Results for 'Ethics of Algorithms'

929 found
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  1.  39
    Mapping the public debate on ethical concerns: algorithms in mainstream media.Balbir S. Barn - 2019 - Journal of Information, Communication and Ethics in Society 18 (1):124-139.
    Purpose Algorithms are in the mainstream media news on an almost daily basis. Their context is invariably artificial intelligence and machine learning decision-making. In media articles, algorithms are described as powerful, autonomous actors that have a capability of producing actions that have consequences. Despite a tendency for deification, the prevailing critique of algorithms focuses on ethical concerns raised by decisions resulting from algorithmic processing. However, the purpose of this paper is to propose that the ethical concerns discussed (...)
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  2.  35
    Ethics for Robots: how to design a moral algorithm.Derek Leben - 2018 - Routledge.
    Ethics for Robots describes and defends a method for designing and evaluating ethics algorithms for autonomous machines, such as self-driving cars and search and rescue drones. Derek Leben argues that such algorithms should be evaluated by how effectively they accomplish the problem of cooperation among self-interested organisms, and therefore, rather than simulating the psychological systems that have evolved to solve this problem, engineers should be tackling the problem itself, taking relevant lessons from our moral psychology. Leben (...)
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  3.  37
    Using Algorithms to Make Ethical Judgements: METHAD vs. the ADC Model.Allen Coin & Veljko Dubljević - 2022 - American Journal of Bioethics 22 (7):41-43.
    In their paper “Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept,” Meier et al. present the design and preliminary results of a proof-of-concept clinical ethics algor...
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  4.  21
    Algorithmic ethics: algorithms and society.Michael Filimowicz (ed.) - 2023 - New York: Routledge, Taylor & Francis Group.
    This book focuses on how new technologies are raising and reshaping ethical questions and practices which aim to automate ethics into program outputs. With new powerful technologies come enhanced capacities to act, which in turn require new ethical concepts for guiding just and fair actions in the use of these new capabilities. The new algorithmic regimes, for their ethical articulation, build on prior ethics discourses in computer and information ethics, as well as the philosophical traditions of (...) generally. Especially as our technologies become more autonomous, operating alongside us in the home, workplace or on the roads, ethics has the potential to limit negative effects and shape the new technical terrains in a more humanly recognizable way. The volume covers a critique of human-centered AI, the effects of AI and the internet of things in the domain of human resource management, how decentralized finance applications on the blockchain encode ethical norms into 'smart contracts,' and the personal surveillance risks of audio beacon technology operating invisibly in our cellphones. Scholars and students from many backgrounds, as well as policy makers, journalists and the general reading public will find a multidisciplinary approach to questions posed by research in algorithmic ethics from the fields of Management, Sociology, Social Policy, Public Service, Religion and Interactive Media. (shrink)
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  5. 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 (...)
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  6.  99
    The algorithmic turn in conservation biology: Characterizing progress in ethically-driven sciences.James Justus & Samantha Wakil - 2021 - Studies in History and Philosophy of Science Part A 88 (C):181-192.
    As a discipline distinct from ecology, conservation biology emerged in the 1980s as a rigorous science focused on protecting biodiversity. Two algorithmic breakthroughs in information processing made this possible: place-prioritization algorithms and geographical information systems. They provided defensible, data-driven methods for designing reserves to conserve biodiversity that obviated the need for largely intuitive and highly problematic appeals to ecological theory at the time. But the scientific basis of these achievements and whether they constitute genuine scientific progress has been criticized. (...)
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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.  29
    Ethical Repetitions: Rhetorical Imitation and/as Algorithmic Judgment.Matthew J. Breece - 2021 - Philosophy and Rhetoric 54 (4):348-373.
    ABSTRACT In order to explore the possibilities of affirmative ethics and algorithmic judgment, this article puts machinic rhetoric in conversation with classical imitation pedagogy. Taking a machine-learning chatbot as my example, I examine how imitation and repetition in a restrictive economy of rhetorical models produces a limited affirmative ethics through dialectical relations. Drawing on Hannah Arendt's concept of representative thinking to theorize a procedure for algorithmic judgment, I argue that rhetorical training requires the affirmation of a plurality of (...)
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  9.  37
    Ethical Algorithmic Advice: Some Reasons to Pause and Think Twice.Torbjørn Gundersen & Kristine Bærøe - 2022 - American Journal of Bioethics 22 (7):26-28.
    Machine learning and other forms of artificial intelligence can improve parts of clinical decision making regarding the gathering and analysis of data, the detection of disease, and the provis...
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  10.  43
    Important Design Questions for Algorithmic Ethics Consultation.Danton Char - 2022 - American Journal of Bioethics 22 (7):38-40.
    Answering the design questions inherent to building and deploying machine learning tools —based on algorithms that can learn from and make predictions on large data sets without being explicitl...
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  11.  23
    From ‘if‐then’ to ‘what if?’ Rethinking healthcare algorithmics with posthuman speculative ethics.Jamie Smith, Goda Klumbyte & Ren Loren Britton - 2023 - Nursing Philosophy 24 (3):e12447.
    This article discusses the role that algorithmic thinking and management play in health care and the kind of exclusions this might create. We argue that evidence‐based medicine relies on research and data to create pathways for patient journeys. Coupled with data‐based algorithmic prediction tools in health care, they establish what could be called health care algorithmics—a mode of management of healthcare that produces forms of algorithmic governmentality. Relying on a critical posthumanist perspective, we show how healthcare algorithmics is contingent on (...)
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  12.  10
    Collective Reflective Equilibrium, Algorithmic Bioethics and Complex Ethics.Julian Savulescu - forthcoming - Cambridge Quarterly of Healthcare Ethics:1-16.
    John Harris has made many seminal contributions to bioethics. Two of these are in the ethics of resource allocation. Firstly, he proposed the “fair innings argument” which was the first sufficientarian approach to distributive justice. Resources should be provided to ensure people have a fair innings—when Harris first wrote this, around 70 years of life, but perhaps now 80. Secondly, Harris famously advanced the egalitarian position in response to utilitarian approaches to allocation (such as maximizing Quality Adjusted Life Years (...)
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  13.  32
    Black box algorithms in mental health apps: An ethical reflection.Tania Manríquez Roa & Nikola Biller-Andorno - 2023 - Bioethics 37 (8):790-797.
    Mental health apps bring unprecedented benefits and risks to individual and public health. A thorough evaluation of these apps involves considering two aspects that are often neglected: the algorithms they deploy and the functions they perform. We focus on mental health apps based on black box algorithms, explore their forms of opacity, discuss the implications derived from their opacity, and propose how to use their outcomes in mental healthcare, self‐care practices, and research. We argue that there is a (...)
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  14. Algorithmic Bias and Risk Assessments: Lessons from Practice.Ali Hasan, Shea Brown, Jovana Davidovic, Benjamin Lange & Mitt Regan - 2022 - Digital Society 1 (1):1-15.
    In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive nature and function of a third-party audit, and the uncertain and shifting regulatory landscape, we suggest that second-party assessments are currently the primary mechanisms for analyzing the social impacts of systems that incorporate artificial intelligence. We then discuss two kinds of as-sessments: (...)
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  15. Algorithm Evaluation Without Autonomy.Scott Hill - forthcoming - AI and Ethics.
    In Algorithms & Autonomy, Rubel, Castro, and Pham (hereafter RCP), argue that the concept of autonomy is especially central to understanding important moral problems about algorithms. In particular, autonomy plays a role in analyzing the version of social contract theory that they endorse. I argue that although RCP are largely correct in their diagnosis of what is wrong with the algorithms they consider, those diagnoses can be appropriated by moral theories RCP see as in competition with their (...)
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  16.  46
    Adaptive Interventions Reducing Social Identity Threat to Increase Equity in Higher Distance Education: A Use Case and Ethical Considerations on Algorithmic Fairness.Laura Froehlich & Sebastian Weydner-Volkmann - 2024 - Journal of Learning Analytics 11 (2):112-122.
    Educational disparities between traditional and non-traditional student groups in higher distance education can potentially be reduced by alleviating social identity threat and strengthening students’ sense of belonging in the academic context. We present a use case of how Learning Analytics and Machine Learning can be applied to develop and implement an algorithm to classify students as at-risk of experiencing social identity threat. These students would be presented with an intervention fostering a sense of belonging. We systematically analyze the intervention’s intended (...)
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  17.  51
    Advertising Benefits from Ethical Artificial Intelligence Algorithmic Purchase Decision Pathways.Waymond Rodgers & Tam Nguyen - 2022 - Journal of Business Ethics 178 (4):1043-1061.
    Artificial intelligence has dramatically changed the way organizations communicate, understand, and interact with their potential consumers. In the context of this trend, the ethical considerations of advertising when applying AI should be the core question for marketers. This paper discusses six dominant algorithmic purchase decision pathways that align with ethical philosophies for online customers when buying a product/goods. The six ethical positions include: ethical egoism, deontology, relativist, utilitarianism, virtue ethics, and ethics of care. Furthermore, this paper launches an (...)
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  18.  85
    Caring in an Algorithmic World: Ethical Perspectives for Designers and Developers in Building AI Algorithms to Fight Fake News.Galit Wellner & Dmytro Mykhailov - 2023 - Science and Engineering Ethics 29 (4):1-16.
    This article suggests several design principles intended to assist in the development of ethical algorithms exemplified by the task of fighting fake news. Although numerous algorithmic solutions have been proposed, fake news still remains a wicked socio-technical problem that begs not only engineering but also ethical considerations. We suggest employing insights from ethics of care while maintaining its speculative stance to ask how algorithms and design processes would be different if they generated care and fight fake news. (...)
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  19.  13
    Writing a Moral Code: Algorithms for Ethical Reasoning by Humans and Machines.James F. McGrath & Ankur Gupta - unknown
    The moral and ethical challenges of living in community pertain not only to the intersection of human beings one with another, but also our interactions with our machine creations. This article explores the philosophical and theological framework for reasoning and decision-making through the lens of science fiction, religion, and artificial intelligence (both real and imagined). In comparing the programming of autonomous machines with human ethical deliberation, we discover that both depend on a concrete ordering of priorities derived from a clearly (...)
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  20.  45
    How to program autonomous vehicle (AV) crash algorithms: an Islamic ethical perspective.Ezieddin Elmahjub & Junaid Qadir - 2023 - Journal of Information, Communication and Ethics in Society 21 (4):452-467.
    Purpose Fully autonomous self-driving cars not only hold the potential for significant economic and environmental advantages but also introduce complex ethical dilemmas. One of the highly debated issues, known as the “trolley problems,” revolves around determining the appropriate actions for a self-driving car when faced with an unavoidable crash. Currently, the discourse on autonomous vehicle (AV) crash algorithms is primarily shaped by Western ethical traditions, resulting in a Eurocentric bias due to the dominant economic and political influence of the (...)
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  21. Crash Algorithms for Autonomous Cars: How the Trolley Problem Can Move Us Beyond Harm Minimisation.Dietmar Hübner & Lucie White - 2018 - Ethical Theory and Moral Practice 21 (3):685-698.
    The prospective introduction of autonomous cars into public traffic raises the question of how such systems should behave when an accident is inevitable. Due to concerns with self-interest and liberal legitimacy that have become paramount in the emerging debate, a contractarian framework seems to provide a particularly attractive means of approaching this problem. We examine one such attempt, which derives a harm minimisation rule from the assumptions of rational self-interest and ignorance of one’s position in a future accident. We contend, (...)
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  22. Algorithms as Emerging Policy Tools in Medicine : Opportunities and Challenges Ahead.Frederick Bouder - 2021 - In Ulrik Kihlbom, Mats G. Hansson & Silke Schicktanz, Ethical, social and psychological impacts of genomic risk communication. New York, NY: Routledge.
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  23.  21
    Bearing Account-able Witness to the Ethical Algorithmic System.Daniel Neyland - 2016 - Science, Technology, and Human Values 41 (1):50-76.
    This paper explores how accountability might make otherwise obscure and inaccessible algorithms available for governance. The potential import and difficulty of accountability is made clear in the compelling narrative reproduced across recent popular and academic reports. Through this narrative we are told that algorithms trap us and control our lives, undermine our privacy, have power and an independent agential impact, at the same time as being inaccessible, reducing our opportunities for critical engagement. The paper suggests that STS sensibilities (...)
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  24. 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 (...)
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  25.  44
    Choosing how to discriminate: navigating ethical trade-offs in fair algorithmic design for the insurance sector.Michele Loi & Markus Christen - 2021 - Philosophy and Technology 34 (4):967-992.
    Here, we provide an ethical analysis of discrimination in private insurance to guide the application of non-discriminatory algorithms for risk prediction in the insurance context. This addresses the need for ethical guidance of data-science experts, business managers, and regulators, proposing a framework of moral reasoning behind the choice of fairness goals for prediction-based decisions in the insurance domain. The reference to private insurance as a business practice is essential in our approach, because the consequences of discrimination and predictive inaccuracy (...)
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  26.  36
    Recognize Everyone’s Interests: An Algorithm for Ethical Decision-Making about Trade-Off Problems.Tobey K. Scharding - 2021 - Business Ethics Quarterly 31 (3):450-473.
    This article addresses a dilemma about autonomous vehicles: how to respond to trade-off scenarios in which all possible responses involve the loss of life but there is a choice about whose life or lives are lost. I consider four options: kill fewer people, protect passengers, equal concern for survival, and recognize everyone’s interests. I solve this dilemma via what I call the new trolley problem, which seeks a rationale for the intuition that it is unethical to kill a smaller number (...)
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  27. 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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  28.  55
    Algorithmic Democracy: A Critical Perspective Based on Deliberative Democracy.Domingo García-Marzá & Patrici Calvo - 2024 - Springer Verlag.
    Based on a deliberative democracy, this book uses a hermeneutic-critical methodology to study bibliographical sources and practical issues in order to analyse the possibilities, limits and consequences of the digital transformation of democracy. Drawing on a two-way democracy, the aim of this book is intended as an aid for thinking through viable alternatives to the current state of democracy with regard to its ethical foundations and the moral knowledge implicit in or assumed by the way we perceive and understand democracy. (...)
  29. 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 (...)
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  30. (1 other version)Algorithmic fairness in mortgage lending: from absolute conditions to relational trade-offs.Michelle Seng Ah Lee & Luciano Floridi - 2020 - 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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  31. Disambiguating Algorithmic Bias: From Neutrality to Justice.Elizabeth Edenberg & Alexandra Wood - 2023 - In Francesca Rossi, Sanmay Das, Jenny Davis, Kay Firth-Butterfield & Alex John, 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 (...)
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  32. From Confucius to Coding and Avicenna to Algorithms: Cultivating Ethical AI Development through Cross-Cultural Ancient Wisdom.Ammar Younas & Yi Zeng - manuscript
    This paper explores the potential of integrating ancient educational principles from diverse eastern cultures into modern AI ethics curricula. It draws on the rich educational traditions of ancient China, India, Arabia, Persia, Japan, Tibet, Mongolia, and Korea, highlighting their emphasis on philosophy, ethics, holistic development, and critical thinking. By examining these historical educational systems, the paper establishes a correlation with modern AI ethics principles, advocating for the inclusion of these ancient teachings in current AI development and education. (...)
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  33. 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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  34.  28
    Algorithmic affordances for productive resistance.Nancy Ettlinger - 2018 - Big Data and Society 5 (1).
    Although overarching if not foundational conceptualizations of digital governance in the field of critical data studies aptly account for and explain subjection, calculated resistance is left conceptually unattended despite case studies that document instances of resistance. I ask at the outset why conceptualizations of digital governance are so bleak, and I argue that all are underscored implicitly by a Deleuzian theory of desire that overlooks agency, defined here in Foucauldian terms. I subsequently conceptualize digital governance as encompassing subjection as well (...)
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  35. Agency Laundering and Algorithmic Decision Systems.Alan Rubel, Adam Pham & Clinton Castro - 2019 - In N. Taylor, C. Christian-Lamb, M. Martin & B. Nardi, Information in Contemporary Society (Lecture Notes in Computer Science). Springer Nature. pp. 590-598.
    This paper has two aims. The first is to explain a type of wrong that arises when agents obscure responsibility for their actions. Call it “agency laundering.” The second is to use the concept of agency laundering to understand the underlying moral issues in a number of recent cases involving algorithmic decision systems. From the Proceedings of the 14th International Conference, iConference 2019, Washington D.C., March 31-April 3, 2019.
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  36. Algorithmic Indirect Discrimination, Fairness, and Harm.Frej Klem Thomsen - 2023 - AI and Ethics.
    Over the past decade, scholars, institutions, and activists have voiced strong concerns about the potential of automated decision systems to indirectly discriminate against vulnerable groups. This article analyses the ethics of algorithmic indirect discrimination, and argues that we can explain what is morally bad about such discrimination by reference to the fact that it causes harm. The article first sketches certain elements of the technical and conceptual background, including definitions of direct and indirect algorithmic differential treatment. It next introduces (...)
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  37.  33
    Algorithms as folding: Reframing the analytical focus.Robin Williams, Claes-Fredrik Helgesson, Lukas Engelmann, Jeffrey Christensen, Jess Bier & Francis Lee - 2019 - Big Data and Society 6 (2).
    This article proposes an analytical approach to algorithms that stresses operations of folding. The aim of this approach is to broaden the common analytical focus on algorithms as biased and opaque black boxes, and to instead highlight the many relations that algorithms are interwoven with. Our proposed approach thus highlights how algorithms fold heterogeneous things: data, methods and objects with multiple ethical and political effects. We exemplify the utility of our approach by proposing three specific operations (...)
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  38. (1 other version)Attention, Moral Skill, and Algorithmic Recommendation.Nick Schuster & Seth Lazar - 2024 - Philosophical Studies 182 (1).
    Recommender systems are artificial intelligence technologies, deployed by online platforms, that model our individual preferences and direct our attention to content we’re likely to engage with. As the digital world has become increasingly saturated with information, we’ve become ever more reliant on these tools to efficiently allocate our attention. And our reliance on algorithmic recommendation may, in turn, reshape us as moral agents. While recommender systems could in principle enhance our moral agency by enabling us to cut through the information (...)
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  39. Algorithms are not neutral: Bias in collaborative filtering.Catherine Stinson - 2021 - 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 (...)
     
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  40.  10
    Occluded algorithms.Adam Burke - 2019 - Big Data and Society 6 (2).
    Two definitions of algorithm, their uses, and their implied models of computing in society, are reviewed. The first, termed the structural programming definition, aligns more with usage in computer science, and as the name suggests, the intellectual project of structured programming. The second, termed the systemic definition, is more informal and emerges from ethnographic observations of discussions of software in both professional and everyday settings. Specific examples of locating algorithms within modern codebases are shared, as well as code directly (...)
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  41. The Ideals Program in Algorithmic Fairness.Rush T. Stewart - forthcoming - AI and Society:1-11.
    I consider statistical criteria of algorithmic fairness from the perspective of the _ideals_ of fairness to which these criteria are committed. I distinguish and describe three theoretical roles such ideals might play. The usefulness of this program is illustrated by taking Base Rate Tracking and its ratio variant as a case study. I identify and compare the ideals of these two criteria, then consider them in each of the aforementioned three roles for ideals. This ideals program may present a way (...)
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  42.  41
    An Algorithmic Perpetrator, or Why We Need to Acknowledge the Many Things We Do Not (Yet) Know.Kristina Khutsishvili - 2024 - Depictions.
    Rapid technological developments may exacerbate the victimhood already experienced by vulnerable individuals and communities. At the same time, broad societal anxieties induced by technology lead to the perception of algorithms, these entities of the unknown, as perpetrators. In this essay, I argue that these tendencies can be addressed by a nuanced process of technological co-creation and by the fostering of a public discourse in which “experts” and “public” are united in the acknowledgment of a shared vulnerability before the unknown, (...)
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  43.  35
    Artificial Misinformation: Exploring Human-Algorithm Interaction Online.Donghee Shin - 2024 - Springer Nature Switzerland.
    This book serves as a guide to understanding the dynamics of AI in human contexts with a specific focus on the generation, sharing, and consumption of misinformation online. How do humans and AI interact? How is AI shaping our understanding of ourselves and our societies? What are the interaction mechanisms that govern how humans and algorithms contribute to misinformation online? And how do we bridge the gap between ethical considerations and practical realities to make responsible, reliable systems? Exploring these (...)
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  44.  37
    Algorithmic Recommender Systems.Susan Kennedy - 2024 - American Philosophical Quarterly 61 (4):327-338.
    Despite their ethical challenges, recommender systems (RS) are widely endorsed as a necessary solution to the problem of information overload. After clarifying how the harmful effects of information overload can be characterized in distinct ways, I explore the often overlooked potential benefits of abundant online spaces. I argue that these spaces afford valuable opportunities to experience spontaneous freedom. I then put forth a more comprehensive evaluation of the role RS should assume in algorithmically structuring the online space. This evaluation aims (...)
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  45.  1
    The Fertility Fix: the Boom in Facial-matching Algorithms for Donor Selection in Assisted Reproduction in Spain.Rebecca Close - forthcoming - The New Bioethics:1-17.
    This article reads the uptake of facial-matching algorithms by fertility clinics in Spain through the lens of ‘the fertility fix’: a software fix to the social reconfiguration of kinship and a fixed capital investment made by competing fertility companies and firms. ‘The fertility fix’ is proposed as a critical, ethical lens through which to situate algorithmic facial-matching in assisted reproduction in the context of the racial politics of the face and phenotype and the spatial politics of market expansion. While (...)
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  46. The ethical debate about the gig economy: a review and critical analysis.Zhi Ming Tan, Nikita Aggarwal, Josh Cowls, Jessica Morley, Mariarosaria Taddeo & Luciano Floridi - 2021 - Technology in Society 65 (2):101954.
    The gig economy is a phenomenon that is rapidly expanding, redefining the nature of work and contributing to a significant change in how contemporary economies are organised. Its expansion is not unproblematic. This article provides a clear and systematic analysis of the main ethical challenges caused by the gig economy. Following a brief overview of the gig economy, its scope and scale, we map the key ethical problems that it gives rise to, as they are discussed in the relevant literature. (...)
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  47.  57
    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 (...)
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    An Algorithmic Approach to Patients Who Refuse Care But Lack Medical Decision-Making Capacity.Maura George, Kevin Wack, Sindhuja Surapaneni & Stephanie A. Larson - 2019 - Journal of Clinical Ethics 30 (4):331-337.
    Situations in which patients lack medical decision-making (MDM) capacity raise ethical challenges, especially when the patients decline care that their surrogate decision makers and/or clinicians agree is indicated. These patients are a vulnerable population and should receive treatment that is the standard of care, in line with their the values of their authentic self, just as any other patient would. But forcing treatment on patients who refuse it, even though they lack capacity, carries medical and psychological risks to the patients (...)
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  49. 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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  50.  16
    An Algorithm for Determining Best Interest?Muriel R. Gillick - 1995 - Journal of Clinical Ethics 6 (1):82-85.
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