Results for 'Algorithmic Discrimination'

964 found
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  1. Negligent Algorithmic Discrimination.Andrés Páez - 2021 - Law and Contemporary Problems 84 (3):19-33.
    The use of machine learning algorithms has become ubiquitous in hiring decisions. Recent studies have shown that many of these algorithms generate unlawful discriminatory effects in every step of the process. The training phase of the machine learning models used in these decisions has been identified as the main source of bias. For a long time, discrimination cases have been analyzed under the banner of disparate treatment and disparate impact, but these concepts have been shown to be ineffective in (...)
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  2. 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 (...)
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  3. 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. (...)
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  4. 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 (...)
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  5. An Epistemic Lens on Algorithmic Fairness.Elizabeth Edenberg & Alexandra Wood - 2023 - Eaamo '23: Proceedings of the 3Rd Acm Conference on Equity and Access in Algorithms, Mechanisms, and Optimization.
    In this position paper, we introduce a new epistemic lens for analyzing algorithmic harm. We argue that the epistemic lens we propose herein has two key contributions to help reframe and address some of the assumptions underlying inquiries into algorithmic fairness. First, we argue that using the framework of epistemic injustice helps to identify the root causes of harms currently framed as instances of representational harm. We suggest that the epistemic lens offers a theoretical foundation for expanding approaches (...)
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  6. Algorithmic Political Bias in Artificial Intelligence Systems.Uwe Peters - 2022 - Philosophy and Technology 35 (2):1-23.
    Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political (...)
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  7. Are algorithms always arbitrary? Three types of arbitrariness and ways to overcome the computationalist’s trilemma.C. Percy - manuscript
    Implementing an algorithm on part of our causally-interconnected physical environment requires three choices that are typically considered arbitrary, i.e. no single option is innately privileged without invoking an external observer perspective. First, how to delineate one set of local causal relationships from the environment. Second, within this delineation, which inputs and outputs to designate for attention. Third, what meaning to assign to particular states of the designated inputs and outputs. Having explained these types of arbitrariness, we assess their relevance for (...)
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  8. Formalising trade-offs beyond algorithmic fairness: lessons from ethical philosophy and welfare economics.Michelle Seng Ah Lee, Luciano Floridi & Jatinder Singh - 2021 - AI and Ethics 3.
    There is growing concern that decision-making informed by machine learning (ML) algorithms may unfairly discriminate based on personal demographic attributes, such as race and gender. Scholars have responded by introducing numerous mathematical definitions of fairness to test the algorithm, many of which are in conflict with one another. However, these reductionist representations of fairness often bear little resemblance to real-life fairness considerations, which in practice are highly contextual. Moreover, fairness metrics tend to be implemented in narrow and targeted toolkits that (...)
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  9. From human resources to human rights: Impact assessments for hiring algorithms.Josephine Yam & Joshua August Skorburg - 2021 - Ethics and Information Technology 23 (4):611-623.
    Over the years, companies have adopted hiring algorithms because they promise wider job candidate pools, lower recruitment costs and less human bias. Despite these promises, they also bring perils. Using them can inflict unintentional harms on individual human rights. These include the five human rights to work, equality and nondiscrimination, privacy, free expression and free association. Despite the human rights harms of hiring algorithms, the AI ethics literature has predominantly focused on abstract ethical principles. This is problematic for two reasons. (...)
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  10. Patterned Inequality, Compounding Injustice, and Algorithmic Prediction.Benjamin Eidelson - 2021 - American Journal of Law and Equality 1 (1):252-276.
    If whatever counts as merit for some purpose is unevenly distributed, a decision procedure that accurately sorts people on that basis will “pick up” and reproduce the pre-existing pattern in ways that more random, less merit-tracking procedures would not. This dynamic is an important cause for concern about the use of predictive models to allocate goods and opportunities. In this article, I distinguish two different objections that give voice to that concern in different ways. First, decision procedures may contribute to (...)
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  11. Análisis jurídico de la discriminación algorítmica en los procesos de selección laboral.Andrés Páez & Natalia Ramírez-Bustamante - 2024 - In Natalia Angel & René Urueña (eds.), Innovación en derecho y nuevas tecnologías. Ediciones Uniandes.
    El uso de sistemas de machine learning en los procesos de selección laboral ha sido de gran utilidad para agilizarlos y volverlos más eficientes, pero al mismo tiempo ha generado problemas en términos de equidad, confiabilidad y transparencia. En este artículo comenzamos explicando los diferentes usos de la Inteligencia Artificial en los procesos de selección laboral en Estados Unidos. Presentamos los sesgos sexuales y raciales que han sido detectados en algunos de ellos y explicamos los obstáculos jurídicos y prácticos para (...)
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  12. Profiling vandalism in Wikipedia: A Schauerian approach to justification.Paul B. de Laat - 2016 - Ethics and Information Technology 18 (2):131-148.
    In order to fight massive vandalism the English- language Wikipedia has developed a system of surveillance which is carried out by humans and bots, supported by various tools. Central to the selection of edits for inspection is the process of using filters or profiles. Can this profiling be justified? On the basis of a careful reading of Frederick Schauer’s books about rules in general (1991) and profiling in particular (2003) I arrive at several conclusions. The effectiveness, efficiency, and risk-aversion of (...)
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  13. 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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  14.  84
    Multiplicative Metric Fairness Under Composition.Milan Mossé - 2023 - Symposium on Foundations of Responsible Computing 4.
    Dwork, Hardt, Pitassi, Reingold, & Zemel [6] introduced two notions of fairness, each of which is meant to formalize the notion of similar treatment for similarly qualified individuals. The first of these notions, which we call additive metric fairness, has received much attention in subsequent work studying the fairness of a system composed of classifiers which are fair when considered in isolation [3, 4, 7, 8, 12] and in work studying the relationship between fair treatment of individuals and fair treatment (...)
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  15. I, NEURON: the neuron as the collective.Lance Nizami - 2017 - Kybernetes 46:1508-1526.
    Purpose – In the last half-century, individual sensory neurons have been bestowed with characteristics of the whole human being, such as behavior and its oft-presumed precursor, consciousness. This anthropomorphization is pervasive in the literature. It is also absurd, given what we know about neurons, and it needs to be abolished. This study aims to first understand how it happened, and hence why it persists. Design/methodology/approach – The peer-reviewed sensory-neurophysiology literature extends to hundreds (perhaps thousands) of papers. Here, more than 90 (...)
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  16. The perception of correlation in scatterplots.Ronald A. Rensink & Gideon Baldridge - 2010 - Computer Graphics Forum 29:1203-1210.
    We present a rigorous way to evaluate the visual perception of correlation in scatterplots, based on classical psychophysical methods originally developed for simple properties such as brightness. Although scatterplots are graphically complex, the quantity they convey is relatively simple. As such, it may be possible to assess the perception of correlation in a similar way. Scatterplots were each of 5.0 extent, containing 100 points with a bivariate normal distribution. Means were 0.5 of the range of the points, and standard deviations (...)
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  17. Limiting Access to Certain Anonymous Information: From the Group Right to Privacy to the Principle of Protecting the Vulnerable.Haleh Asgarinia - 2024 - Journal of Value Inquiry 58 (1):1-27.
    An issue about the privacy of the clustered groups designed by algorithms arises when attempts are made to access certain pieces of information about those groups that would likely be used to harm them. Therefore, limitations must be imposed regarding accessing such information about clustered groups. In the discourse on group privacy, it is argued that the right to privacy of such groups should be recognised to respect group privacy, protecting clustered groups against discrimination. According to this viewpoint, this (...)
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  18. Contents, vehicles, and complex data analysis in neuroscience.Daniel C. Burnston - 2020 - Synthese 199 (1-2):1617-1639.
    The notion of representation in neuroscience has largely been predicated on localizing the components of computational processes that explain cognitive function. On this view, which I call “algorithmic homuncularism,” individual, spatially and temporally distinct parts of the brain serve as vehicles for distinct contents, and the causal relationships between them implement the transformations specified by an algorithm. This view has a widespread influence in philosophy and cognitive neuroscience, and has recently been ably articulated and defended by Shea. Still, I (...)
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  19. Digital Habitus or Personalization Without Personality.Alberto Romele & Dario Rodighiero - 2020 - Humana Mente 13 (37).
    Most of the existing studies on Bourdieu and the digital regards the social and class distinctions in the use of digital technologies, thus presupposing a certain transparency of technologies themselves. Our proposal is to refer to this attitude as “Bourdieu outside the digital.” Yet in this paper, another perspective called “Bourdieu inside the digital” is developed, which moves the focus on the effects of some emerging technologies on social distinctions and discrimination. The main hypothesis is that algorithms of machine (...)
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  20. Shadowboxing with Social Justice Warriors. A Review of Endre Begby’s Prejudice: A Study in Non-Ideal Epistemology.Alex Madva - 2022 - Philosophical Psychology.
    Endre Begby’s Prejudice: A Study in Non-Ideal Epistemology engages a wide range of issues of enduring interest to epistemologists, applied ethicists, and anyone concerned with how knowledge and justice intersect. Topics include stereotypes and generics, evidence and epistemic justification, epistemic injustice, ethical-epistemic dilemmas, moral encroachment, and the relations between blame and accountability. Begby applies his views about these topics to an equally wide range of pressing social questions, such as conspiracy theories, misinformation, algorithmic bias, discrimination, and criminal justice. (...)
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  21. Conformism, Ignorance & Injustice: AI as a Tool of Epistemic Oppression.Martin Miragoli - 2024 - Episteme: A Journal of Social Epistemology:1-19.
    From music recommendation to assessment of asylum applications, machine-learning algorithms play a fundamental role in our lives. Naturally, the rise of AI implementation strategies has brought to public attention the ethical risks involved. However, the dominant anti-discrimination discourse, too often preoccupied with identifying particular instances of harmful AIs, has yet to bring clearly into focus the more structural roots of AI-based injustice. This paper addresses the problem of AI-based injustice from a distinctively epistemic angle. More precisely, I argue that (...)
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  22. Book review: Coeckelbergh, Mark (2022): The political philosophy of AI. [REVIEW]Michael W. Schmidt - 2024 - TATuP - Zeitschrift Für Technikfolgenabschätzung in Theorie Und Praxis 33 (1):68–69.
    Mark Coeckelbergh starts his book with a very powerful picture based on a real incident: On the 9th of January 2020, Robert Williams was wrongfully arrested by Detroit police officers in front of his two young daughters, wife and neighbors. For 18 hours the police would not disclose the grounds for his arrest (American Civil Liberties Union 2020; Hill 2020). The decision to arrest him was primarily based on a facial detection algorithm which matched Mr. Williams’ driving license photo with (...)
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  23. Democratizing Algorithmic Fairness.Pak-Hang Wong - 2020 - Philosophy and Technology 33 (2):225-244.
    Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers (...)
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  24.  97
    (5 other versions)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 autonomy based theory. (...)
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  25. Algorithmic paranoia: the temporal governmentality of predictive policing.Bonnie Sheehey - 2019 - Ethics and Information Technology 21 (1):49-58.
    In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After first (...)
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  26. Algorithm and Parameters: Solving the Generality Problem for Reliabilism.Jack C. Lyons - 2019 - Philosophical Review 128 (4):463-509.
    The paper offers a solution to the generality problem for a reliabilist epistemology, by developing an “algorithm and parameters” scheme for type-individuating cognitive processes. Algorithms are detailed procedures for mapping inputs to outputs. Parameters are psychological variables that systematically affect processing. The relevant process type for a given token is given by the complete algorithmic characterization of the token, along with the values of all the causally relevant parameters. The typing that results is far removed from the typings of (...)
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  27. 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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  28. Algorithmic Profiling as a Source of Hermeneutical Injustice.Silvia Milano & Carina Prunkl - forthcoming - Philosophical Studies:1-19.
    It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only (...)
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  29. 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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  30. The ethics of algorithms: mapping the debate.Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter & Luciano Floridi - 2016 - Big Data and Society 3 (2):2053951716679679.
    In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences (...)
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  31. Discrimination and Equality of Opportunity.Carl Knight - 2017 - In Kasper Lippert-Rasmussen (ed.), The Routledge Handbook of the Ethics of Discrimination. New York: Routledge. pp. 140-150.
    Discrimination, understood as differential treatment of individuals on the basis of their respective group memberships, is widely considered to be morally wrong. This moral judgment is backed in many jurisdictions with the passage of equality of opportunity legislation, which aims to ensure that racial, ethnic, religious, sexual, sexual-orientation, disability and other groups are not subjected to discrimination. This chapter explores the conceptual underpinnings of discrimination and equality of opportunity using the tools of analytical moral and political philosophy.
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  32. Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept.Lukas J. Meier, Alice Hein, Klaus Diepold & Alena Buyx - 2022 - American Journal of Bioethics 22 (7):4-20.
    Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...)
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  33. Indirect Discrimination is Not Necessarily Unjust.Kasper Lippert-Rasmussen - 2014 - Journal of Practical Ethics 2 (2):33-57.
    This article argues that, as commonly understood, indirect discrimination is not necessarily unjust: 1) indirect discrimination involves the disadvantaging in relation to a particular benefit and such disadvantages are not unjust if the overall distribution of benefits and burdens is just; 2) indirect discrimination focuses on groups and group averages and ignores the distribution of harms and benefits within groups subjected to discrimination, but distributive justice is concerned with individuals; and 3) if indirect discrimination as (...)
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  34.  46
    Introduction: Algorithmic Thought.M. Beatrice Fazi - 2021 - Theory, Culture and Society 38 (7-8):5-11.
    This introduction to a special section on algorithmic thought provides a framework through which the articles in that collection can be contextualised and their individual contributions highlighted. Over the past decade, there has been a growing interest in artificial intelligence (AI). This special section reflects on this AI boom and its implications for studying what thinking is. Focusing on the algorithmic character of computing machines and the thinking that these machines might express, each of the special section’s essays (...)
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  35.  66
    Algorithmic Decision-Making, Agency Costs, and Institution-Based Trust.Keith Dowding & Brad R. Taylor - 2024 - Philosophy and Technology 37 (2):1-22.
    Algorithm Decision Making (ADM) systems designed to augment or automate human decision-making have the potential to produce better decisions while also freeing up human time and attention for other pursuits. For this potential to be realised, however, algorithmic decisions must be sufficiently aligned with human goals and interests. We take a Principal-Agent (P-A) approach to the questions of ADM alignment and trust. In a broad sense, ADM is beneficial if and only if human principals can trust algorithmic agents (...)
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  36. Algorithmic neutrality.Milo Phillips-Brown - manuscript
    Algorithms wield increasing control over our lives—over the jobs we get, the loans we're granted, the information we see online. Algorithms can and often do wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has been largely neglected. I investigate algorithmic neutrality, tackling three questions: What is algorithmic neutrality? Is it possible? And when we have it in mind, what can we learn about algorithmic (...)
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  37. The Ethics of Algorithmic Outsourcing in Everyday Life.John Danaher - 2019 - In Karen Yeung & Martin Lodge (eds.), Algorithmic Regulation. Oxford University Press.
    We live in a world in which ‘smart’ algorithmic tools are regularly used to structure and control our choice environments. They do so by affecting the options with which we are presented and the choices that we are encouraged or able to make. Many of us make use of these tools in our daily lives, using them to solve personal problems and fulfill goals and ambitions. What consequences does this have for individual autonomy and how should our legal and (...)
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  38. On statistical criteria of algorithmic fairness.Brian Hedden - 2021 - Philosophy and Public Affairs 49 (2):209-231.
    Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of (...)
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  39. 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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  40. 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 (...)
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  41. Discrimination and Self-Knowledge.Patrick Greenough - 2012 - In Declan Smithies & Daniel Stoljar (eds.), Introspection and Consciousness. , US: Oxford University Press.
    In this paper I show that a variety of Cartesian Conceptions of the mental are unworkable. In particular, I offer a much weaker conception of limited discrimination than the one advanced by Williamson (2000) and show that this weaker conception, together with some plausible background assumptions, is not only able to undermine the claim that our core mental states are luminous (roughly: if one is in such a state then one is in a position to know that one is) (...)
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  42. "Wrongful discrimination" - a tautological claim?Pascale Willemsen, Simone Sommer Degn, Jan Alejandro Garcia Olier & Kevin Reuter - forthcoming - Proceedings of the Annual Meeting of the Cognitive Science Society.
    Is it tautological to call an action "wrongful discrimination?" Some philosophers and political theorists answer this question in the affirmative and claim that the term "discrimination" is intrinsically evaluative. Others agree that "discrimination" usually conveys the action’s moral wrongness but claim that the term can be used in a purely descriptive way. In this paper, we present two corpus studies and two experiments designed to test whether the folk concept of discrimination is evaluative. We demonstrate that (...)
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  43. Ameliorating Algorithmic Bias, or Why Explainable AI Needs Feminist Philosophy.Linus Ta-Lun Huang, Hsiang-Yun Chen, Ying-Tung Lin, Tsung-Ren Huang & Tzu-Wei Hung - 2022 - Feminist Philosophy Quarterly 8 (3).
    Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice. However, such algorithmic decision systems can have prevalent biases against marginalized social groups and undermine social justice. Explainable artificial intelligence (XAI) is a recent development aiming to make an AI system’s decision processes less opaque and to expose its problematic biases. This paper argues against technical XAI, according to which the detection and interpretation of algorithmic bias can (...)
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  44. 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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  45. (1 other version)Algorithmic correspondence and completeness in modal logic. IV. Semantic extensions of SQEMA.Willem Conradie & Valentin Goranko - 2008 - Journal of Applied Non-Classical Logics 18 (2):175-211.
    In a previous work we introduced the algorithm \SQEMA\ for computing first-order equivalents and proving canonicity of modal formulae, and thus established a very general correspondence and canonical completeness result. \SQEMA\ is based on transformation rules, the most important of which employs a modal version of a result by Ackermann that enables elimination of an existentially quantified predicate variable in a formula, provided a certain negative polarity condition on that variable is satisfied. In this paper we develop several extensions of (...)
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  46. Algorithmic decision-making: the right to explanation and the significance of stakes.Lauritz Munch, Jens Christian Bjerring & Jakob Mainz - forthcoming - Big Data and Society.
    The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we will (...)
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  47.  85
    Migration and discrimination: exploring the pathways of a more integrated research agenda.Esma Baycan-Herzog, Annamari Vitikainen & Kasper Lippert-Rasmussen - 2024 - Ethics and Global Politics 17 (2):1-8.
    This special issue consists of four articles, contributed by David Owen; Désirée Lim, Sahar Akhtar and (as co-authors) Mollie Gerver, Miranda Simon, Patrick Lown and Dominik Duell. These contributions address issues related to migration policies with the aim of bringing normative theories of migration and discrimination into dialogue. These theories describe the various types of discrimination inherent in the domestic and global migration systems, as well as assess arguments, pro et contra, about whether these forms of discrimination (...)
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  48. Ideological diversity, hostility, and discrimination in philosophy.Uwe Peters, Nathan Honeycutt, Andreas De Block & Lee Jussim - 2020 - Philosophical Psychology 33 (4):511-548.
    Members of the field of philosophy have, just as other people, political convictions or, as psychologists call them, ideologies. How are different ideologies distributed and perceived in the field? Using the familiar distinction between the political left and right, we surveyed an international sample of 794 subjects in philosophy. We found that survey participants clearly leaned left (75%), while right-leaning individuals (14%) and moderates (11%) were underrepresented. Moreover, and strikingly, across the political spectrum, from very left-leaning individuals and moderates to (...)
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  49. Discrimination, emotion, and health inequities.Carina Fourie - 2018 - Les Ateliers de l'Éthique / the Ethics Forum 13 (3):123-149.
    In this paper I argue that certain ways in which the relationship among discrimination, emotions and health is presented can undermine equity. I identify a model of this relationship the discrimination-emotion-health model - and claim that while the model is important for understanding the detrimental impact that discrimination and oppression can have on emotions and health, certain implications of the model are troubling. I identify six critiques of the model, and show that equity could be undermined, for (...)
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  50. A modal theory of discrimination.Guido Melchior - 2021 - Synthese 198 (11):10661-10684.
    Discrimination is a central epistemic capacity but typically, theories of discrimination only use discrimination as a vehicle for analyzing knowledge. This paper aims at developing a self-contained theory of discrimination. Internalist theories of discrimination fail since there is no compelling correlation between discriminatory capacities and experiences. Moreover, statistical reliabilist theories are also flawed. Only a modal theory of discrimination is promising. Versions of sensitivity and adherence that take particular alternatives into account provide necessary and (...)
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