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  1. Promises and Pitfalls of Algorithm Use by State Authorities.Maryam Amir Haeri, Kathrin Hartmann, Jürgen Sirsch, Georg Wenzelburger & Katharina A. Zweig - 2022 - Philosophy and Technology 35 (2):1-31.
    Algorithmic systems are increasingly used by state agencies to inform decisions about humans. They produce scores on risks of recidivism in criminal justice, indicate the probability for a job seeker to find a job in the labor market, or calculate whether an applicant should get access to a certain university program. In this contribution, we take an interdisciplinary perspective, provide a bird’s eye view of the different key decisions that are to be taken when state actors decide to use an (...)
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  • A New Argument for No-Fault Compensation in Health Care: The Introduction of Artificial Intelligence Systems.Søren Holm, Catherine Stanton & Benjamin Bartlett - 2021 - Health Care Analysis 29 (3):171-188.
    Artificial intelligence systems advising healthcare professionals will be widely introduced into healthcare settings within the next 5–10 years. This paper considers how this will sit with tort/negligence based legal approaches to compensation for medical error. It argues that the introduction of AI systems will provide an additional argument pointing towards no-fault compensation as the better legal solution to compensation for medical error in modern health care systems. The paper falls into four parts. The first part rehearses the main arguments for (...)
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  • What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms.Bas de Boer & Olya Kudina - 2021 - Theoretical Medicine and Bioethics 42 (5):245-266.
    In this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which (...)
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  • Responsible Nudging for Social Good: New Healthcare Skills for AI-Driven Digital Personal Assistants.Marianna Capasso & Steven Umbrello - 2022 - Medicine, Health Care and Philosophy 25 (1):11-22.
    Traditional medical practices and relationships are changing given the widespread adoption of AI-driven technologies across the various domains of health and healthcare. In many cases, these new technologies are not specific to the field of healthcare. Still, they are existent, ubiquitous, and commercially available systems upskilled to integrate these novel care practices. Given the widespread adoption, coupled with the dramatic changes in practices, new ethical and social issues emerge due to how these systems nudge users into making decisions and changing (...)
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  • The Right to Refuse Diagnostics and Treatment Planning by Artificial Intelligence.Thomas Ploug & Søren Holm - 2020 - Medicine, Health Care and Philosophy 23 (1):107-114.
    In an analysis of artificially intelligent systems for medical diagnostics and treatment planning we argue that patients should be able to exercise a right to withdraw from AI diagnostics and treatment planning for reasons related to the physician’s role in the patients’ formation of and acting on personal preferences and values, the bias and opacity problem of AI systems, and rational concerns about the future societal effects of introducing AI systems in the health care sector.
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  • Mobile Health Ethics and the Expanding Role of Autonomy.Bettina Schmietow & Georg Marckmann - 2019 - Medicine, Health Care and Philosophy 22 (4):623-630.
    Mhealth technology is mushrooming world-wide and, in a variety of forms, reaches increasing numbers of users in ever-widening contexts and virtually independent from standard medical evidence assessment. Yet, debate on the broader societal impact including in particular mapping and classification of ethical issues raised has been limited. This article, as part of an ongoing empirically informed ethical research project, provides an overview of ethical issues of mhealth applications with a specific focus on implications on autonomy as a key notion in (...)
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  • The Algorithm Audit: Scoring the Algorithms That Score Us.Jovana Davidovic, Shea Brown & Ali Hasan - 2021 - Big Data and Society 8 (1).
    In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that do not (...)
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  • Artificial Intelligence Methods for a Bayesian Epistemology‐Powered Evidence Evaluation.Francesco De Pretis, Jürgen Landes & William Peden - 2021 - Journal of Evaluation in Clinical Practice 27 (3):504-512.
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  • Varieties of Artifacts: Embodied, Perceptual, Cognitive, and Affective.Richard Heersmink - 2021 - Topics in Cognitive Science (4):1-24.
    The primary goal of this essay is to provide a comprehensive overview and analysis of the various relations between material artifacts and the embodied mind. A secondary goal of this essay is to identify some of the trends in the design and use of artifacts. First, based on their functional properties, I identify four categories of artifacts co-opted by the embodied mind, namely (1) embodied artifacts, (2) perceptual artifacts, (3) cognitive artifacts, and (4) affective artifacts. These categories can overlap and (...)
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  • Unprepared Humanities: A Pedagogy (Forced) Online.Houman Harouni - 2021 - Journal of Philosophy of Education 55 (4-5):633-648.
    Journal of Philosophy of Education, EarlyView.
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  • Machine Learning and Power Relations.Jonne Maas - forthcoming - AI and Society.
    There has been an increased focus within the AI ethics literature on questions of power, reflected in the ideal of accountability supported by many Responsible AI guidelines. While this recent debate points towards the power asymmetry between those who shape AI systems and those affected by them, the literature lacks normative grounding and misses conceptual clarity on how these power dynamics take shape. In this paper, I develop a workable conceptualization of said power dynamics according to Cristiano Castelfranchi’s conceptual framework (...)
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  • Challenges in Enabling User Control Over Algorithm-Based Services.Pascal D. König - forthcoming - AI and Society.
    Algorithmic systems that provide services to people by supporting or replacing human decision-making promise greater convenience in various areas. The opacity of these applications, however, means that it is not clear how much they truly serve their users. A promising way to address the issue of possible undesired biases consists in giving users control by letting them configure a system and aligning its performance with users’ own preferences. However, as the present paper argues, this form of control over an algorithmic (...)
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  • Zombies in the Loop? Humans Trust Untrustworthy AI-Advisors for Ethical Decisions.Sebastian Krügel, Andreas Ostermaier & Matthias Uhl - 2022 - Philosophy and Technology 35 (1):1-37.
    Departing from the claim that AI needs to be trustworthy, we find that ethical advice from an AI-powered algorithm is trusted even when its users know nothing about its training data and when they learn information about it that warrants distrust. We conducted online experiments where the subjects took the role of decision-makers who received advice from an algorithm on how to deal with an ethical dilemma. We manipulated the information about the algorithm and studied its influence. Our findings suggest (...)
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  • The ethics of algorithms: key problems and solutions.Andreas Tsamados, Nikita Aggarwal, Josh Cowls, Jessica Morley, Huw Roberts, Mariarosaria Taddeo & Luciano Floridi - 2022 - AI and Society 37 (1):215-230.
    Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...)
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  • 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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  • Algorithmic Augmentation of Democracy: Considering Whether Technology Can Enhance the Concepts of Democracy and the Rule of Law Through Four Hypotheticals.Paul Burgess - 2022 - AI and Society 37 (1):97-112.
    The potential use, relevance, and application of AI and other technologies in the democratic process may be obvious to some. However, technological innovation and, even, its consideration may face an intuitive push-back in the form of algorithm aversion :114–126, 2015). In this paper, I confront this intuition and suggest that a more ‘extreme’ form of technological change in the democratic process does not necessarily result in a worse outcome in terms of the fundamental concepts of democracy and the Rule of (...)
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  • A Code of Digital Ethics: laying the foundation for digital ethics in a science and technology company.Sarah J. Becker, André T. Nemat, Simon Lucas, René M. Heinitz, Manfred Klevesath & Jean Enno Charton - forthcoming - AI and Society:1-11.
    The rapid and dynamic nature of digital transformation challenges companies that wish to develop and deploy novel digital technologies. Like other actors faced with this transformation, companies need to find robust ways to ethically guide their innovations and business decisions. Digital ethics has recently featured in a plethora of both practical corporate guidelines and compilations of high-level principles, but there remains a gap concerning the development of sound ethical guidance in specific business contexts. As a multinational science and technology company (...)
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  • Tensions in transparent urban AI: designing a smart electric vehicle charge point.Kars Alfrink, Ianus Keller, Neelke Doorn & Gerd Kortuem - forthcoming - AI and Society:1-17.
    The increasing use of artificial intelligence by public actors has led to a push for more transparency. Previous research has conceptualized AI transparency as knowledge that empowers citizens and experts to make informed choices about the use and governance of AI. Conversely, in this paper, we critically examine if transparency-as-knowledge is an appropriate concept for a public realm where private interests intersect with democratic concerns. We conduct a practice-based design research study in which we prototype and evaluate a transparent smart (...)
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  • From Responsibility to Reason-Giving Explainable Artificial Intelligence.Kevin Baum, Susanne Mantel, Timo Speith & Eva Schmidt - 2022 - Philosophy and Technology 35 (1):1-30.
    We argue that explainable artificial intelligence (XAI), specifically reason-giving XAI, often constitutes the most suitable way of ensuring that someone can properly be held responsible for decisions that are based on the outputs of artificial intelligent (AI) systems. We first show that, to close moral responsibility gaps (Matthias 2004), often a human in the loop is needed who is directly responsible for particular AI-supported decisions. Second, we appeal to the epistemic condition on moral responsibility to argue that, in order to (...)
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  • People Prefer Moral Discretion to Algorithms: Algorithm Aversion Beyond Intransparency.Johanna Jauernig, Matthias Uhl & Gari Walkowitz - 2022 - Philosophy and Technology 35 (1):1-25.
    We explore aversion to the use of algorithms in moral decision-making. So far, this aversion has been explained mainly by the fear of opaque decisions that are potentially biased. Using incentivized experiments, we study which role the desire for human discretion in moral decision-making plays. This seems justified in light of evidence suggesting that people might not doubt the quality of algorithmic decisions, but still reject them. In our first study, we found that people prefer humans with decision-making discretion to (...)
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  • Weapons of Moral Construction? On the Value of Fairness in Algorithmic Decision-Making.Simona Tiribelli & Benedetta Giovanola - 2022 - Ethics and Information Technology 24 (1).
    Fairness is one of the most prominent values in the Ethics and Artificial Intelligence debate and, specifically, in the discussion on algorithmic decision-making. However, while the need for fairness in ADM is widely acknowledged, the very concept of fairness has not been sufficiently explored so far. Our paper aims to fill this gap and claims that an ethically informed re-definition of fairness is needed to adequately investigate fairness in ADM. To achieve our goal, after an introductory section aimed at clarifying (...)
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  • A Neo-Republican Critique of AI Ethics.Jonne Maas - 2022 - Journal of Responsible Technology 9:100022.
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  • From Reality to World. A Critical Perspective on AI Fairness.Jean-Marie John-Mathews, Dominique Cardon & Christine Balagué - forthcoming - Journal of Business Ethics:1-15.
    Fairness of Artificial Intelligence decisions has become a big challenge for governments, companies, and societies. We offer a theoretical contribution to consider AI ethics outside of high-level and top-down approaches, based on the distinction between “reality” and “world” from Luc Boltanski. To do so, we provide a new perspective on the debate on AI fairness and show that criticism of ML unfairness is “realist”, in other words, grounded in an already instituted reality based on demographic categories produced by institutions. Second, (...)
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  • Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda.Anna Lena Hunkenschroer & Christoph Luetge - forthcoming - Journal of Business Ethics:1-31.
    Companies increasingly deploy artificial intelligence technologies in their personnel recruiting and selection process to streamline it, making it faster and more efficient. AI applications can be found in various stages of recruiting, such as writing job ads, screening of applicant resumes, and analyzing video interviews via face recognition software. As these new technologies significantly impact people’s lives and careers but often trigger ethical concerns, the ethicality of these AI applications needs to be comprehensively understood. However, given the novelty of AI (...)
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  • Discrimination in the Age of Artificial Intelligence.Bert Heinrichs - 2022 - AI and Society 37 (1):143-154.
    In this paper, I examine whether the use of artificial intelligence and automated decision-making aggravates issues of discrimination as has been argued by several authors. For this purpose, I first take up the lively philosophical debate on discrimination and present my own definition of the concept. Equipped with this account, I subsequently review some of the recent literature on the use AI/ADM and discrimination. I explain how my account of discrimination helps to understand that the general claim in view of (...)
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  • Epistemic injustice and data science technologies.John Symons & Ramón Alvarado - 2022 - Synthese 200 (2):1-26.
    Technologies that deploy data science methods are liable to result in epistemic harms involving the diminution of individuals with respect to their standing as knowers or their credibility as sources of testimony. Not all harms of this kind are unjust but when they are we ought to try to prevent or correct them. Epistemically unjust harms will typically intersect with other more familiar and well-studied kinds of harm that result from the design, development, and use of data science technologies. However, (...)
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  • Fairness as Equal Concession: Critical Remarks on Fair AI.Christopher Yeomans & Ryan van Nood - 2021 - Science and Engineering Ethics 27 (6):1-14.
    Although existing work draws attention to a range of obstacles in realizing fair AI, the field lacks an account that emphasizes how these worries hang together in a systematic way. Furthermore, a review of the fair AI and philosophical literature demonstrates the unsuitability of ‘treat like cases alike’ and other intuitive notions as conceptions of fairness. That review then generates three desiderata for a replacement conception of fairness valuable to AI research: It must provide a meta-theory for understanding tradeoffs, entailing (...)
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  • Examination and Diagnosis of Electronic Patient Records and Their Associated Ethics: A Scoping Literature Review.Tim Jacquemard, Colin P. Doherty & Mary B. Fitzsimons - 2020 - BMC Medical Ethics 21 (1):1-13.
    BackgroundElectronic patient record technology is a key enabler for improvements to healthcare service and management. To ensure these improvements and the means to achieve them are socially and ethically desirable, careful consideration of the ethical implications of EPRs is indicated. The purpose of this scoping review was to map the literature related to the ethics of EPR technology. The literature review was conducted to catalogue the prevalent ethical terms, to describe the associated ethical challenges and opportunities, and to identify the (...)
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  • Just Data? Solidarity and Justice in Data-Driven Medicine.Matthias Braun & Patrik Hummel - 2020 - Life Sciences, Society and Policy 16 (1):1-18.
    This paper argues that data-driven medicine gives rise to a particular normative challenge. Against the backdrop of a distinction between the good and the right, harnessing personal health data towards the development and refinement of data-driven medicine is to be welcomed from the perspective of the good. Enacting solidarity drives progress in research and clinical practice. At the same time, such acts of sharing could—especially considering current developments in big data and artificial intelligence—compromise the right by leading to injustices and (...)
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  • Modeling Ethics: Approaches to Data Creep in Higher Education.Madisson Whitman - 2021 - Science and Engineering Ethics 27 (6):1-18.
    Though rapid collection of big data is ubiquitous across domains, from industry settings to academic contexts, the ethics of big data collection and research are contested. A nexus of data ethics issues is the concept of creep, or repurposing of data for other applications or research beyond the conditions of original collection. Data creep has proven controversial and has prompted concerns about the scope of ethical oversight. Institutional review boards offer little guidance regarding big data, and problematic research can still (...)
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  • The Epistemological Foundations of Data Science: A Critical Analysis.Jules Desai, David Watson, Vincent Wang, Mariarosaria Taddeo & Luciano Floridi - manuscript
    The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of data science; (ii) the kind of enquiry (...)
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  • AI Recruitment Algorithms and the Dehumanization Problem.Megan Fritts & Frank Cabrera - 2021 - Ethics and Information Technology (4):1-11.
    According to a recent survey by the HR Research Institute, as the presence of artificial intelligence (AI) becomes increasingly common in the workplace, HR professionals are worried that the use of recruitment algorithms will lead to a “dehumanization” of the hiring process. Our main goals in this paper are threefold: i) to bring attention to this neglected issue, ii) to clarify what exactly this concern about dehumanization might amount to, and iii) to sketch an argument for why dehumanizing the hiring (...)
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  • Evaluating the Prospects for University-Based Ethical Governance in Artificial Intelligence and Data-Driven Innovation.Christine Hine - 2021 - Research Ethics 17 (4):464-479.
    There has been considerable debate around the ethical issues raised by data-driven technologies such as artificial intelligence. Ethical principles for the field have focused on the need to ensure...
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  • Assessing Biases, Relaxing Moralism: On Ground-Truthing Practices in Machine Learning Design and Application.Florian Jaton - 2021 - Big Data and Society 8 (1).
    This theoretical paper considers the morality of machine learning algorithms and systems in the light of the biases that ground their correctness. It begins by presenting biases not as a priori negative entities but as contingent external referents—often gathered in benchmarked repositories called ground-truth datasets—that define what needs to be learned and allow for performance measures. I then argue that ground-truth datasets and their concomitant practices—that fundamentally involve establishing biases to enable learning procedures—can be described by their respective morality, here (...)
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  • The Digital Phenotype: A Philosophical and Ethical Exploration.Michele Loi - 2019 - Philosophy and Technology 32 (1):155-171.
    The concept of the digital phenotype has been used to refer to digital data prognostic or diagnostic of disease conditions. Medical conditions may be inferred from the time pattern in an insomniac’s tweets, the Facebook posts of a depressed individual, or the web searches of a hypochondriac. This paper conceptualizes digital data as an extended phenotype of humans, that is as digital information produced by humans and affecting human behavior and culture. It argues that there are ethical obligations to persons (...)
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  • What has the Trolley Dilemma Ever Done for Us ? On Some Recent Debates About the Ethics of Self-Driving Cars.Andreas Wolkenstein - 2018 - Ethics and Information Technology 20 (3):163-173.
    Self-driving cars currently face a lot of technological problems that need to be solved before the cars can be widely used. However, they also face ethical problems, among which the question of crash-optimization algorithms is most prominently discussed. Reviewing current debates about whether we should use the ethics of the Trolley Dilemma as a guide towards designing self-driving cars will provide us with insights about what exactly ethical research does. It will result in the view that although we need the (...)
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  • Four Responsibility Gaps with Artificial Intelligence: Why They Matter and How to Address Them.Filippo Santoni de Sio & Giulio Mecacci - 2021 - Philosophy and Technology 34 (4):1057-1084.
    The notion of “responsibility gap” with artificial intelligence was originally introduced in the philosophical debate to indicate the concern that “learning automata” may make more difficult or impossible to attribute moral culpability to persons for untoward events. Building on literature in moral and legal philosophy, and ethics of technology, the paper proposes a broader and more comprehensive analysis of the responsibility gap. The responsibility gap, it is argued, is not one problem but a set of at least four interconnected problems (...)
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  • Problems with “Friendly AI”.Oliver Li - 2021 - Ethics and Information Technology 23 (3):543-550.
    On virtue ethical grounds, Barbro Fröding and Martin Peterson recently recommended that near-future AIs should be developed as ‘Friendly AI’. AI in social interaction with humans should be programmed such that they mimic aspects of human friendship. While it is a reasonable goal to implement AI systems interacting with humans as Friendly AI, I identify four issues that need to be addressed concerning Friendly AI with Fröding’s and Peterson’s understanding of Friendly AI as a starting point. In a first step, (...)
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  • The Influence of Business Incentives and Attitudes on Ethics Discourse in the Information Technology Industry.Sanju Ahuja & Jyoti Kumar - 2021 - Philosophy and Technology 34 (4):941-966.
    As information technologies have become synonymous with progress in modern society, several ethical concerns have surfaced about their societal implications. In the past few decades, information technologies have had a value-laden impact on social evolution. However, there is limited agreement on the responsibility of businesses and innovators concerning the ethical aspects of information technologies. There is a need to understand the role of business incentives and attitudes in driving technological progress and to understand how they steer the ethics discourse on (...)
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  • Algorithms and values in justice and security.Paul Hayes, Ibo van de Poel & Marc Steen - 2020 - AI and Society 35 (3):533-555.
    This article presents a conceptual investigation into the value impacts and relations of algorithms in the domain of justice and security. As a conceptual investigation, it represents one step in a value sensitive design based methodology. Here, we explicate and analyse the expression of values of accuracy, privacy, fairness and equality, property and ownership, and accountability and transparency in this context. We find that values are sensitive to disvalue if algorithms are designed, implemented or deployed inappropriately or without sufficient consideration (...)
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  • Artificial Intelligence, Responsibility Attribution, and a Relational Justification of Explainability.Mark Coeckelbergh - 2020 - Science and Engineering Ethics 26 (4):2051-2068.
    This paper discusses the problem of responsibility attribution raised by the use of artificial intelligence technologies. It is assumed that only humans can be responsible agents; yet this alone already raises many issues, which are discussed starting from two Aristotelian conditions for responsibility. Next to the well-known problem of many hands, the issue of “many things” is identified and the temporal dimension is emphasized when it comes to the control condition. Special attention is given to the epistemic condition, which draws (...)
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  • The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence.David S. Watson - 2019 - Minds and Machines 29 (3):417-440.
    Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...)
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  • The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity.Ulrich Leicht-Deobald, Thorsten Busch, Christoph Schank, Antoinette Weibel, Simon Schafheitle, Isabelle Wildhaber & Gabriel Kasper - 2019 - Journal of Business Ethics 160 (2):377-392.
    Organizations increasingly rely on algorithm-based HR decision-making to monitor their employees. This trend is reinforced by the technology industry claiming that its decision-making tools are efficient and objective, downplaying their potential biases. In our manuscript, we identify an important challenge arising from the efficiency-driven logic of algorithm-based HR decision-making, namely that it may shift the delicate balance between employees’ personal integrity and compliance more in the direction of compliance. We suggest that critical data literacy, ethical awareness, the use of participatory (...)
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  • Human-Aligned Artificial Intelligence is a Multiobjective Problem.Peter Vamplew, Richard Dazeley, Cameron Foale, Sally Firmin & Jane Mummery - 2018 - Ethics and Information Technology 20 (1):27-40.
    As the capabilities of artificial intelligence systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility paradigm provides insufficient (...)
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  • A Constructionist Philosophy of Logic.Patrick Allo - 2017 - Minds and Machines 27 (3):545-564.
    This paper develops and refines the suggestion that logical systems are conceptual artefacts that are the outcome of a design-process by exploring how a constructionist epistemology and meta-philosophy can be integrated within the philosophy of logic.
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  • Technology and Moral Vacuums in Just War Theorising.Elke Schwarz - 2018 - Journal of International Political Theory 14 (3):280-298.
    Our contemporary condition is deeply infused with scientific-technological rationales. These influence and shape our ethical reasoning on war, including the moral status of civilians and the moral choices available to us. In this article, I discuss how technology shapes and directs the moral choices available to us by setting parameters for moral deliberation. I argue that technology has moral significance for just war thinking, yet this is often overlooked in attempts to assess who is liable to harm in war and (...)
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  • Political machines: a framework for studying politics in social machines.Orestis Papakyriakopoulos - 2022 - AI and Society 37 (1):113-130.
    In the age of ubiquitous computing and artificially intelligent applications, social machines serves as a powerful framework for understanding and interpreting interactions in socio-algorithmic ecosystems. Although researchers have largely used it to analyze the interactions of individuals and algorithms, limited attempts have been made to investigate the politics in social machines. In this study, I claim that social machines are per se political machines, and introduce a five-point framework for classifying influence processes in socio-algorithmic ecosystems. By drawing from scholars from (...)
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  • Artificial Intelligence and the ‘Good Society’: The US, EU, and UK Approach.Corinne Cath, Sandra Wachter, Brent Mittelstadt, Mariarosaria Taddeo & Luciano Floridi - 2018 - Science and Engineering Ethics 24 (2):505-528.
    In October 2016, the White House, the European Parliament, and the UK House of Commons each issued a report outlining their visions on how to prepare society for the widespread use of artificial intelligence. In this article, we provide a comparative assessment of these three reports in order to facilitate the design of policies favourable to the development of a ‘good AI society’. To do so, we examine how each report addresses the following three topics: the development of a ‘good (...)
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  • Artificial Intelligence and Patient-Centered Decision-Making.Jens Christian Bjerring & Jacob Busch - 2020 - Philosophy and Technology 34 (2):349-371.
    Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...)
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  • The Ethics of Algorithms: Key Problems and Solutions.Andreas Tsamados, Nikita Aggarwal, Josh Cowls, Jessica Morley, Huw Roberts, Mariarosaria Taddeo & Luciano Floridi - 2021 - AI and Society.
    Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis of epistemic and normative (...)
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