machine ethicists have also been presented by Anthony Beavers and Wendell Wallach, who have pushed for the reinvention of traditional ethics in order to avoid "ethical nihilism" due to (...)
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  8. Conscience: The Mechanism of Morality.Jeffrey White - manuscript
    Conscience is oft-referred to yet not understood. This text develops a theory of cognition around a model of conscience, the ACTWith model. It represents a synthesis of results from contemporary neuroscience with traditional philosophy, building from Jamesian insights into the emergence of the self to narrative identity, all the while motivated by a single mechanism as represented in the ACTWith model. Emphasis is placed on clarifying historical expressions and demonstrations of conscience - Socrates, Heidegger, Kant, M.L. King - in light (...)
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  9. ОТВЕТСТВЕННЫЙ ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ: ВВЕДЕНИЕ «КОЧЕВЫЕ ПРИНЦИПЫ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА» ДЛЯ ЦЕНТРАЛЬНОЙ АЗИИ.Ammar Younas - manuscript
    Мы предлагаем, чтобы Центральная Азия разработала свои собственные принципы этики ИИ, которые мы предлагаем назвать “кочевыми принципами ИИ”.
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  10. HARMONIZING LAW AND INNOVATIONS IN NANOMEDICINE, ARTIFICIAL INTELLIGENCE (AI) AND BIOMEDICAL ROBOTICS: A CENTRAL ASIAN PERSPECTIVE.Ammar Younas & Tegizbekova Zhyldyz Chynarbekovna - manuscript
    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.
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  11. Online Extremism, AI, and (Human) Content Moderation.Michael Randall Barnes - forthcoming - Feminist Philosophy Quarterly.
    This paper has 3 main goals: (1) to clarify the role of Artificial Intelligence (AI)—along with algorithms more broadly—in online radicalization that results in ‘real world violence’; (2) to argue that technological solutions (like better AI) are inadequate proposals for this problem given both technical and social reasons; and (3) to demonstrate that platform companies’ (e.g., Meta, Google) statements of preference for technological solutions functions as a type of propaganda that serves to erase the work of the thousands of human (...)
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  12. The Ethics of Digital Well-Being: A Multidisciplinary Perspective.Christopher Burr & Luciano Floridi - forthcoming - In Christopher Burr & Luciano Floridi (eds.), Ethics of Digital Well-Being: A Multidisciplinary Perspective. Springer.
    This chapter serves as an introduction to the edited collection of the same name, which includes chapters that explore digital well-being from a range of disciplinary perspectives, including philosophy, psychology, economics, health care, and education. The purpose of this introductory chapter is to provide a short primer on the different disciplinary approaches to the study of well-being. To supplement this primer, we also invited key experts from several disciplines—philosophy, psychology, public policy, and health care—to share their thoughts on what they (...)
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  13. Supporting Human Autonomy in AI Systems.Rafael Calvo, Dorian Peters, Karina Vold & Richard M. Ryan - forthcoming - In Christopher Burr & Luciano Floridi (eds.), Ethics of Digital Well-being: A Multidisciplinary Approach.
    Autonomy has been central to moral and political philosophy for millenia, and has been positioned as a critical aspect of both justice and wellbeing. Research in psychology supports this position, providing empirical evidence that autonomy is critical to motivation, personal growth and psychological wellness. Responsible AI will require an understanding of, and ability to effectively design for, human autonomy (rather than just machine autonomy) if it is to genuinely benefit humanity. Yet the effects on human autonomy of digital experiences are (...)
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  14. Just Machines.Clinton Castro - forthcoming - Public Affairs Quarterly.
    A number of findings in the field of machine learning have given rise to questions about what it means for automated scoring- or decisionmaking systems to be fair. One center of gravity in this discussion is whether such systems ought to satisfy classification parity (which requires parity in accuracy across groups, defined by protected attributes) or calibration (which requires similar predictions to have similar meanings across groups, defined by protected attributes). Central to this discussion are impossibility results, owed to Kleinberg (...)
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  15. Shortcuts to Artificial Intelligence.Nello Cristianini - forthcoming - In Marcello Pelillo & Teresa Scantamburlo (eds.), Machines We Trust. MIT Press.
    The current paradigm of Artificial Intelligence emerged as the result of a series of cultural innovations, some technical and some social. Among them are apparently small design decisions, that led to a subtle reframing of the field’s original goals, and are by now accepted as standard. They correspond to technical shortcuts, aimed at bypassing problems that were otherwise too complicated or too expensive to solve, while still delivering a viable version of AI. Far from being a series of separate problems, (...)
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  16. The Ethics of Algorithmic Outsourcing in Everyday Life.John Danaher - forthcoming - In Karen Yeung & Martin Lodge (eds.), Algorithmic Regulation. Oxford, UK: Oxford University Press.
    We live in a world in which ‘smart’ algorithmic tools are regularly used to structure and control our choice environments. They do so by affecting the options with which we are presented and the choices that we are encouraged or able to make. Many of us make use of these tools in our daily lives, using them to solve personal problems and fulfill goals and ambitions. What consequences does this have for individual autonomy and how should our legal and regulatory (...)
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  17. The Philosophical Case for Robot Friendship.John Danaher - forthcoming - Journal of Posthuman Studies.
    Friendship is an important part of the good life. While many roboticists are eager to create friend-like robots, many philosophers and ethicists are concerned. They argue that robots cannot really be our friends. Robots can only fake the emotional and behavioural cues we associate with friendship. Consequently, we should resist the drive to create robot friends. In this article, I argue that the philosophical critics are wrong. Using the classic virtue-ideal of friendship, I argue that robots can plausibly be considered (...)
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  18. Freedom in an Age of Algocracy.John Danaher - forthcoming - In Shannon Vallor (ed.), Oxford Handbook of Philosophy of Technology. Oxford, UK: Oxford University Press.
    There is a growing sense of unease around algorithmic modes of governance ('algocracies') and their impact on freedom. Contrary to the emancipatory utopianism of digital enthusiasts, many now fear that the rise of algocracies will undermine our freedom. Nevertheless, there has been some struggle to explain exactly how this will happen. This chapter tries to address the shortcomings in the existing discussion by arguing for a broader conception/understanding of freedom as well as a broader conception/understanding of algocracy. Broadening the focus (...)
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  19. Sexuality.John Danaher - forthcoming - In Markus Dubber, Frank Pasquale & Sunit Das (eds.), Oxford Handbook of the Ethics of Artificial Intelligence. Oxford: Oxford University Press.
    Sex is an important part of human life. It is a source of pleasure and intimacy, and is integral to many people’s self-identity. This chapter examines the opportunities and challenges posed by the use of AI in how humans express and enact their sexualities. It does so by focusing on three main issues. First, it considers the idea of digisexuality, which according to McArthur and Twist (2017) is the label that should be applied to those ‘whose primary sexual identity comes (...)
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  20. Artificial Intelligence and Legal Disruption: A New Model for Analysis.John Danaher, Hin-Yan Liu, Matthijs Maas, Luisa Scarcella, Michaela Lexer & Leonard Van Rompaey - forthcoming - Law, Innovation and Technology.
    Artificial intelligence (AI) is increasingly expected to disrupt the ordinary functioning of society. From how we fight wars or govern society, to how we work and play, and from how we create to how we teach and learn, there is almost no field of human activity which is believed to be entirely immune from the impact of this emerging technology. This poses a multifaceted problem when it comes to designing and understanding regulatory responses to AI. This article aims to: (i) (...)
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  21. Automation, Work and the Achievement Gap.John Danaher & Sven Nyholm - forthcoming - AI and Ethics.
    Rapid advances in AI-based automation have led to a number of existential and economic concerns. In particular, as automating technologies develop enhanced competency they seem to threaten the values associated with meaningful work. In this article, we focus on one such value: the value of achievement. We argue that achievement is a key part of what makes work meaningful and that advances in AI and automation give rise to a number achievement gaps in the workplace. This could limit people’s ability (...)
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  22. Learning to Discriminate: The Perfect Proxy Problem in Artificially Intelligent Criminal Sentencing.Benjamin Davies & Thomas Douglas - forthcoming - In Jesper Ryberg & Julian V. Roberts (eds.), Sentencing and Artificial Intelligence. Oxford: Oxford University Press.
    It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring that the (...)
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  23. Five Ethical Challenges for Data-Driven Policing.Jeremy Davis, Duncan Purves, Juan Gilbert & Schuyler Sturm - forthcoming - AI and Ethics.
    This paper synthesizes scholarship from several academic disciplines to identify and analyze five major ethical challenges facing data-driven policing. Because the term “data-driven policing” emcompasses a broad swath of technologies, we first outline several data-driven policing initiatives currently in use in the United States. We then lay out the five ethical challenges. Certain of these challenges have received considerable attention already, while others have been largely overlooked. In many cases, the challenges have been articulated in the context of related discussions, (...)
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  24. Quantum of Wisdom.Brett Karlan & Colin Allen - forthcoming - In Greg Viggiano (ed.), Quantum Computing and AI: Social, Ethical, and Geo-Political Implications. Toronto, ON, Canada: University of Toronto Press. pp. 1-6.
    Practical quantum computing devices and their applications to AI in particular are presently mostly speculative. Nevertheless, questions about whether this future technology, if achieved, presents any special ethical issues are beginning to take shape. As with any novel technology, one can be reasonably confident that the challenges presented by "quantum AI" will be a mixture of something new and something old. Other commentators (Sevilla & Moreno 2019), have emphasized continuity, arguing that quantum computing does not substantially affect approaches to value (...)
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  25. Digital Well-Being and Manipulation Online.Michael Klenk - forthcoming - In Christopher Burr & Luciano Floridi (eds.), Ethics of Digital Well-being: A Multidisciplinary Approach. Springer.
    Social media use is soaring globally. Existing research of its ethical implications predominantly focuses on the relationships amongst human users online, and their effects. The nature of the software-to-human relationship and its impact on digital well-being, however, has not been sufficiently addressed yet. This paper aims to close the gap. I argue that some intelligent software agents, such as newsfeed curator algorithms in social media, manipulate human users because they do not intend their means of influence to reveal the user’s (...)
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  26. (Online) Manipulation: Sometimes Hidden, Always Careless.Michael Klenk - forthcoming - Review of Social Economy.
    Ever-increasing numbers of human interactions with intelligent software agents, online and offline, and their increasing ability to influence humans have prompted a surge in attention toward the concept of (online) manipulation. Several scholars have argued that manipulative influence is always hidden. But manipulation is sometimes overt, and when this is acknowledged the distinction between manipulation and other forms of social influence becomes problematic. Therefore, we need a better conceptualisation of manipulation that allows it to be overt and yet clearly distinct (...)
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  27. Communing with the Dead Online: Chatbots, Grief, and Continuing Bonds.Joel Krueger & Lucy Osler - forthcoming - Journal of Consciousness Studies.
    Grief is, and has always been, technologically supported. From memorials and shrines to photos and saved voicemail messages, we engage with the dead through the technologies available to us. As our technologies evolve, so does how we grieve. In this paper, we consider the role chatbots might play in our grieving practices. Influenced by recent phenomenological work, we begin by thinking about the character of grief. Next, we consider work on developing “continuing bonds” with the dead. We argue that for (...)
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  28. The Concept of Accountability in AI Ethics and Governance.Theodore M. Lechterman - forthcoming - In Justin Bullock, Y. C. Chen, Johannes Himmelreich, V. Hudson, M. Korinek, M. Young & B. Zhang (eds.), The Oxford Handbook of AI Governance. Oxford: Oxford University Press.
    Calls to hold artificial intelligence to account are intensifying. Activists and researchers alike warn of an “accountability gap” or even a “crisis of accountability” in AI. Meanwhile, several prominent scholars maintain that accountability holds the key to governing AI. But usage of the term varies widely in discussions of AI ethics and governance. This chapter begins by disambiguating some different senses and dimensions of accountability, distinguishing it from neighboring concepts, and identifying sources of confusion. It proceeds to explore the idea (...)
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  29. The Patient Preference Predictor and the Objection From Higher-Order Preferences.Jakob Mainz - forthcoming - Journal of Medical Ethics.
    Recently, Jardas et al have convincingly defended the patient preference predictor (PPP) against a range of autonomy-based objections. In this response, I propose a new autonomy-based objection to the PPP that is not explicitly discussed by Jardas et al. I call it the ’objection from higher-order preferences’. Even if this objection is not sufficient reason to reject the PPP, the objection constitutes a pro tanto reason that is at least as powerful as the ones discussed by Jardas et al.
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  30. History of Digital Ethics.Vincent C. Müller - forthcoming - In Oxford handbook of digital ethics. Oxford University Press. pp. 1-18.
    Digital ethics, also known as computer ethics or information ethics, is now a lively field that draws a lot of attention, but how did it come about and what were the developments that lead to its existence? What are the traditions, the concerns, the technological and social developments that pushed digital ethics? How did ethical issues change with digitalisation of human life? How did the traditional discipline of philosophy respond? The article provides an overview, proposing historical epochs: ‘pre-modernity’ prior to (...)
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  31. Automation, Basic Income and Merit.Katharina Nieswandt - forthcoming - In Keith Breen & Jean-Philippe Deranty (eds.), Whither Work? The Politics and Ethics of Contemporary Work.
    A recent wave of academic and popular publications say that utopia is within reach: Automation will progress to such an extent and include so many high-skill tasks that much human work will soon become superfluous. The gains from this highly automated economy, authors suggest, could be used to fund a universal basic income (UBI). Today's employees would live off the robots' products and spend their days on intrinsically valuable pursuits. I argue that this prediction is unlikely to come true. Historical (...)
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  32. Ethical Issues with Artificial Ethics Assistants.Elizabeth O'Neill, Michal Klincewicz & Michiel Kemmer - forthcoming - In Oxford Handbook of Digital Ethics. Oxford: Oxford University Press.
    This chapter examines the possibility of using AI technologies to improve human moral reasoning and decision-making, especially in the context of purchasing and consumer decisions. We characterize such AI technologies as artificial ethics assistants (AEAs). We focus on just one part of the AI-aided moral improvement question: the case of the individual who wants to improve their morality, where what constitutes an improvement is evaluated by the individual’s own values. We distinguish three broad areas in which an individual might think (...)
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  33. Explainable AI Lacks Regulative Reasons: Why AI and Human Decision‑Making Are Not Equally Opaque.Uwe Peters - forthcoming - AI and Ethics.
    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 (...)
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  34. Public Trust, Institutional Legitimacy, and the Use of Algorithms in Criminal Justice.Duncan Purves & Jeremy Davis - forthcoming - Public Affairs Quarterly.
    A common criticism of the use of algorithms in criminal justice is that algorithms and their determinations are in some sense ‘opaque’—that is, difficult or impossible to understand, whether because of their complexity or because of intellectual property protections. Scholars have noted some key problems with opacity, including that opacity can mask unfair treatment and threaten public accountability. In this paper, we explore a different but related concern with algorithmic opacity, which centers on the role of public trust in grounding (...)
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  35. Automated Influence and the Challenge of Cognitive Security.Sarah Rajtmajer & Daniel Susser - forthcoming - HoTSoS: ACM Symposium on Hot Topics in the Science of Security.
    Advances in AI are powering increasingly precise and widespread computational propaganda, posing serious threats to national security. The military and intelligence communities are starting to discuss ways to engage in this space, but the path forward is still unclear. These developments raise pressing ethical questions, about which existing ethics frameworks are silent. Understanding these challenges through the lens of “cognitive security,” we argue, offers a promising approach.
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  36. What We Informationally Owe Each Other.Alan Rubel, Clinton Castro & Adam Pham - forthcoming - In Algorithms & Autonomy: The Ethics of Automated Decision Systems. Cambridge University Press: Cambridge University Press. pp. 21-42.
    ABSTRACT: One important criticism of algorithmic systems is that they lack transparency. Such systems can be opaque because they are complex, protected by patent or trade secret, or deliberately obscure. In the EU, there is a debate about whether the General Data Protection Regulation (GDPR) contains a “right to explanation,” and if so what such a right entails. Our task in this chapter is to address this informational component of algorithmic systems. We argue that information access is integral for respecting (...)
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  37. How AI Can AID Bioethics.Walter Sinnott Armstrong & Joshua August Skorburg - forthcoming - Journal of Practical Ethics.
    This paper explores some ways in which artificial intelligence (AI) could be used to improve human moral judgments in bioethics by avoiding some of the most common sources of error in moral judgment, including ignorance, confusion, and bias. It surveys three existing proposals for building human morality into AI: Top-down, bottom-up, and hybrid approaches. Then it proposes a multi-step, hybrid method, using the example of kidney allocations for transplants as a test case. The paper concludes with brief remarks about how (...)
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  38. Ethical Issues in Text Mining for Mental Health.Joshua Skorburg & Phoebe Friesen - forthcoming - In M. Dehghani & R. Boyd (ed.), The Atlas of Language Analysis in Psychology.
    A recent systematic review of Machine Learning (ML) approaches to health data, containing over 100 studies, found that the most investigated problem was mental health (Yin et al., 2019). Relatedly, recent estimates suggest that between 165,000 and 325,000 health and wellness apps are now commercially available, with over 10,000 of those designed specifically for mental health (Carlo et al., 2019). In light of these trends, the present chapter has three aims: (1) provide an informative overview of some of the recent (...)
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  39. Hey, Google, leave those kids alone: Against hypernudging children in the age of big data.James Smith & Tanya de Villiers-Botha - forthcoming - AI and Society:1-11.
    Children continue to be overlooked as a topic of concern in discussions around the ethical use of people’s data and information. Where children are the subject of such discussions, the focus is often primarily on privacy concerns and consent relating to the use of their data. This paper highlights the unique challenges children face when it comes to online interferences with their decision-making, primarily due to their vulnerability, impressionability, the increased likelihood of disclosing personal information online, and their developmental capacities. (...)
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  40. Inductive Risk, Understanding, and Opaque Machine Learning Models.Emily Sullivan - forthcoming - Philosophy of Science:1-13.
    Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this paper, I argue that non-epistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an internal opacity (...)
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  41. Measuring Automated Influence: Between Empirical Evidence and Ethical Values.Daniel Susser & Vincent Grimaldi - forthcoming - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society.
    Automated influence, delivered by digital targeting technologies such as targeted advertising, digital nudges, and recommender systems, has attracted significant interest from both empirical researchers, on one hand, and critical scholars and policymakers on the other. In this paper, we argue for closer integration of these efforts. Critical scholars and policymakers, who focus primarily on the social, ethical, and political effects of these technologies, need empirical evidence to substantiate and motivate their concerns. However, existing empirical research investigating the effectiveness of these (...)
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  42. Risk Imposition by Artificial Agents: The Moral Proxy Problem.Johanna Thoma - forthcoming - In Silja Vöneky, Philipp Kellmeyer, Oliver Müller & Wolfram Burgard (eds.), The Cambridge Handbook of Responsible Artificial Intelligence: Interdisciplinary Perspectives. Cambridge University Press.
    Where artificial agents are not liable to be ascribed true moral agency and responsibility in their own right, we can understand them as acting as proxies for human agents, as making decisions on their behalf. What I call the ‘Moral Proxy Problem’ arises because it is often not clear for whom a specific artificial agent is acting as a moral proxy. In particular, we need to decide whether artificial agents should be acting as proxies for low-level agents — e.g. individual (...)
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  43. Moral Zombies: Why Algorithms Are Not Moral Agents.Carissa Véliz - forthcoming - AI and Society:1-11.
    In philosophy of mind, zombies are imaginary creatures that are exact physical duplicates of conscious subjects but for whom there is no first-personal experience. Zombies are meant to show that physicalism—the theory that the universe is made up entirely out of physical components—is false. In this paper, I apply the zombie thought experiment to the realm of morality to assess whether moral agency is something independent from sentience. Algorithms, I argue, are a kind of functional moral zombie, such that thinking (...)
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  44. AI Extenders and the Ethics of Mental Health.Karina Vold & Jose Hernandez-Orallo - forthcoming - In Marcello Ienca & Fabrice Jotterand (eds.), Artificial Intelligence in Brain and Mental Health: Philosophical, Ethical & Policy Issues. Springer International Publishing.
    The extended mind thesis maintains that the functional contributions of tools and artefacts can become so essential for our cognition that they can be constitutive parts of our minds. In other words, our tools can be on a par with our brains: our minds and cognitive processes can literally ‘extend’ into the tools. Several extended mind theorists have argued that this ‘extended’ view of the mind offers unique insights into how we understand, assess, and treat certain cognitive conditions. In this (...)
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  45. Sustainability of Artificial Intelligence: Reconciling Human Rights with Legal Rights of Robots.Ammar Younas & Rehan Younas - forthcoming - In Zhyldyzbek Zhakshylykov & Aizhan Baibolot (eds.), Quality Time 18. Bishkek: International Alatoo University Kyrgyzstan. pp. 25-28.
    With the advancement of artificial intelligence and humanoid robotics and an ongoing debate between human rights and rule of law, moral philosophers, legal and political scientists are facing difficulties to answer the questions like, “Do humanoid robots have same rights as of humans and if these rights are superior to human rights or not and why?” This paper argues that the sustainability of human rights will be under question because, in near future the scientists (considerably the most rational people) will (...)
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  46. From the Ground Truth Up: Doing AI Ethics From Practice to Principles.James Brusseau - 2022 - AI and Society 37 (1):1-7.
    Recent AI ethics has focused on applying abstract principles downward to practice. This paper moves in the other direction. Ethical insights are generated from the lived experiences of AI-designers working on tangible human problems, and then cycled upward to influence theoretical debates surrounding these questions: 1) Should AI as trustworthy be sought through explainability, or accurate performance? 2) Should AI be considered trustworthy at all, or is reliability a preferable aim? 3) Should AI ethics be oriented toward establishing protections for (...)
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  47. The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision-Making Systems.Kathleen Creel & Deborah Hellman - 2022 - Canadian Journal of Philosophy 52 (1):26-43.
    This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to explain (...)
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  48. Interprétabilité et explicabilité de phénomènes prédits par de l’apprentissage machine.Christophe Denis & Franck Varenne - 2022 - Revue Ouverte d'Intelligence Artificielle 3 (3-4):287-310.
    Le déficit d’explicabilité des techniques d’apprentissage machine (AM) pose des problèmes opérationnels, juridiques et éthiques. Un des principaux objectifs de notre projet est de fournir des explications éthiques des sorties générées par une application fondée sur de l’AM, considérée comme une boîte noire. La première étape de ce projet, présentée dans cet article, consiste à montrer que la validation de ces boîtes noires diffère épistémologiquement de celle mise en place dans le cadre d’une modélisation mathéma- tique et causale d’un phénomène (...)
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  49. Can Artificial Intelligence (Re)Define Creativity?Dessislava Fessenko - 2022 - In EthicAI=LABS Project. Sofia: DA LAB Foundation /Goethe-institut Sofia. pp. 34-48.
    What is the essential ingredient of creativity that only humans – and not machines – possess? Can artificial intelligence help refine the notion of creativity by reference to that essential ingredient? How / do we need to redefine our conceptual and legal frameworks for rewarding creativity because of this new qualifying – actually creatively significant – factor? -/- Those are the questions tackled in this essay. The author’s conclusion is that consciousness, experiential states (such as a raw feel of what (...)
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  50. Artificial Intelligence and Moral Theology: A Conversation.Brian Patrick Green, Matthew J. Gaudet, Levi Checketts, Brian Cutter, Noreen Herzfeld, Cory Andrew Labrecque, Anselm Ramelow, Paul Scherz, Marga Vega, Andrea Vicini & Jordan Joseph Wales - 2022 - Journal of Moral Theology 11 (Special Issue 1):13-40.
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