About this topic
Summary Ethical issues associated with AI are proliferating and rising to popular attention as intelligent machines become ubiquitous. For example, AIs can and do model aspects essential to moral agency and so offer tools for the investigation of consciousness and other aspects of cognition contributing to moral status (either ascribed or achieved). This has deep implications for our understanding of moral agency, and so of systems of ethics meant to account for and to provide for the development of such capacities. This raises the issue of responsible and/or blameworthy AIs operating openly in general society, with deep implications again for systems of ethics which must accommodate moral AIs. Consider also that human social infrastructure (e.g. energy grids, mass-transit systems) are increasingly moderated by increasingly intelligent machines. This alone raises many moral/ethical concerns. For example, who or what is responsible in the case of an accident due to system error, or due to design flaws, or due to proper operation outside of anticipated constraints? 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 issues often bear on the long-term safety of intelligent systems, and 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. 
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 2022, see also  https://plato.stanford.edu/entries/ethics-ai/
Related categories

1891 found
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Algorithmic Fairness
  1. 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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  2. Algorithmic Fairness and the Situated Dynamics of Justice.Sina Fazelpour, Zachary C. Lipton & David Danks - 2022 - Canadian Journal of Philosophy 52 (1):44-60.
    Machine learning algorithms are increasingly used to shape high-stake allocations, sparking research efforts to orient algorithm design towards ideals of justice and fairness. In this research on algorithmic fairness, normative theorizing has primarily focused on identification of “ideally fair” target states. In this paper, we argue that this preoccupation with target states in abstraction from the situated dynamics of deployment is misguided. We propose a framework that takes dynamic trajectories as direct objects of moral appraisal, highlighting three respects in which (...)
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  3. Social Media and its Negative Impacts on Autonomy.Siavosh Sahebi & Paul Formosa - 2022 - Philosophy and Technology 35 (3):1-24.
    How social media impacts the autonomy of its users is a topic of increasing focus. However, much of the literature that explores these impacts fails to engage in depth with the philosophical literature on autonomy. This has resulted in a failure to consider the full range of impacts that social media might have on autonomy. A deeper consideration of these impacts is thus needed, given the importance of both autonomy as a moral concept and social media as a feature of (...)
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  4. Understanding User Sensemaking in Fairness and Transparency in Algorithms: Algorithmic Sensemaking in Over-the-Top Platform.Donghee Shin, Joon Soo Lim, Norita Ahmad & Mohammed Ibahrine - forthcoming - AI and Society:1-14.
    A number of artificial intelligence systems have been proposed to assist users in identifying the issues of algorithmic fairness and transparency. These AI systems use diverse bias detection methods from various perspectives, including exploratory cues, interpretable tools, and revealing algorithms. This study explains the design of AI systems by probing how users make sense of fairness and transparency as they are hypothetical in nature, with no specific ways for evaluation. Focusing on individual perceptions of fairness and transparency, this study examines (...)
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  5. Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept.Lukas J. Meier, Alice Hein, Klaus Diepold & Alena Buyx - 2022 - American Journal of Bioethics 22 (7):4-20.
    Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...)
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  6. Assembled Bias: Beyond Transparent Algorithmic Bias.Robyn Repko Waller & Russell L. Waller - forthcoming - Minds and Machines:1-30.
    In this paper we make the case for the emergence of novel kind of bias with the use of algorithmic decision-making systems. We argue that the distinctive generative process of feature creation, characteristic of machine learning, contorts feature parameters in ways that can lead to emerging feature spaces that encode novel algorithmic bias involving already marginalized groups. We term this bias assembled bias. Moreover, assembled biases are distinct from the much-discussed algorithmic bias, both in source and in content. As such, (...)
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  7. From Explanation to Recommendation: Ethical Standards for Algorithmic Recourse.Emily Sullivan & Philippe Verreault-Julien - forthcoming - Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES’22).
    People are increasingly subject to algorithmic decisions, and it is generally agreed that end-users should be provided an explanation or rationale for these decisions. There are different purposes that explanations can have, such as increasing user trust in the system or allowing users to contest the decision. One specific purpose that is gaining more traction is algorithmic recourse. We first pro- pose that recourse should be viewed as a recommendation problem, not an explanation problem. Then, we argue that the capability (...)
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  8. Medical AI and Human Dignity: Contrasting Perceptions of Human and Artificially Intelligent (AI) Decision Making in Diagnostic and Medical Resource Allocation Contexts.Paul Formosa, Wendy Rogers, Yannick Griep, Sarah Bankins & Deborah Richards - 2022 - Computers in Human Behaviour 133.
    Forms of Artificial Intelligence (AI) are already being deployed into clinical settings and research into its future healthcare uses is accelerating. Despite this trajectory, more research is needed regarding the impacts on patients of increasing AI decision making. In particular, the impersonal nature of AI means that its deployment in highly sensitive contexts-of-use, such as in healthcare, raises issues associated with patients’ perceptions of (un) dignified treatment. We explore this issue through an experimental vignette study comparing individuals’ perceptions of being (...)
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  9. AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context.Sarah Bankins, Paul Formosa, Yannick Griep & Deborah Richards - forthcoming - Information Systems Frontiers.
    Using artificial intelligence (AI) to make decisions in human resource management (HRM) raises questions of how fair employees perceive these decisions to be and whether they experience respectful treatment (i.e., interactional justice). In this experimental survey study with open-ended qualitative questions, we examine decision making in six HRM functions and manipulate the decision maker (AI or human) and decision valence (positive or negative) to determine their impact on individuals’ experiences of interactional justice, trust, dehumanization, and perceptions of decision-maker role appropriate- (...)
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  10. Basic Issues in AI Policy.Vincent C. Müller - 2022 - In Maria Amparo Grau-Ruiz (ed.), Interactive robotics: Legal, ethical, social and economic aspects. Cham: Springer. pp. 3-9.
    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.
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  11. La logique algorithmique confrontée à l'organisation de l'administration publique française.Giada Pistilli - 2021 - Giornale di Filosofia 2 (2):163-169.
    Cet article montre comment la logique algorithmique d’un agent conversationnel peut aider l’organisation des connaissances au sein d’une organisation de l’administration publique française, notamment une collectivité territoriale. Par le bias d’une recherche sur le terrain, je cherche à montrer comment il existe deux différentes adoptions de la technologie de la part de l’administration publique : une complexifiante et une simplifiante.
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  12. What Lies Behind AGI: Ethical Concerns Related to LLMs.Giada Pistilli - 2022 - Éthique Et Numérique 1 (1):59-68.
    This paper opens the philosophical debate around the notion of Artificial General Intelligence (AGI) and its application in Large Language Models (LLMs). Through the lens of moral philosophy, the paper raises questions about these AI systems' capabilities and goals, the treatment of humans behind them, and the risk of perpetuating a monoculture through language.
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  13. When Gig Workers Become Essential: Leveraging Customer Moral Self-Awareness Beyond COVID-19.Julian Friedland - forthcoming - Business Horizons 66.
    The COVID-19 pandemic has intensified the extent to which economies in the developed and developing world rely on gig workers to perform essential tasks such as health care, personal transport, food and package delivery, and ad hoc tasking services. As a result, workers who provide such services are no longer perceived as mere low-skilled laborers, but as essential workers who fulfill a crucial role in society. The newly elevated moral and economic status of these workers increases consumer demand for corporate (...)
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  14. Identity and the Limits of Fair Assessment.Rush T. Stewart - forthcoming - Journal of Theoretical Politics.
    In many assessment problems—aptitude testing, hiring decisions, appraisals of the risk of recidivism, evaluation of the credibility of testimonial sources, and so on—the fair treatment of different groups of individuals is an important goal. But individuals can be legitimately grouped in many different ways. Using a framework and fairness constraints explored in research on algorithmic fairness, I show that eliminating certain forms of bias across groups for one way of classifying individuals can make it impossible to eliminate such bias across (...)
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  15. Algorithmic Fairness Through Group Parities? The Case of COMPAS-SAPMOC.Francesca Lagioia, Riccardo Rovatti & Giovanni Sartor - forthcoming - AI and Society.
    Machine learning classifiers are increasingly used to inform, or even make, decisions significantly affecting human lives. Fairness concerns have spawned a number of contributions aimed at both identifying and addressing unfairness in algorithmic decision-making. This paper critically discusses the adoption of group-parity criteria as fairness standards. To this end, we evaluate the use of machine learning methods relative to different steps of the decision-making process: assigning a predictive score, linking a classification to the score, and adopting decisions based on the (...)
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  16. Algorithmic Fairness and Base Rate Tracking.Benjamin Eva - 2022 - Philosophy and Public Affairs 50 (2):239-266.
    Philosophy & Public Affairs, Volume 50, Issue 2, Page 239-266, Spring 2022.
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  17. Algorithmic Political Bias in Artificial Intelligence Systems.Uwe Peters - 2022 - Philosophy and Technology 35 (2):1-23.
    Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can arise in (...)
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  18. Machine See, Machine Do: How Technology Mirrors Bias in Our Criminal Justice System.Patrick K. Lin - 2021 - New Degree Press.
    “When today’s technology relies on yesterday’s data, it will simply mirror our past mistakes and biases.” -/- AI and other high-tech tools embed and reinforce America’s history of prejudice and exclusion — even when they are used with the best intentions. Patrick K. Lin’s Machine See, Machine Do: How Technology Mirrors Bias in Our Criminal Justice System takes a deep and thorough look into the use of technology in the criminal justice system, and investigates the instances of coded bias present (...)
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  19. The Limits of Reallocative and Algorithmic Policing.Luke William Hunt - 2022 - Criminal Justice Ethics 41 (1):1-24.
    Policing in many parts of the world—the United States in particular—has embraced an archetypal model: a conception of the police based on the tenets of individuated archetypes, such as the heroic police “warrior” or “guardian.” Such policing has in part motivated moves to (1) a reallocative model: reallocating societal resources such that the police are no longer needed in society (defunding and abolishing) because reform strategies cannot fix the way societal problems become manifest in (archetypal) policing; and (2) an algorithmic (...)
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  20. The Use and Misuse of Counterfactuals in Ethical Machine Learning.Atoosa Kasirzadeh & Andrew Smart - 2021 - In ACM Conference on Fairness, Accountability, and Transparency (FAccT 21).
    The use of counterfactuals for considerations of algorithmic fairness and explainability is gaining prominence within the machine learning community and industry. This paper argues for more caution with the use of counterfactuals when the facts to be considered are social categories such as race or gender. We review a broad body of papers from philosophy and social sciences on social ontology and the semantics of counterfactuals, and we conclude that the counterfactual approach in machine learning fairness and social explainability can (...)
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  21. Algorithmic Fairness in Mortgage Lending: From Absolute Conditions to Relational Trade-Offs.Michelle Seng Ah Lee & Luciano Floridi - 2021 - In Josh Cowls & Jessica Morley (eds.), The 2020 Yearbook of the Digital Ethics Lab. Springer Verlag. pp. 145-171.
    To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decision-making processes. Using U.S. mortgage lending as an example use (...)
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  22. The Fairness in Algorithmic Fairness.Sune Holm - forthcoming - Res Publica:1-17.
    With the increasing use of algorithms in high-stakes areas such as criminal justice and health has come a significant concern about the fairness of prediction-based decision procedures. In this article I argue that a prominent class of mathematically incompatible performance parity criteria can all be understood as applications of John Broome’s account of fairness as the proportional satisfaction of claims. On this interpretation these criteria do not disagree on what it means for an algorithm to be fair. Rather they express (...)
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  23. Disability, Fairness, and Algorithmic Bias in AI Recruitment.Nicholas Tilmes - 2022 - Ethics and Information Technology 24 (2).
    While rapid advances in artificial intelligence hiring tools promise to transform the workplace, these algorithms risk exacerbating existing biases against marginalized groups. In light of these ethical issues, AI vendors have sought to translate normative concepts such as fairness into measurable, mathematical criteria that can be optimized for. However, questions of disability and access often are omitted from these ongoing discussions about algorithmic bias. In this paper, I argue that the multiplicity of different kinds and intensities of people’s disabilities and (...)
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  24. Rule by Automation: How Automated Decision Systems Promote Freedom and Equality.Athmeya Jayaram & Jacob Sparks - forthcoming - Moral Philosophy and Politics.
    Using automated systems to avoid the need for human discretion in government contexts – a scenario we call ‘rule by automation’ – can help us achieve the ideal of a free and equal society. Drawing on relational theories of freedom and equality, we explain how rule by automation is a more complete realization of the rule of law and why thinkers in these traditions have strong reasons to support it. Relational theories are based on the absence of human domination and (...)
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  25. On Algorithmic Fairness in Medical Practice.Thomas Grote & Geoff Keeling - 2022 - Cambridge Quarterly of Healthcare Ethics 31 (1):83-94.
    The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. (...)
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  26. Rawls’s Original Position and Algorithmic Fairness.Ulrik Franke - 2021 - Philosophy and Technology 34 (4):1803-1817.
    Modern society makes extensive use of automated algorithmic decisions, fueled by advances in artificial intelligence. However, since these systems are not perfect, questions about fairness are increasingly investigated in the literature. In particular, many authors take a Rawlsian approach to algorithmic fairness. This article aims to identify some complications with this approach: Under which circumstances can Rawls’s original position reasonably be applied to algorithmic fairness decisions? First, it is argued that there are important differences between Rawls’s original position and a (...)
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  27. Non-Empirical Problems in Fair Machine Learning.Teresa Scantamburlo - 2021 - Ethics and Information Technology 23 (4):703-712.
    The problem of fair machine learning has drawn much attention over the last few years and the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also raises important conceptual issues that would fail to be addressed if one relies entirely on empirical considerations. Herein, I will argue that the current debate has developed an empirical framework that has brought important contributions to the development of algorithmic decision-making, such as new techniques to discover and prevent discrimination, additional assessment (...)
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  28. Fair Machine Learning Under Partial Compliance.Jessica Dai, Sina Fazelpour & Zachary Lipton - 2021 - In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society. pp. 55–65.
    Typically, fair machine learning research focuses on a single decision maker and assumes that the underlying population is stationary. However, many of the critical domains motivating this work are characterized by competitive marketplaces with many decision makers. Realistically, we might expect only a subset of them to adopt any non-compulsory fairness-conscious policy, a situation that political philosophers call partial compliance. This possibility raises important questions: how does partial compliance and the consequent strategic behavior of decision subjects affect the allocation outcomes? (...)
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  29. Measuring Fairness in an Unfair World.Jonathan Herington - 2020 - Proceedings of AAAI/ACM Conference on AI, Ethics, and Society 2020:286-292.
    Computer scientists have made great strides in characterizing different measures of algorithmic fairness, and showing that certain measures of fairness cannot be jointly satisfied. In this paper, I argue that the three most popular families of measures - unconditional independence, target-conditional independence and classification-conditional independence - make assumptions that are unsustainable in the context of an unjust world. I begin by introducing the measures and the implicit idealizations they make about the underlying causal structure of the contexts in which they (...)
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  30. Proceed with Caution.Annette Zimmermann & Chad Lee-Stronach - 2021 - Canadian Journal of Philosophy (1):6-25.
    It is becoming more common that the decision-makers in private and public institutions are predictive algorithmic systems, not humans. This article argues that relying on algorithmic systems is procedurally unjust in contexts involving background conditions of structural injustice. Under such nonideal conditions, algorithmic systems, if left to their own devices, cannot meet a necessary condition of procedural justice, because they fail to provide a sufficiently nuanced model of which cases count as relevantly similar. Resolving this problem requires deliberative capacities uniquely (...)
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  31. Formalising Trade-Offs Beyond Algorithmic Fairness: Lessons From Ethical Philosophy and Welfare Economics.Michelle Seng Ah Lee, Luciano Floridi & Jatinder Singh - 2021 - AI and Ethics 3.
    There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits that (...)
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  32. What's Fair About Individual Fairness?Will Fleisher - 2021 - Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society.
    One of the main lines of research in algorithmic fairness involves individual fairness (IF) methods. Individual fairness is motivated by an intuitive principle, similar treatment, which requires that similar individuals be treated similarly. IF offers a precise account of this principle using distance metrics to evaluate the similarity of individuals. Proponents of individual fairness have argued that it gives the correct definition of algorithmic fairness, and that it should therefore be preferred to other methods for determining fairness. I argue that (...)
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  33. Oxford Handbook of Digital Ethics.Carissa Veliz (ed.) - forthcoming - Oxford University Press.
    The Oxford Handbook of Digital Ethics is a lively and authoritative guide to ethical issues related to digital technologies, with a special emphasis on AI. Philosophers with a wide range of expertise cover thirty-seven topics: from the right to have access to internet, to trolling and online shaming, speech on social media, fake news, sex robots and dating online, persuasive technology, value alignment, algorithmic bias, predictive policing, price discrimination online, medical AI, privacy and surveillance, automating democracy, the future of work, (...)
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  34. On Statistical Criteria of Algorithmic Fairness.Brian Hedden - 2021 - Philosophy and Public Affairs 49 (2):209-231.
    Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of false (...)
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  35. Algorithmic Fairness in Mortgage Lending: From Absolute Conditions to Relational Trade-Offs.Michelle Seng Ah Lee & Luciano Floridi - 2021 - Minds and Machines 31 (1):165-191.
    To address the rising concern that algorithmic decision-making may reinforce discriminatory biases, researchers have proposed many notions of fairness and corresponding mathematical formalizations. Each of these notions is often presented as a one-size-fits-all, absolute condition; however, in reality, the practical and ethical trade-offs are unavoidable and more complex. We introduce a new approach that considers fairness—not as a binary, absolute mathematical condition—but rather, as a relational notion in comparison to alternative decisionmaking processes. Using US mortgage lending as an example use (...)
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  36. The Emerging Hazard of AI‐Related Health Care Discrimination.Sharona Hoffman - 2021 - Hastings Center Report 51 (1):8-9.
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  37. A Moral Framework for Understanding of Fair ML Through Economic Models of Equality of Opportunity.Hoda Heidari - 2019 - Proceedings of the Conference on Fairness, Accountability, and Transparency 1.
    We map the recently proposed notions of algorithmic fairness to economic models of Equality of opportunity (EOP)---an extensively studied ideal of fairness in political philosophy. We formally show that through our conceptual mapping, many existing definition of algorithmic fairness, such as predictive value parity and equality of odds, can be interpreted as special cases of EOP. In this respect, our work serves as a unifying moral framework for understanding existing notions of algorithmic fairness. Most importantly, this framework allows us to (...)
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  38. 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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  39. On the Possibility of Testimonial Justice.Rush T. Stewart & Michael Nielsen - 2020 - Australasian Journal of Philosophy 98 (4):732-746.
    Recent impossibility theorems for fair risk assessment extend to the domain of epistemic justice. We translate the relevant model, demonstrating that the problems of fair risk assessment and just credibility assessment are structurally the same. We motivate the fairness criteria involved in the theorems as also being appropriate in the setting of testimonial justice. Any account of testimonial justice that implies the fairness/justice criteria must be abandoned, on pain of triviality.
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  40. Democratizing Algorithmic Fairness.Pak-Hang Wong - 2020 - Philosophy and Technology 33 (2):225-244.
    Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers have (...)
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  41. A Ghost Workers' Bill of Rights: How to Establish a Fair and Safe Gig Work Platform.Julian Friedland, David Balkin & Ramiro Montealegre - 2020 - California Management Review 62 (2).
    Many of us assume that all the free editing and sorting of online content we ordinarily rely on is carried out by AI algorithms — not human persons. Yet in fact, that is often not the case. This is because human workers remain cheaper, quicker, and more reliable than AI for performing myriad tasks where the right answer turns on ineffable contextual criteria too subtle for algorithms to yet decode. The output of this work is then used for machine learning (...)
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  42. Algorithmic Fairness From a Non-Ideal Perspective.Sina Fazelpour & Zachary C. Lipton - 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society.
    Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...)
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  43. Detecting Racial Bias in Algorithms and Machine Learning.Nicol Turner Lee - 2018 - Journal of Information, Communication and Ethics in Society 16 (3):252-260.
    Purpose The online economy has not resolved the issue of racial bias in its applications. While algorithms are procedures that facilitate automated decision-making, or a sequence of unambiguous instructions, bias is a byproduct of these computations, bringing harm to historically disadvantaged populations. This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Relying upon sociological and technical research, the paper offers commentary on the need for more workplace diversity within high-tech industries and (...)
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  44. Equitable Selection in Bilateral Matching Markets.Antonio Romero-Medina - 2005 - Theory and Decision 58 (3):305-324.
    This paper presents a procedure to select equitable stable allocations in two-sided matching markets without side payments. The Equitable set is computed using the Equitable algorithm. The algorithm limits the set of options available for each agent throughout the procedure. The stable matchings selected are generally not extreme, form a lattice and satisfy the condition of being “Ralwsian” in each partition of the market. The Equitable algorithm can also be used to select a particular matching from the Equitable Set favoring (...)
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Autonomous Vehicles
  1. Basic Issues in AI Policy.Vincent C. Müller - 2022 - In Maria Amparo Grau-Ruiz (ed.), Interactive robotics: Legal, ethical, social and economic aspects. Cham: Springer. pp. 3-9.
    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.
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  2. Sven Nyholm, Humans and Robots; Ethics, Agency and Anthropomorphism.Lydia Farina - 2022 - Journal of Moral Philosophy 19 (2):221-224.
    How should human beings and robots interact with one another? Nyholm’s answer to this question is given below in the form of a conditional: If a robot looks or behaves like an animal or a human being then we should treat them with a degree of moral consideration (p. 201). Although this is not a novel claim in the literature on ai ethics, what is new is the reason Nyholm gives to support this claim; we should treat robots that look (...)
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  3. African Reasons Why AI Should Not Maximize Utility (Repr.).Thaddeus Metz - forthcoming - In Aribiah Attoe, Samuel Segun, Victor Nweke & John-Bosco Umezurike (eds.), Conversations on African Philosophy of Mind, Consciousness and AI. Springer.
    Reprint of a chapter first appearing in African Values, Ethics, and Technology: Questions, Issues, and Approaches (2021).
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Autonomous Weapons
  1. Autonomous Weapon Systems in Just War Theory Perspective. Maciej - 2022 - Dissertation,
    Please contact me at [email protected] if you are interested in reading a particular chapter or being sent the entire manuscript for private use. -/- The thesis offers a comprehensive argument in favor of a regulationist approach to autonomous weapon systems (AWS). AWS, defined as all military robots capable of selecting or engaging targets without direct human involvement, are an emerging and potentially deeply transformative military technology subject to very substantial ethical controversy. AWS have both their enthusiasts and their detractors, prominently (...)
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  2. Not Even Close to a (Fair) Fight: Technology and the Future of War.Jennifer Kling - 2021 - Philosophical Journal of Conflict and Violence 5 (1):1-17.
    The exponential expansion and advancement of wartime technology has the potential to wipe out ‘war’ as a meaningful category. Assuming that the creation of new wartime technologies continues to accelerate, it could soon be the case that there will no longer be wars, but rather mass killings, slaughters, or genocides. This is because the concept of ‘war’ entails that opposing sides either will, or are able to, fight back against one another to some recognizable degree. In fact, this is one (...)
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  3. Designed for Death: Controlling Killer Robots.Steven Umbrello - 2022 - Budapest: Trivent Publishing.
    Autonomous weapons systems, often referred to as ‘killer robots’, have been a hallmark of popular imagination for decades. However, with the inexorable advance of artificial intelligence systems (AI) and robotics, killer robots are quickly becoming a reality. These lethal technologies can learn, adapt, and potentially make life and death decisions on the battlefield with little-to-no human involvement. This naturally leads to not only legal but ethical concerns as to whether we can meaningful control such machines, and if so, then how. (...)
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