Results for 'moral algorithm,'

988 found
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  1.  28
    Toward safe AI.Andres Morales-Forero, Samuel Bassetto & Eric Coatanea - 2023 - AI and Society 38 (2):685-696.
    Since some AI algorithms with high predictive power have impacted human integrity, safety has become a crucial challenge in adopting and deploying AI. Although it is impossible to prevent an algorithm from failing in complex tasks, it is crucial to ensure that it fails safely, especially if it is a critical system. Moreover, due to AI’s unbridled development, it is imperative to minimize the methodological gaps in these systems’ engineering. This paper uses the well-known Box-Jenkins method for statistical modeling as (...)
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  2.  17
    Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store.Jaikishan Khatri, Javier Marín-Morales, Masoud Moghaddasi, Jaime Guixeres, Irene Alice Chicchi Giglioli & Mariano Alcañiz - 2022 - Frontiers in Psychology 13.
    Virtual reality is a useful tool to study consumer behavior while they are immersed in a realistic scenario. Among several other factors, personality traits have been shown to have a substantial influence on purchasing behavior. The primary objective of this study was to classify consumers based on the Big Five personality domains using their behavior while performing different tasks in a virtual shop. The personality recognition was ascertained using behavioral measures received from VR hardware, including eye-tracking, navigation, posture and interaction. (...)
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  3.  10
    Self-Esteem at University: Proposal of an Artificial Neural Network Based on Resilience, Stress, and Sociodemographic Variables.Juan Pedro Martínez-Ramón, Francisco Manuel Morales-Rodríguez, Cecilia Ruiz-Esteban & Inmaculada Méndez - 2022 - Frontiers in Psychology 13.
    Artificial intelligence is a useful predictive tool for a wide variety of fields of knowledge. Despite this, the educational field is still an environment that lacks a variety of studies that use this type of predictive tools. In parallel, it is postulated that the levels of self-esteem in the university environment may be related to the strategies implemented to solve problems. For these reasons, the aim of this study was to analyze the levels of self-esteem presented by teaching staff and (...)
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  4.  21
    Computing probability intervals with simulated annealing and probability trees.Andrés Cano, Juan M. Fernández-Luna & Serafín Moral - 2002 - Journal of Applied Non-Classical Logics 12 (2):151-171.
    This paper presents a method to compute a posteriori probability intervals when the initial conditional information is also given with probability intervals. The right way to make an exact computation is with the associated convex set of probabilities. Probability trees are used to represent these initial conditional convex sets because they greatly save the space required. This paper proposes a simulated annealing algorithm, which uses probability trees to represent the convex sets in order to compute the a posteriori intervals.
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  5.  26
    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 draws on the (...)
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  6. Moral zombies: why algorithms are not moral agents.Carissa Véliz - 2021 - AI and Society 36 (2):487-497.
    In philosophy of mind, zombies are imaginary creatures that are exact physical duplicates of conscious subjects but for whom there is no first-personal experience. Zombies are meant to show that physicalism—the theory that the universe is made up entirely out of physical components—is false. In this paper, I apply the zombie thought experiment to the realm of morality to assess whether moral agency is something independent from sentience. Algorithms, I argue, are a kind of functional moral zombie, such (...)
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  7.  44
    Why Moral Agreement is Not Enough to Address Algorithmic Structural Bias.P. Benton - 2022 - Communications in Computer and Information Science 1551:323-334.
    One of the predominant debates in AI Ethics is the worry and necessity to create fair, transparent and accountable algorithms that do not perpetuate current social inequities. I offer a critical analysis of Reuben Binns’s argument in which he suggests using public reason to address the potential bias of the outcomes of machine learning algorithms. In contrast to him, I argue that ultimately what is needed is not public reason per se, but an audit of the implicit moral assumptions (...)
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  8.  55
    Attention, moral skill, and algorithmic recommendation.Nick Schuster & Seth Lazar - forthcoming - Philosophical Studies:1-26.
    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 (...)
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  9.  7
    CAN Algorithm: An Individual Level Approach to Identify Consequence and Norm Sensitivities and Overall Action/Inaction Preferences in Moral Decision-Making.Chuanjun Liu & Jiangqun Liao - 2021 - Frontiers in Psychology 11.
    Recently, a multinomial process tree model was developed to measure an agent’s consequence sensitivity, norm sensitivity, and generalized inaction/action preferences when making moral decisions (CNI model). However, the CNI model presupposed that an agent considersconsequences—norms—generalizedinaction/actionpreferences sequentially, which is untenable based on recent evidence. Besides, the CNI model generates parameters at the group level based on binary categorical data. Hence, theC/N/Iparameters cannot be used for correlation analyses or other conventional research designs. To solve these limitations, we developed the CAN algorithm (...)
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  10.  42
    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 (...)
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  11.  55
    Weapons of moral construction? On the value of fairness in algorithmic decision-making.Simona Tiribelli & Benedetta Giovanola - 2022 - Ethics and Information Technology 24 (1):1-13.
    Fairness is one of the most prominent values in the Ethics and Artificial Intelligence (AI) debate and, specifically, in the discussion on algorithmic decision-making (ADM). 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 (...)
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  12.  67
    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 (...)
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  13. Algorithms, Agency, and Respect for Persons.Alan Rubel, Clinton Castro & Adam Pham - 2020 - Social Theory and Practice 46 (3):547-572.
    Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key (...)
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  14.  16
    Effects of Moral Violation on Algorithmic Transparency: An Empirical Investigation.Muhammad Umair Shah, Umair Rehman, Bidhan Parmar & Inara Ismail - forthcoming - Journal of Business Ethics:1-16.
    Workers can be fired from jobs, citizens sent to jail, and adolescents more likely to experience depression, all because of algorithms. Algorithms have considerable impacts on our lives. To increase user satisfaction and trust, the most common proposal from academics and developers is to increase the transparency of algorithmic design. While there is a large body of literature on algorithmic transparency, the impact of unethical data collection practices is less well understood. Currently, there is limited research on the factors that (...)
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  15.  45
    The Eurotransplant Kidney Allocation Algorithm–Moral Consensus or Pragmatic Compromise?Georg Marckmann - 2001 - Analyse & Kritik 23 (2):271-279.
    The selection and balancing of values for the Eurotransplant kidney allocation algorithm poses both practical and ethical challenges. The paper argues that any allocation algorithm can only be justified by reference to some substantive conception of a good life that reflects our value preferences regarding the allocation of scarce donor kidneys. It is concluded that the criterion of HLA compatibility maximizes overall rather than individual utility. The paper emphasizes that good pragmatic arguments for maintaining the primacy of HLA matching can (...)
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  16. 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 (...)
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  17.  5
    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 (...)
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  18. 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 (...)
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  19. Algorithms and Autonomy: The Ethics of Automated Decision Systems.Alan Rubel, Clinton Castro & Adam Pham - 2021 - Cambridge University Press.
    Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. Using (...)
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  20.  17
    Correction to: Weapons of moral construction? On the value of fairness in algorithmic decision-making.Simona Tiribelli & Benedetta Giovanola - 2024 - Ethics and Information Technology 26 (1):1-1.
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  21.  30
    Managing Algorithmic Accountability: Balancing Reputational Concerns, Engagement Strategies, and the Potential of Rational Discourse.Alexander Buhmann, Johannes Paßmann & Christian Fieseler - 2020 - Journal of Business Ethics 163 (2):265-280.
    While organizations today make extensive use of complex algorithms, the notion of algorithmic accountability remains an elusive ideal due to the opacity and fluidity of algorithms. In this article, we develop a framework for managing algorithmic accountability that highlights three interrelated dimensions: reputational concerns, engagement strategies, and discourse principles. The framework clarifies that accountability processes for algorithms are driven by reputational concerns about the epistemic setup, opacity, and outcomes of algorithms; that the way in which organizations practically engage with emergent (...)
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  22. Disambiguating Algorithmic Bias: From Neutrality to Justice.Elizabeth Edenberg & Alexandra Wood - 2023 - In Francesca Rossi, Sanmay Das, Jenny Davis, Kay Firth-Butterfield & Alex John (eds.), AIES '23: Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society. Association for Computing Machinery. pp. 691-704.
    As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered by conflations (...)
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  23.  36
    The ethics of Smart City (EoSC): moral implications of hyperconnectivity, algorithmization and the datafication of urban digital society.Patrici Calvo - 2020 - Ethics and Information Technology 22 (2):141-149.
    Cities, such as industry or the universities, are immersed in a process of digital transformation generated by the possibility and technological convergence of the Internet of Things, Big Data and Artificial Intelligence and its consequences: hyperconnectivity, datafication and algorithmization. A process of transformation towards what has come to be called as Smart Cities. The aim of this paper is to show the impacts and consequences of digital connectivity, algorithmization and the datafication of urban digital society to outline possible ways of (...)
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  24. 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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  25.  47
    Algorithmic fairness and resentment.Boris Babic & Zoë Johnson King - forthcoming - Philosophical Studies:1-33.
    In this paper we develop a general theory of algorithmic fairness. Drawing on Johnson King and Babic’s work on moral encroachment, on Gary Becker’s work on labor market discrimination, and on Strawson’s idea of resentment and indignation as responses to violations of the demand for goodwill toward oneself and others, we locate attitudes to fairness in an agent’s utility function. In particular, we first argue that fairness is a matter of a decision-maker’s relative concern for the plight of people (...)
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  26. 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 (...)
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  27. 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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  28.  12
    Bioethics of Things: on the algorithmization of moral deliberation in clinical practice.Patrici Calvo - 2019 - Filosofia Unisinos 20 (2).
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  29. Algorithms are not neutral: Bias in collaborative filtering.Catherine Stinson - 2022 - AI and Ethics 2 (4):763-770.
    When Artificial Intelligence (AI) is applied in decision-making that affects people’s lives, it is now well established that the outcomes can be biased or discriminatory. The question of whether algorithms themselves can be among the sources of bias has been the subject of recent debate among Artificial Intelligence researchers, and scholars who study the social impact of technology. There has been a tendency to focus on examples, where the data set used to train the AI is biased, and denial on (...)
     
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  30.  9
    Algorithmic Fairness, Risk, and the Dominant Protective Agency.Ulrik Franke - 2023 - Philosophy and Technology 36 (4):1-7.
    With increasing use of automated algorithmic decision-making, issues of algorithmic fairness have attracted much attention lately. In this growing literature, existing concepts from ethics and political philosophy are often applied to new contexts. The reverse—that novel insights from the algorithmic fairness literature are fed back into ethics and political philosophy—is far less established. However, this short commentary on Baumann and Loi (Philosophy & Technology, 36(3), 45 2023) aims to do precisely this. Baumann and Loi argue that among algorithmic group fairness (...)
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  31.  37
    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 (...)
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  32.  27
    Are Algorithmic Decisions Legitimate? The Effect of Process and Outcomes on Perceptions of Legitimacy of AI Decisions.Kirsten Martin & Ari Waldman - 2022 - Journal of Business Ethics 183 (3):653-670.
    Firms use algorithms to make important business decisions. To date, the algorithmic accountability literature has elided a fundamentally empirical question important to business ethics and management: Under what circumstances, if any, are algorithmic decision-making systems considered legitimate? The present study begins to answer this question. Using factorial vignette survey methodology, we explore the impact of decision importance, governance, outcomes, and data inputs on perceptions of the legitimacy of algorithmic decisions made by firms. We find that many of the procedural governance (...)
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  33.  34
    Algorithmic Decision-making, Statistical Evidence and the Rule of Law.Vincent Chiao - forthcoming - Episteme:1-24.
    The rapidly increasing role of automation throughout the economy, culture and our personal lives has generated a large literature on the risks of algorithmic decision-making, particularly in high-stakes legal settings. Algorithmic tools are charged with bias, shrouded in secrecy, and frequently difficult to interpret. However, these criticisms have tended to focus on particular implementations, specific predictive techniques, and the idiosyncrasies of the American legal-regulatory regime. They do not address the more fundamental unease about the prospect that we might one day (...)
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  34. Algorithmic Microaggressions.Emma McClure & Benjamin Wald - 2022 - Feminist Philosophy Quarterly 8 (3).
    We argue that machine learning algorithms can inflict microaggressions on members of marginalized groups and that recognizing these harms as instances of microaggressions is key to effectively addressing the problem. The concept of microaggression is also illuminated by being studied in algorithmic contexts. We contribute to the microaggression literature by expanding the category of environmental microaggressions and highlighting the unique issues of moral responsibility that arise when we focus on this category. We theorize two kinds of algorithmic microaggression, stereotyping (...)
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  35. Should Algorithms that Predict Recidivism Have Access to Race?Duncan Purves & Jeremy Davis - 2023 - American Philosophical Quarterly 60 (2):205-220.
    Recent studies have shown that recidivism scoring algorithms like COMPAS have significant racial bias: Black defendants are roughly twice as likely as white defendants to be mistakenly classified as medium- or high-risk. This has led some to call for abolishing COMPAS. But many others have argued that algorithms should instead be given access to a defendant's race, which, perhaps counterintuitively, is likely to improve outcomes. This approach can involve either establishing race-sensitive risk thresholds, or distinct racial ‘tracks’. Is there a (...)
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  36. On the ethics of algorithmic decision-making in healthcare.Thomas Grote & Philipp Berens - 2020 - Journal of Medical Ethics 46 (3):205-211.
    In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical (...)
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  37. The Ethics of Accident-Algorithms for Self-Driving Cars: an Applied Trolley Problem?Sven Nyholm & Jilles Smids - 2016 - Ethical Theory and Moral Practice 19 (5):1275-1289.
    Self-driving cars hold out the promise of being safer than manually driven cars. Yet they cannot be a 100 % safe. Collisions are sometimes unavoidable. So self-driving cars need to be programmed for how they should respond to scenarios where collisions are highly likely or unavoidable. The accident-scenarios self-driving cars might face have recently been likened to the key examples and dilemmas associated with the trolley problem. In this article, we critically examine this tempting analogy. We identify three important ways (...)
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  38. 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 three (...)
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  39. Deadly Algorithms.Susan Schuppli - 2015 - Continent 4 (4).
    It has long been argued that robotic systems decrease the error margin of civilian casualties that are often the consequence of human error. The focus on algorithmic decision-making and the gradual reduction of human control unveils moral and juristic issues that lead to the crucial question: can we take algorithms to court?
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  40. Three Lessons For and From Algorithmic Discrimination.Frej Klem Thomsen - 2023 - Res Publica (2):1-23.
    Algorithmic discrimination has rapidly become a topic of intense public and academic interest. This article explores three issues raised by algorithmic discrimination: 1) the distinction between direct and indirect discrimination, 2) the notion of disadvantageous treatment, and 3) the moral badness of discriminatory automated decision-making. It argues that some conventional distinctions between direct and indirect discrimination appear not to apply to algorithmic discrimination, that algorithmic discrimination may often be discrimination between groups, as opposed to against groups, and that it (...)
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  41.  63
    Justice by Algorithm: The Limits of AI in Criminal Sentencing.Isaac Taylor - 2023 - Criminal Justice Ethics 42 (3):193-213.
    Criminal justice systems have traditionally relied heavily on human decision-making, but new technologies are increasingly supplementing the human role in this sector. This paper considers what general limits need to be placed on the use of algorithms in sentencing decisions. It argues that, even once we can build algorithms that equal human decision-making capacities, strict constraints need to be placed on how they are designed and developed. The act of condemnation is a valuable element of criminal sentencing, and using algorithms (...)
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  42.  11
    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 ethics generally. Especially as (...)
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  43.  94
    What is “Race” in Algorithmic Discrimination on the Basis of Race?Lily Hu - 2023 - Journal of Moral Philosophy 21 (1-2):1-26.
    Machine learning algorithms bring out an under-appreciated puzzle of discrimination, namely figuring out when a decision made on the basis of a factor correlated with race is a decision made on the basis of race. I argue that prevailing approaches, which are based on identifying and then distinguishing among causal effects of race, in their metaphysical timidity, fail to get off the ground. I suggest, instead, that adopting a constructivist theory of race answers this puzzle in a principled manner. On (...)
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  44.  9
    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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  45. Agency Laundering and Algorithmic Decision Systems.Alan Rubel, Adam Pham & Clinton Castro - 2019 - In N. Taylor, C. Christian-Lamb, M. Martin & B. Nardi (eds.), 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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  46.  41
    Fairness in Algorithmic Policing.Duncan Purves - 2022 - Journal of the American Philosophical Association 8 (4):741-761.
    Predictive policing, the practice of using of algorithmic systems to forecast crime, is heralded by police departments as the new frontier of crime analysis. At the same time, it is opposed by civil rights groups, academics, and media outlets for being ‘biased’ and therefore discriminatory against communities of color. This paper argues that the prevailing focus on racial bias has overshadowed two normative factors that are essential to a full assessment of the moral permissibility of predictive policing: fairness in (...)
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  47. Bias in Information, Algorithms, and Systems.Alan Rubel, Clinton Castro & Adam Pham - 2018 - In Jo Bates, Paul D. Clough, Robert Jäschke & Jahna Otterbacher (eds.), Proceedings of the International Workshop on Bias in Information, Algorithms, and Systems (BIAS). pp. 9-13.
    We argue that an essential element of understanding the moral salience of algorithmic systems requires an analysis of the relation between algorithms and agency. We outline six key ways in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making.
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  48. Clinical applications of machine learning algorithms: beyond the black box.David S. Watson, Jenny Krutzinna, Ian N. Bruce, Christopher E. M. Griffiths, Iain B. McInnes, Michael R. Barnes & Luciano Floridi - 2019 - British Medical Journal 364:I886.
    Machine learning algorithms may radically improve our ability to diagnose and treat disease. For moral, legal, and scientific reasons, it is essential that doctors and patients be able to understand and explain the predictions of these models. Scalable, customisable, and ethical solutions can be achieved by working together with relevant stakeholders, including patients, data scientists, and policy makers.
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  49. Ethical Implications and Accountability of Algorithms.Kirsten Martin - 2018 - Journal of Business Ethics 160 (4):835-850.
    Algorithms silently structure our lives. Algorithms can determine whether someone is hired, promoted, offered a loan, or provided housing as well as determine which political ads and news articles consumers see. Yet, the responsibility for algorithms in these important decisions is not clear. This article identifies whether developers have a responsibility for their algorithms later in use, what those firms are responsible for, and the normative grounding for that responsibility. I conceptualize algorithms as value-laden, rather than neutral, in that algorithms (...)
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    Mapping the Ethicality of Algorithmic Pricing: A Review of Dynamic and Personalized Pricing. [REVIEW]Peter Seele, Claus Dierksmeier, Reto Hofstetter & Mario D. Schultz - 2019 - Journal of Business Ethics 170 (4):697-719.
    Firms increasingly deploy algorithmic pricing approaches to determine what to charge for their goods and services. Algorithmic pricing can discriminate prices both dynamically over time and personally depending on individual consumer information. Although legal, the ethicality of such approaches needs to be examined as often they trigger moral concerns and sometimes outrage. In this research paper, we provide an overview and discussion of the ethical challenges germane to algorithmic pricing. As a basis for our discussion, we perform a systematic (...)
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