Results for 'algorithms'

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  1.  29
    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. (...)
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  2. 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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  3. What an Algorithm Is.Robin K. Hill - 2016 - Philosophy and Technology 29 (1):35-59.
    The algorithm, a building block of computer science, is defined from an intuitive and pragmatic point of view, through a methodological lens of philosophy rather than that of formal computation. The treatment extracts properties of abstraction, control, structure, finiteness, effective mechanism, and imperativity, and intentional aspects of goal and preconditions. The focus on the algorithm as a robust conceptual object obviates issues of correctness and minimality. Neither the articulation of an algorithm nor the dynamic process constitute the algorithm itself. Analysis (...)
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  4. Algorithms and the Individual in Criminal Law.Renée Jorgensen - forthcoming - Canadian Journal of Philosophy:1-17.
    Law-enforcement agencies are increasingly able to leverage crime statistics to make risk predictions for particular individuals, employing a form of inference some condemn as violating the right to be "treated as an individual". I suggest that the right encodes agents' entitlement to fair distribution of the burdens and benefits of the rule of law. Rather than precluding statistical prediction, it requires that citizens be able to anticipate which variables will be used as predictors, and act intentionally to avoid them. Furthermore, (...)
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  5.  83
    Algorithmic Bias: Senses, Sources, Solutions.Sina Fazelpour & David Danks - 2021 - Philosophy Compass 16 (8):e12760.
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  6.  52
    Algorithmic Political Bias in Artificial Intelligence Systems.Uwe Peters - 2022 - Philosophy and Technology 35 (2):1-23.
    Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can arise in (...)
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  7. Algorithms, Abstraction and Implementation.C. Foster - 1990 - Academic Press.
  8. 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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  9.  50
    A Learning Algorithm for Boltzmann Machines.David H. Ackley, Geoffrey E. Hinton & Terrence J. Sejnowski - 1985 - Cognitive Science 9 (1):147-169.
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  10. 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 ways (...)
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  11. The Ethics of Algorithms: Mapping the Debate.Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter & Luciano Floridi - 2016 - Big Data and Society 3 (2).
    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 (...)
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  12. The Algorithmic Leviathan: Arbitrariness, Fairness, and Opportunity in Algorithmic Decision Making Systems.Kathleen A. Creel & Deborah Hellman - forthcoming - Canadian Journal of Philosophy:1-18.
    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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  13. 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 (...)
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  14. 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 (...)
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  15.  17
    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 (...)
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  16. Algorithmic Decision-Making Based on Machine Learning From Big Data: Can Transparency Restore Accountability?Paul B. de Laat - 2018 - Philosophy and Technology 31 (4):525-541.
    Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated (...) usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)
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  17.  29
    Algorithmic Content Moderation: Technical and Political Challenges in the Automation of Platform Governance.Christian Katzenbach, Reuben Binns & Robert Gorwa - 2020 - Big Data and Society 7 (1):1–15.
    As government pressure on major technology companies builds, both firms and legislators are searching for technical solutions to difficult platform governance puzzles such as hate speech and misinformation. Automated hash-matching and predictive machine learning tools – what we define here as algorithmic moderation systems – are increasingly being deployed to conduct content moderation at scale by major platforms for user-generated content such as Facebook, YouTube and Twitter. This article provides an accessible technical primer on how algorithmic moderation works; examines some (...)
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  18. Algorithmic bias: on the implicit biases of social technology.Gabbrielle M. Johnson - 2020 - Synthese 198 (10):9941-9961.
    Often machine learning programs inherit social patterns reflected in their training data without any directed effort by programmers to include such biases. Computer scientists call this algorithmic bias. This paper explores the relationship between machine bias and human cognitive bias. In it, I argue similarities between algorithmic and cognitive biases indicate a disconcerting sense in which sources of bias emerge out of seemingly innocuous patterns of information processing. The emergent nature of this bias obscures the existence of the bias itself, (...)
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  19.  68
    Algorithmic Accountability and Public Reason.Reuben Binns - 2018 - Philosophy and Technology 31 (4):543-556.
    The ever-increasing application of algorithms to decision-making in a range of social contexts has prompted demands for algorithmic accountability. Accountable decision-makers must provide their decision-subjects with justifications for their automated system’s outputs, but what kinds of broader principles should we expect such justifications to appeal to? Drawing from political philosophy, I present an account of algorithmic accountability in terms of the democratic ideal of ‘public reason’. I argue that situating demands for algorithmic accountability within this justificatory framework enables us (...)
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  20. 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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  21. 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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  22.  17
    Algorithms as Culture: Some Tactics for the Ethnography of Algorithmic Systems.Nick Seaver - 2017 - Big Data and Society 4 (2).
    This article responds to recent debates in critical algorithm studies about the significance of the term “algorithm.” Where some have suggested that critical scholars should align their use of the term with its common definition in professional computer science, I argue that we should instead approach algorithms as “multiples”—unstable objects that are enacted through the varied practices that people use to engage with them, including the practices of “outsider” researchers. This approach builds on the work of Laura Devendorf, Elizabeth (...)
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  23. 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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  24. Are Algorithms Value-Free? Feminist Theoretical Virtues in Machine Learning.Gabbrielle Johnson - forthcoming - Journal Moral Philosophy.
    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.  30
    Algorithmic Decision-Making Based on Machine Learning From Big Data: Can Transparency Restore Accountability?Massimo Durante & Marcello D'Agostino - 2018 - Philosophy and Technology 31 (4):525-541.
    Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated (...) usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)
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  26. Detecting Racial Bias in Algorithms and Machine Learning.Nicol Turner Lee - 2018 - Journal of Information, Communication and Ethics in Society 16 (3):252-260.
    Purpose The online economy has not resolved the issue of racial bias in its applications. While algorithms are procedures that facilitate automated decision-making, or a sequence of unambiguous instructions, bias is a byproduct of these computations, bringing harm to historically disadvantaged populations. This paper argues that algorithmic biases explicitly and implicitly harm racial groups and lead to forms of discrimination. Relying upon sociological and technical research, the paper offers commentary on the need for more workplace diversity within high-tech industries (...)
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  27.  3
    Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence: Papers From the Ray Solomonoff 85th Memorial Conference, Melbourne, Vic, Australia, November 30 -- December 2, 2011.David L. Dowe (ed.) - 2013 - Springer.
    Algorithmic probability and friends: Proceedings of the Ray Solomonoff 85th memorial conference is a collection of original work and surveys. The Solomonoff 85th memorial conference was held at Monash University's Clayton campus in Melbourne, Australia as a tribute to pioneer, Ray Solomonoff, honouring his various pioneering works - most particularly, his revolutionary insight in the early 1960s that the universality of Universal Turing Machines could be used for universal Bayesian prediction and artificial intelligence. This work continues to increasingly influence and (...)
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  28. Recipes, Algorithms, and Programs.Carol E. Cleland - 2001 - Minds and Machines 11 (2):219-237.
    In the technical literature of computer science, the concept of an effective procedure is closely associated with the notion of an instruction that precisely specifies an action. Turing machine instructions are held up as providing paragons of instructions that "precisely describe" or "well define" the actions they prescribe. Numerical algorithms and computer programs are judged effective just insofar as they are thought to be translatable into Turing machine programs. Nontechnical procedures (e.g., recipes, methods) are summarily dismissed as ineffective on (...)
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  29.  12
    Algorithmic Decision-Making Based on Machine Learning From Big Data: Can Transparency Restore Accountability?Paul Laat - 2018 - Philosophy and Technology 31 (4):525-541.
    Decision-making assisted by algorithms developed by machine learning is increasingly determining our lives. Unfortunately, full opacity about the process is the norm. Would transparency contribute to restoring accountability for such systems as is often maintained? Several objections to full transparency are examined: the loss of privacy when datasets become public, the perverse effects of disclosure of the very algorithms themselves, the potential loss of companies’ competitive edge, and the limited gains in answerability to be expected since sophisticated (...) usually are inherently opaque. It is concluded that, at least presently, full transparency for oversight bodies alone is the only feasible option; extending it to the public at large is normally not advisable. Moreover, it is argued that algorithmic decisions preferably should become more understandable; to that effect, the models of machine learning to be employed should either be interpreted ex post or be interpretable by design ex ante. (shrink)
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  30.  62
    Algorithmic and Human Decision Making: For a Double Standard of Transparency.Mario Günther & Atoosa Kasirzadeh - 2022 - AI and Society 37 (1):375-381.
    Should decision-making algorithms be held to higher standards of transparency than human beings? The way we answer this question directly impacts what we demand from explainable algorithms, how we govern them via regulatory proposals, and how explainable algorithms may help resolve the social problems associated with decision making supported by artificial intelligence. Some argue that algorithms and humans should be held to the same standards of transparency and that a double standard of transparency is hardly justified. (...)
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  31. 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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  32. Moral Zombies: Why Algorithms Are Not Moral Agents.Carissa Véliz - forthcoming - AI and Society:1-11.
    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 that (...)
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  33.  7
    Algorithms, Governance, and Governmentality: On Governing Academic Writing.Lucas D. Introna - 2016 - Science, Technology, and Human Values 41 (1):17-49.
    Algorithms, or rather algorithmic actions, are seen as problematic because they are inscrutable, automatic, and subsumed in the flow of daily practices. Yet, they are also seen to be playing an important role in organizing opportunities, enacting certain categories, and doing what David Lyon calls “social sorting.” Thus, there is a general concern that this increasingly prevalent mode of ordering and organizing should be governed more explicitly. Some have argued for more transparency and openness, others have argued for more (...)
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  34. Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept.Lukas J. Meier, Alice Hein, Klaus Diepold & Alena Buyx - forthcoming - American Journal of Bioethics:1-17.
    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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  35.  44
    Algorithm Exploitation: Humans Are Keen to Exploit Benevolent AI.Jurgis Karpus, Adrian Krüger, Julia Tovar Verba, Bahador Bahrami & Ophelia Deroy - 2021 - iScience 24 (6):102679.
    We cooperate with other people despite the risk of being exploited or hurt. If future artificial intelligence (AI) systems are benevolent and cooperative toward us, what will we do in return? Here we show that our cooperative dispositions are weaker when we interact with AI. In nine experiments, humans interacted with either another human or an AI agent in four classic social dilemma economic games and a newly designed game of Reciprocity that we introduce here. Contrary to the hypothesis that (...)
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  36.  5
    Algorithms and Their Others: Algorithmic Culture in Context.Paul Dourish - 2016 - Big Data and Society 3 (2).
    Algorithms, once obscure objects of technical art, have lately been subject to considerable popular and scholarly scrutiny. What does it mean to adopt the algorithm as an object of analytic attention? What is in view, and out of view, when we focus on the algorithm? Using Niklaus Wirth's 1975 formulation that “algorithms + data structures = programs” as a launching-off point, this paper examines how an algorithmic lens shapes the way in which we might inquire into contemporary digital (...)
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  37. The Ethics of Algorithms: Key Problems and Solutions.Andreas Tsamados, Nikita Aggarwal, Josh Cowls, Jessica Morley, Huw Roberts, Mariarosaria Taddeo & Luciano Floridi - 2021 - AI and Society.
    Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis (...)
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  38.  5
    Algorithms and Complexity in Mathematics, Epistemology, and Science: Proceedings of 2015 and 2016 Acmes Conferences.Nicolas Fillion, Robert M. Corless & Ilias S. Kotsireas (eds.) - 2019 - Springer New York.
    ACMES is a multidisciplinary conference series that focuses on epistemological and mathematical issues relating to computation in modern science. This volume includes a selection of papers presented at the 2015 and 2016 conferences held at Western University that provide an interdisciplinary outlook on modern applied mathematics that draws from theory and practice, and situates it in proper context. These papers come from leading mathematicians, computational scientists, and philosophers of science, and cover a broad collection of mathematical and philosophical topics, including (...)
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  39.  12
    Algorithmic Governance: Developing a Research Agenda Through the Power of Collective Intelligence.Kalpana Shankar, Burkhard Schafer, Niall O'Brolchain, Maria Helen Murphy, John Morison, Su-Ming Khoo, Muki Haklay, Heike Felzmann, Aisling De Paor, Anthony Behan, Rónán Kennedy, Chris Noone, Michael J. Hogan & John Danaher - 2017 - Big Data and Society 4 (2).
    We are living in an algorithmic age where mathematics and computer science are coming together in powerful new ways to influence, shape and guide our behaviour and the governance of our societies. As these algorithmic governance structures proliferate, it is vital that we ensure their effectiveness and legitimacy. That is, we need to ensure that they are an effective means for achieving a legitimate policy goal that are also procedurally fair, open and unbiased. But how can we ensure that algorithmic (...)
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  40. 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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  41.  16
    The Oxford Handbook of Algorithmic Music.Alex McLean & Roger T. Dean (eds.) - 2018 - Oxford University Press.
    Featuring chapters by emerging and established scholars as well as by leading practitioners in the field, this Handbook both describes the state of algorithmic composition and also set the agenda for critical research on and analysis of algorithmic music.
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  42.  3
    Algorithms as Fetish: Faith and Possibility in Algorithmic Work.Jamie Sherman, Dawn Nafus & Suzanne L. Thomas - 2018 - Big Data and Society 5 (1).
    Algorithms are powerful because we invest in them the power to do things. With such promise, they can transform the ordinary, say snapshots along a robotic vacuum cleaner’s route, into something much more, such as a clean home. Echoing David Graeber’s revision of fetishism, we argue that this easy slip from technical capabilities to broader claims betrays not the “magic” of algorithms but rather the dynamics of their exchange. Fetishes are not indicators of false thinking, but social contracts (...)
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  43.  10
    Algorithms and the Individual in Criminal Law – Corrigendum.Renée Jorgensen - forthcoming - Canadian Journal of Philosophy:1-1.
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  44.  10
    Algorithmic Management in a Work Context.Will Sutherland, Eliscia Kinder, Christine T. Wolf, Min Kyung Lee, Gemma Newlands & Mohammad Hossein Jarrahi - 2021 - Big Data and Society 8 (2).
    The rapid development of machine-learning algorithms, which underpin contemporary artificial intelligence systems, has created new opportunities for the automation of work processes and management functions. While algorithmic management has been observed primarily within the platform-mediated gig economy, its transformative reach and consequences are also spreading to more standard work settings. Exploring algorithmic management as a sociotechnical concept, which reflects both technological infrastructures and organizational choices, we discuss how algorithmic management may influence existing power and social structures within organizations. We (...)
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  45.  29
    Mental Algorithms: Are Minds Computational Systems?James H. Fetzer - 1994 - Pragmatics and Cognition 21 (1):1-29.
    The idea that human thought requires the execution of mental algorithms provides a foundation for research programs in cognitive science, which are largely based upon the computational conception of language and mentality. Consideration is given to recent work by Penrose, Searle, and Cleland, who supply various grounds for disputing computationalism. These grounds in turn qualify as reasons for preferring a non-computational, semiotic approach, which can account for them as predictable manifestations of a more adquate conception. Thinking does not ordinarily (...)
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  46.  17
    Algorithmic Culture and the Colonization of Life-Worlds.Andrew Simon Gilbert - 2018 - Thesis Eleven 146 (1):87-96.
    This article explores some of the concerns which are being raised about algorithms with recourse to Habermas’s theory of communicative action. The intention is not to undertake an empirical examination of ‘algorithms’ or their consequences but to connect critical theory to some contemporary concerns regarding digital cultures. Habermas’s ‘colonization of life-worlds’ thesis gives theoretical expression to two different trends which underlie many current criticisms of the insidious influence of digital algorithms: the privatization of communication, and the particularization (...)
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  47.  17
    Mental Algorithms: Are Minds Computational Systems?James H. Fetzer - 1994 - Pragmatics and Cognition 2 (1):1-29.
    The idea that human thought requires the execution of mental algorithms provides a foundation for research programs in cognitive science, which are largely based upon the computational conception of language and mentality. Consideration is given to recent work by Penrose, Searle, and Cleland, who supply various grounds for disputing computationalism. These grounds in turn qualify as reasons for preferring a non-computational, semiotic approach, which can account for them as predictable manifestations of a more adquate conception. Thinking does not ordinarily (...)
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  48.  82
    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 (...)
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  49. Algorithms in Modern Mathematics and Computer Science: Proceedings, Urgench, Uzbek Ssr, September 16-22, 1979.A. P. Ershov & Donald Ervin Knuth (eds.) - 1981 - Springer Verlag.
  50.  3
    Algorithms in Practice: Comparing Web Journalism and Criminal Justice.Angèle Christin - 2017 - Big Data and Society 4 (2).
    Big Data evangelists often argue that algorithms make decision-making more informed and objective—a promise hotly contested by critics of these technologies. Yet, to date, most of the debate has focused on the instruments themselves, rather than on how they are used. This article addresses this lack by examining the actual practices surrounding algorithmic technologies. Specifically, drawing on multi-sited ethnographic data, I compare how algorithms are used and interpreted in two institutional contexts with markedly different characteristics: web journalism and (...)
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