About this topic
Summary Ethical issues associated with AI are proliferating and rising to popular attention as machines engineered to perform tasks traditionally requiring biological intelligence become ubiquitous. Consider that civil infrastructure including energy grids and mass-transit systems are increasingly moderated by increasingly intelligent machines. Ethical issues include those of responsibility and/or blameworthiness of such systems, with implications for engineers who must responsibly design them, and philosophers who must interpret impacts - both potential and actual - in order to advise ethical designers. For example, who or what is responsible in the case of an accident due to an AI system error, or due to design flaws, or due to proper operation outside of anticipated constraints, such as part of a semi-autonomous automobile or actuarial algorithm? These are issues falling under the heading of Ethics of AI, as well as to other categories, e.g. those dedicated to autonomous vehicles, algorithmic fairness or artificial system safety. Finally, as AIs become increasingly intelligent, there seems some legitimate concern over the potential for AIs to manage human systems according to AI values, rather than as directly programmed by human designers. These concerns call into question the long-term safety of intelligent systems, not only for individual human beings, but for the human race and life on Earth as a whole. These issues and many others are central to Ethics of AI, and works focusing on such ideas can be found here. 
Key works Some works: Bostrom manuscriptMüller 2014, Müller 2016, Etzioni & Etzioni 2017, Dubber et al 2020, Tasioulas 2019, Müller 2021
Introductions Müller 2013, Gunkel 2012, Coeckelbergh 2020, Gordon et al 2021, Müller 2022Jecker & Nakazawa 2022, Mao & Shi-Kupfer 2023, Dietrich et al 2021, see also  https://plato.stanford.edu/entries/ethics-ai/
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  1. Idealism, realism, pragmatism: three modes of theorising within secular AI ethics.Rune Nyrup & Beba Cibralic - 2024 - In Barry Solemain & I. Glenn Cohen (eds.), Research Handbook on Health, AI and the Law. Edward Edgar Publishing. pp. 203-2018.
    Healthcare applications of AI have the potential to produce great benefit, but also come with significant ethical risks. This has brought ethics to the forefront of academic, policy and public debates about AI in healthcare. To help navigate these debates, we distinguish three general modes of ethical theorizing in contemporary secular AI ethics: (1) idealism, which seeks to articulate moral ideals that can be applied to concrete problems; (2) realism, which focuses on understanding complex social realities that underpin ethical problems; (...)
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  2. Research Handbook on Health, AI and the Law.Barry Solemain & I. Glenn Cohen (eds.) - 2024 - Edward Edgar Publishing.
    The Research Handbook on Health, AI and the Law explores the use of AI in healthcare, identifying the important laws and ethical issues that arise from its use. Adopting an international approach, it analyses the varying responses of multiple jurisdictions to the use of AI and examines the influence of major religious and secular ethical traditions.
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  3. Reliability Gaps Between Groups in COMPAS Dataset.Tim Räz - 2024 - In - Acm (ed.), FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. New York NY United States: Association for Computing Machinery. pp. 113–126.
  4. Disciplining Deliberation: A Sociotechnical Perspective on Machine Learning Trade-offs.Sina Fazelpour - manuscript
    This paper focuses on two highly publicized formal trade-offs in the field of responsible artificial intelligence (AI) -- between predictive accuracy and fairness and between predictive accuracy and interpretability. These formal trade-offs are often taken by researchers, practitioners, and policy-makers to directly imply corresponding tensions between underlying values. Thus interpreted, the trade-offs have formed a core focus of normative engagement in AI governance, accompanied by a particular division of labor along disciplinary lines. This paper argues against this prevalent interpretation by (...)
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  5. Aligning artificial intelligence with moral intuitions: an intuitionist approach to the alignment problem.Dario Cecchini, Michael Pflanzer & Veljko Dubljevic - 2024 - AI and Ethics:1-11.
    As artificial intelligence (AI) continues to advance, one key challenge is ensuring that AI aligns with certain values. However, in the current diverse and democratic society, reaching a normative consensus is complex. This paper delves into the methodological aspect of how AI ethicists can effectively determine which values AI should uphold. After reviewing the most influential methodologies, we detail an intuitionist research agenda that offers guidelines for aligning AI applications with a limited set of reliable moral intuitions, each underlying a (...)
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  6. Automated Influence and Value Collapse: Resisting the Control Argument.Dylan J. White - forthcoming - American Philosophical Quarterly.
    Automated influence is one of the most pervasive applications of artificial intelligence in our day-to-day lives, yet a thoroughgoing account of its associated individual and societal harms is lacking. By far the most widespread, compelling, and intuitive account of the harms associated with automated influence follows what I call the control argument. This argument suggests that users are persuaded, manipulated, and influenced by automated influence in a way that they have little or no control over. Based on evidence about the (...)
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  7. Computers will not acquire general intelligence, but may still rule the world.Ragnar Fjelland - 2024 - Cosmos+Taxis 12 (5+6):58-68.
    Jobst Langrebe’s and Barry Smith’s book Why Machines Will Never Rule the World argues that artificial general intelligence (AGI) will never be realized. Drawing on theories of complexity they argue that it is not only technically, but mathematically impossible to realize AGI. The book is the result of cooperation between a philosopher and a mathematician. In addition to a thorough treatment of mathematical modelling of complex systems the book addresses many fundamental philosophical questions. The authors show that philosophy is still (...)
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  8. Towards a Feminist Metaethics of AI.Anastasia Siapka - 2022 - Aies '22: Proceedings of the 2022 Aaai/Acm Conference on Ai, Ethics, and Society:665–674.
    The proliferation of Artificial Intelligence (AI) has sparked an overwhelming number of AI ethics guidelines, boards and codes of conduct. These outputs primarily analyse competing theories, principles and values for AI development and deployment. However, as a series of recent problematic incidents about AI ethics/ethicists demonstrate, this orientation is insufficient. Before proceeding to evaluate other professions, AI ethicists should critically evaluate their own; yet, such an evaluation should be more explicitly and systematically undertaken in the literature. I argue that these (...)
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  9. Introduction: Affectivity and Technology - Philosophical Explorations.Giulia Piredda, Richard Heersmink & Marco Fasoli - 2024 - Topoi 43 (3):1-6.
    In connecting embodied, embedded, extended, and enactive (4E) cognition with affectivity and emotions, the framework of “situated affectivity” has recently emerged. This framework emphasizes the interactions between the emoter and the environment in the unfolding of our affective lives (Colombetti and Krueger 2015; Griffiths and Scarantino 2009; Piredda 2022; Stephan and Walter 2020). In the last decades, there has also been a growing interest in the philosophical analysis of technology and artifacts (Houkes and Vermaas 2010; Margolis and Laurence 2007; Preston (...)
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  10. Il futuro della tecnologia. Progettazione etica e innovazione.Steven Umbrello - forthcoming - Roma: tab edizioni.
    Il volume approfondisce le intricate narrazioni che danno forma alla nostra comprensione della tecnologia, dai punti di vista strumentali al costruttivismo sociale, sostenendo l'interazionismo come l’interpretazione più promettente. Man mano che l’umanità intreccia nodi sempre più stretti con la tecnologia, la comprensione e l'attuazione di questi principi diventa non solo vantaggiosa ma addirittura essenziale. Ecco allora che l’articolazione del libro, dimostrata attraverso esempi e narrazioni avvincenti, svela le sfumature delle diverse posizioni filosofiche e suggerisce un chiaro percorso da seguire: i (...)
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  11. Attributions toward Artificial Agents in a modified Moral Turing Test.Eyal Aharoni, Sharlene Fernandes, Daniel Brady, Caelan Alexander, Michael Criner, Kara Queen, Javier Rando, Eddy Nahmias & Victor Crespo - 2024 - Scientific Reports 14 (8458):1-11.
    Advances in artificial intelligence (AI) raise important questions about whether people view moral evaluations by AI systems similarly to human-generated moral evaluations. We conducted a modified Moral Turing Test (m-MTT), inspired by Allen et al. (Exp Theor Artif Intell 352:24–28, 2004) proposal, by asking people to distinguish real human moral evaluations from those made by a popular advanced AI language model: GPT-4. A representative sample of 299 U.S. adults first rated the quality of moral evaluations when blinded to their source. (...)
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  12. Existentialist risk and value misalignment.Ariela Tubert & Justin Tiehen - forthcoming - Philosophical Studies.
    We argue that two long-term goals of AI research stand in tension with one another. The first involves creating AI that is safe, where this is understood as solving the problem of value alignment. The second involves creating artificial general intelligence, meaning AI that operates at or beyond human capacity across all or many intellectual domains. Our argument focuses on the human capacity to make what we call “existential choices”, choices that transform who we are as persons, including transforming what (...)
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  13. Machine agency and representation.Beba Cibralic & James Mattingly - 2024 - AI and Society 39 (1):345-352.
    Theories of action tend to require agents to have mental representations. A common trope in discussions of artificial intelligence (AI) is that they do not, and so cannot be agents. Properly understood there may be something to the requirement, but the trope is badly misguided. Here we provide an account of representation for AI that is sufficient to underwrite attributions to these systems of ownership, action, and responsibility. Existing accounts of mental representation tend to be too demanding and unparsimonious. We (...)
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  14. When the Digital Continues After Death Ethical Perspectives on Death Tech and the Digital Afterlife.Anna Puzio - 2023 - Communicatio Socialis 56 (3):427-436.
    Nothing seems as certain as death. However, what if life continues digitally after death? Companies and initiatives such as Amazon, Storyfile, Here After AI, Forever Identity and LifeNaut are dedicated to precisely this objective: using avatars, records, and other digital content of the deceased, they strive to enable a digital continuation of life. The deceased live on digitally, and at times, these can even appear very much alive-perhaps too alive? This article explores the ethical implications of these technologies, commonly known (...)
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  15. Artificial Intelligence and an Anthropological Ethics of Work: Implications on the Social Teaching of the Church.Justin Nnaemeka Onyeukaziri - 2024 - Religions 15 (5):623.
    It is the contention of this paper that ethics of work ought to be anthropological, and artificial intelligence (AI) research and development, which is the focus of work today, should be anthropological, that is, human-centered. This paper discusses the philosophical and theological implications of the development of AI research on the intrinsic nature of work and the nature of the human person. AI research and the implications of its development and advancement, being a relatively new phenomenon, have not been comprehensively (...)
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Algorithmic Fairness
  1. Bias detectives: The researchers striving to make algorithms fair.R. Courtland - 2018 - Nature 558.
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  2. Ethical AI at Work: The Social Contract for Artificial Intelligence and Its Implications for the Workplace Psychological Contract.Sarah Bankins & Paul Formosa - 2021 - In Sarah Bankins & Paul Formosa (eds.), Ethical AI at Work: The Social Contract for Artificial Intelligence and Its Implications for the Workplace Psychological Contract. Cham, Switzerland:
    Artificially intelligent (AI) technologies are increasingly being used in many workplaces. It is recognised that there are ethical dimensions to the ways in which organisations implement AI alongside, or substituting for, their human workforces. How will these technologically driven disruptions impact the employee–employer exchange? We provide one way to explore this question by drawing on scholarship linking Integrative Social Contracts Theory (ISCT) to the psychological contract (PC). Using ISCT, we show that the macrosocial contract’s ethical AI norms of beneficence, non-maleficence, (...)
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  3. A Genealogical Approach to Algorithmic Bias.Marta Ziosi, David Watson & Luciano Floridi - 2024 - Minds and Machines 34 (2):1-17.
    The Fairness, Accountability, and Transparency (FAccT) literature tends to focus on bias as a problem that requires ex post solutions (e.g. fairness metrics), rather than addressing the underlying social and technical conditions that (re)produce it. In this article, we propose a complementary strategy that uses genealogy as a constructive, epistemic critique to explain algorithmic bias in terms of the conditions that enable it. We focus on XAI feature attributions (Shapley values) and counterfactual approaches as potential tools to gauge these conditions (...)
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  4. Gradual (in) compatibility of fairness criteria.Hertweck Corinna & Tim Räz - 2022 - Proceedings of the AAAI Conference on Artificial Intelligence 36 (11):11926-11934.
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  5. Legitimate Power, Illegitimate Automation: The problem of ignoring legitimacy in automated decision systems.Jake Iain Stone & Brent Mittelstadt - forthcoming - The Association for Computing Machinery Conference on Fairness, Accountability, and Transparency 2024.
    Progress in machine learning and artificial intelligence has spurred the widespread adoption of automated decision systems (ADS). An extensive literature explores what conditions must be met for these systems' decisions to be fair. However, questions of legitimacy -- why those in control of ADS are entitled to make such decisions -- have received comparatively little attention. This paper shows that when such questions are raised theorists often incorrectly conflate legitimacy with either public acceptance or other substantive values such as fairness, (...)
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  6. From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap.Tianqi Kou - manuscript
    Two goals - improving replicability and accountability of Machine Learning research respectively, have accrued much attention from the AI ethics and the Machine Learning community. Despite sharing the measures of improving transparency, the two goals are discussed in different registers - replicability registers with scientific reasoning whereas accountability registers with ethical reasoning. Given the existing challenge of the Responsibility Gap - holding Machine Learning scientists accountable for Machine Learning harms due to them being far from sites of application, this paper (...)
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  7. Algorithms are not neutral: Bias in collaborative filtering.Catherine Stinson - 2022 - AI and Ethics 2 (4):763-770.
    When Artificial Intelligence (AI) is applied in decision-making that affects people’s lives, it is now well established that the outcomes can be biased or discriminatory. The question of whether algorithms themselves can be among the sources of bias has been the subject of recent debate among Artificial Intelligence researchers, and scholars who study the social impact of technology. There has been a tendency to focus on examples, where the data set used to train the AI is biased, and denial on (...)
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  8. A Framework for Assurance Audits of Algorithmic Systems.Benjamin Lange, Khoa Lam, Borhane Hamelin, Davidovic Jovana, Shea Brown & Ali Hasan - forthcoming - Proceedings of the 2024 Acm Conference on Fairness, Accountability, and Transparency.
    An increasing number of regulations propose the notion of ‘AI audits’ as an enforcement mechanism for achieving transparency and accountability for artificial intelligence (AI) systems. Despite some converging norms around various forms of AI auditing, auditing for the purpose of compliance and assurance currently have little to no agreed upon practices, procedures, taxonomies, and standards. We propose the ‘criterion audit’ as an operationalizable compliance and assurance external audit framework. We model elements of this approach after financial auditing practices, and argue (...)
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  9. Yet Another Impossibility Theorem in Algorithmic Fairness.Fabian Beigang - 2023 - Minds and Machines 33 (4):715-735.
    In recent years, there has been a surge in research addressing the question which properties predictive algorithms ought to satisfy in order to be considered fair. Three of the most widely discussed criteria of fairness are the criteria called equalized odds, predictive parity, and counterfactual fairness. In this paper, I will present a new impossibility result involving these three criteria of algorithmic fairness. In particular, I will argue that there are realistic circumstances under which any predictive algorithm that satisfies counterfactual (...)
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  10. Are Algorithms Value-Free?Gabbrielle M. Johnson - 2023 - Journal Moral Philosophy 21 (1-2):1-35.
    As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments from feminist philosophy of (...)
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  11. Conformism, Ignorance & Injustice: AI as a Tool of Epistemic Oppression.Martin Miragoli - 2024 - Episteme: A Journal of Social Epistemology:1-19.
    From music recommendation to assessment of asylum applications, machine-learning algorithms play a fundamental role in our lives. Naturally, the rise of AI implementation strategies has brought to public attention the ethical risks involved. However, the dominant anti-discrimination discourse, too often preoccupied with identifying particular instances of harmful AIs, has yet to bring clearly into focus the more structural roots of AI-based injustice. This paper addresses the problem of AI-based injustice from a distinctively epistemic angle. More precisely, I argue that the (...)
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  12. Criteria for Assessing AI-Based Sentencing Algorithms: A Reply to Ryberg.Thomas Douglas - 2024 - Philosophy and Technology 37 (1):1-4.
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  13. An Impossibility Theorem for Base Rate Tracking and Equalized Odds.Rush T. Stewart, Benjamin Eva, Shanna Slank & Reuben Stern - forthcoming - Analysis.
    There is a theorem that shows that it is impossible for an algorithm to jointly satisfy the statistical fairness criteria of Calibration and Equalised Odds non-trivially. But what about the recently advocated alternative to Calibration, Base Rate Tracking? Here, we show that Base Rate Tracking is strictly weaker than Calibration, and then take up the question of whether it is possible to jointly satisfy Base Rate Tracking and Equalised Odds in non-trivial scenarios. We show that it is not, thereby establishing (...)
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  14. Big Data as Tracking Technology and Problems of the Group and its Members.Haleh Asgarinia - 2023 - In Kevin Macnish & Adam Henschke (eds.), The Ethics of Surveillance in Times of Emergency. Oxford University Press. pp. 60-75.
    Digital data help data scientists and epidemiologists track and predict outbreaks of disease. Mobile phone GPS data, social media data, or other forms of information updates such as the progress of epidemics are used by epidemiologists to recognize disease spread among specific groups of people. Targeting groups as potential carriers of a disease, rather than addressing individuals as patients, risks causing harm to groups. While there are rules and obligations at the level of the individual, we have to reach a (...)
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  15. Causal models and algorithmic fairness.Fabian Beigang - unknown
    This thesis aims to clarify a number of conceptual aspects of the debate surrounding algorithmic fairness. The particular focus here is the role of causal modeling in defining criteria of algorithmic fairness. In Chapter 1, I argue that in the discussion of algorithmic fairness, two fundamentally distinct notions of fairness have been conflated. Subsequently, I propose that what is usually taken to be the problem of algorithmic fairness should be divided into two subproblems, the problem of predictive fairness, and the (...)
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  16. Bare statistical evidence and the legitimacy of software-based judicial decisions.Eva Schmidt, Maximilian Köhl & Andreas Sesing-Wagenpfeil - 2023 - Synthese 201 (4):1-27.
    Can the evidence provided by software systems meet the standard of proof for civil or criminal cases, and is it individualized evidence? Or, to the contrary, do software systems exclusively provide bare statistical evidence? In this paper, we argue that there are cases in which evidence in the form of probabilities computed by software systems is not bare statistical evidence, and is thus able to meet the standard of proof. First, based on the case of State v. Loomis, we investigate (...)
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  17. Algorithmic Profiling as a Source of Hermeneutical Injustice.Silvia Milano & Carina Prunkl - forthcoming - Philosophical Studies:1-19.
    It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only demonstrate how (...)
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  18. Algorithmic Transparency and Manipulation.Michael Klenk - 2023 - Philosophy and Technology 36 (4):1-20.
    A series of recent papers raises worries about the manipulative potential of algorithmic transparency (to wit, making visible the factors that influence an algorithm’s output). But while the concern is apt and relevant, it is based on a fraught understanding of manipulation. Therefore, this paper draws attention to the ‘indifference view’ of manipulation, which explains better than the ‘vulnerability view’ why algorithmic transparency has manipulative potential. The paper also raises pertinent research questions for future studies of manipulation in the context (...)
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  19. Algorithmic Fairness, Risk, and the Dominant Protective Agency.Ulrik Franke - 2023 - Philosophy and Technology 36 (4):1-7.
    With increasing use of automated algorithmic decision-making, issues of algorithmic fairness have attracted much attention lately. In this growing literature, existing concepts from ethics and political philosophy are often applied to new contexts. The reverse—that novel insights from the algorithmic fairness literature are fed back into ethics and political philosophy—is far less established. However, this short commentary on Baumann and Loi (Philosophy & Technology, 36(3), 45 2023) aims to do precisely this. Baumann and Loi argue that among algorithmic group fairness (...)
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  20. An Epistemic Lens on Algorithmic Fairness.Elizabeth Edenberg & Alexandra Wood - 2023 - Eaamo '23: Proceedings of the 3Rd Acm Conference on Equity and Access in Algorithms, Mechanisms, and Optimization.
    In this position paper, we introduce a new epistemic lens for analyzing algorithmic harm. We argue that the epistemic lens we propose herein has two key contributions to help reframe and address some of the assumptions underlying inquiries into algorithmic fairness. First, we argue that using the framework of epistemic injustice helps to identify the root causes of harms currently framed as instances of representational harm. We suggest that the epistemic lens offers a theoretical foundation for expanding approaches to algorithmic (...)
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  21. Disambiguating Algorithmic Bias: From Neutrality to Justice.Elizabeth Edenberg & Alexandra Wood - 2023 - In Francesca Rossi, Sanmay Das, Jenny Davis, Kay Firth-Butterfield & Alex John (eds.), AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery. pp. 691-704.
    As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered by conflations (...)
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  22. Markets, market algorithms, and algorithmic bias.Philippe van Basshuysen - 2022 - Journal of Economic Methodology 30 (4):310-321.
    Where economists previously viewed the market as arising from a ‘spontaneous order’, antithetical to design, they now design markets to achieve specific purposes. This paper reconstructs how this change in what markets are and can do came about and considers some consequences. Two decisive developments in economic theory are identified: first, Hurwicz’s view of institutions as mechanisms, which should be designed to align incentives with social goals; and second, the notion of marketplaces – consisting of infrastructure and algorithms – which (...)
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  23. Fair equality of chances for prediction-based decisions.Michele Loi, Anders Herlitz & Hoda Heidari - forthcoming - Economics and Philosophy:1-24.
    This article presents a fairness principle for evaluating decision-making based on predictions: a decision rule is unfair when the individuals directly impacted by the decisions who are equal with respect to the features that justify inequalities in outcomes do not have the same statistical prospects of being benefited or harmed by them, irrespective of their socially salient morally arbitrary traits. The principle can be used to evaluate prediction-based decision-making from the point of view of a wide range of antecedently specified (...)
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  24. Shared decision-making and maternity care in the deep learning age: Acknowledging and overcoming inherited defeaters.Keith Begley, Cecily Begley & Valerie Smith - 2021 - Journal of Evaluation in Clinical Practice 27 (3):497–503.
    In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds in (...)
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  25. ChatGPT’s Responses to Dilemmas in Medical Ethics: The Devil is in the Details.Lukas J. Meier - 2023 - American Journal of Bioethics 23 (10):63-65.
    In their Target Article, Rahimzadeh et al. (2023) discuss the virtues and vices of employing ChatGPT in ethics education for healthcare professionals. To this end, they confront the chatbot with a moral dilemma and analyse its response. In interpreting the case, ChatGPT relies on Beauchamp and Childress’ four prima-facie principles: beneficence, non-maleficence, respect for patient autonomy, and justice. While the chatbot’s output appears admirable at first sight, it is worth taking a closer look: ChatGPT not only misses the point when (...)
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  26. Melting contestation: insurance fairness and machine learning.Laurence Barry & Arthur Charpentier - 2023 - Ethics and Information Technology 25 (4):1-13.
    With their intensive use of data to classify and price risk, insurers have often been confronted with data-related issues of fairness and discrimination. This paper provides a comparative review of discrimination issues raised by traditional statistics versus machine learning in the context of insurance. We first examine historical contestations of insurance classification, showing that it was organized along three types of bias: pure stereotypes, non-causal correlations, or causal effects that a society chooses to protect against, are thus the main sources (...)
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  27. “Just” accuracy? Procedural fairness demands explainability in AI‑based medical resource allocation.Jon Rueda, Janet Delgado Rodríguez, Iris Parra Jounou, Joaquín Hortal-Carmona, Txetxu Ausín & David Rodríguez-Arias - 2022 - AI and Society:1-12.
    The increasing application of artificial intelligence (AI) to healthcare raises both hope and ethical concerns. Some advanced machine learning methods provide accurate clinical predictions at the expense of a significant lack of explainability. Alex John London has defended that accuracy is a more important value than explainability in AI medicine. In this article, we locate the trade-off between accurate performance and explainable algorithms in the context of distributive justice. We acknowledge that accuracy is cardinal from outcome-oriented justice because it helps (...)
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  28. What we owe to decision-subjects: beyond transparency and explanation in automated decision-making.David Gray Grant, Jeff Behrends & John Basl - 2023 - Philosophical Studies 2003:1-31.
    The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts (...)
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  29. Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems.Alex John London & Hoda Heidari - manuscript
    The prevailing discourse around AI ethics lacks the language and formalism necessary to capture the diverse ethical concerns that emerge when AI systems interact with individuals. Drawing on Sen and Nussbaum's capability approach, we present a framework formalizing a network of ethical concepts and entitlements necessary for AI systems to confer meaningful benefit or assistance to stakeholders. Such systems enhance stakeholders' ability to advance their life plans and well-being while upholding their fundamental rights. We characterize two necessary conditions for morally (...)
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  30. ACROCPoLis: A Descriptive Framework for Making Sense of Fairness.Andrea Aler Tubella, Dimitri Coelho Mollo, Adam Dahlgren, Hannah Devinney, Virginia Dignum, Petter Ericson, Anna Jonsson, Tim Kampik, Tom Lenaerts, Julian Mendez & Juan Carlos Nieves Sanchez - 2023 - Proceedings of the 2023 Acm Conference on Fairness, Accountability, and Transparency:1014-1025.
    Fairness is central to the ethical and responsible development and use of AI systems, with a large number of frameworks and formal notions of algorithmic fairness being available. However, many of the fairness solutions proposed revolve around technical considerations and not the needs of and consequences for the most impacted communities. We therefore want to take the focus away from definitions and allow for the inclusion of societal and relational aspects to represent how the effects of AI systems impact and (...)
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  31. Artificial intelligence ELSI score for science and technology: a comparison between Japan and the US.Tilman Hartwig, Yuko Ikkatai, Naohiro Takanashi & Hiromi M. Yokoyama - 2023 - AI and Society 38 (4):1609-1626.
    Artificial intelligence (AI) has become indispensable in our lives. The development of a quantitative scale for AI ethics is necessary for a better understanding of public attitudes toward AI research ethics and to advance the discussion on using AI within society. For this study, we developed an AI ethics scale based on AI-specific scenarios. We investigated public attitudes toward AI ethics in Japan and the US using online questionnaires. We designed a test set using four dilemma scenarios and questionnaire items (...)
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  32. Meta’s Oversight Board: A Review and Critical Assessment.David Wong & Luciano Floridi - 2023 - Minds and Machines 33 (2):261-284.
    Since the announcement and establishment of the Oversight Board (OB) by the technology company Meta as an independent institution reviewing Facebook and Instagram’s content moderation decisions, the OB has been subjected to scholarly scrutiny ranging from praise to criticism. However, there is currently no overarching framework for understanding the OB’s various strengths and weaknesses. Consequently, this article analyses, organises, and supplements academic literature, news articles, and Meta and OB documents to understand the OB’s strengths and weaknesses and how it can (...)
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  33. Informational richness and its impact on algorithmic fairness.Marcello Di Bello & Ruobin Gong - forthcoming - Philosophical Studies:1-29.
    The literature on algorithmic fairness has examined exogenous sources of biases such as shortcomings in the data and structural injustices in society. It has also examined internal sources of bias as evidenced by a number of impossibility theorems showing that no algorithm can concurrently satisfy multiple criteria of fairness. This paper contributes to the literature stemming from the impossibility theorems by examining how informational richness affects the accuracy and fairness of predictive algorithms. With the aid of a computer simulation, we (...)
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  34. Dirty data labeled dirt cheap: epistemic injustice in machine learning systems.Gordon Hull - 2023 - Ethics and Information Technology 25 (3):1-14.
    Artificial intelligence (AI) and machine learning (ML) systems increasingly purport to deliver knowledge about people and the world. Unfortunately, they also seem to frequently present results that repeat or magnify biased treatment of racial and other vulnerable minorities. This paper proposes that at least some of the problems with AI’s treatment of minorities can be captured by the concept of epistemic injustice. To substantiate this claim, I argue that (1) pretrial detention and physiognomic AI systems commit testimonial injustice because their (...)
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  35. Bias Optimizers.Damien P. Williams - 2023 - American Scientist 111 (4):204-207.
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