Results for ' subgenre algorithms'

992 found
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  1. 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 bias?
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  2. 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 (...)
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  3. How the machine ‘thinks’: Understanding opacity in machine learning algorithms.Jenna Burrell - 2016 - Big Data and Society 3 (1):205395171562251.
    This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: opacity as intentional corporate (...)
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  4.  60
    Criminal Justice and Artificial Intelligence: How Should we Assess the Performance of Sentencing Algorithms?Jesper Ryberg - 2024 - Philosophy and Technology 37 (1):1-15.
    Artificial intelligence is increasingly permeating many types of high-stake societal decision-making such as the work at the criminal courts. Various types of algorithmic tools have already been introduced into sentencing. This article concerns the use of algorithms designed to deliver sentence recommendations. More precisely, it is considered how one should determine whether one type of sentencing algorithm (e.g., a model based on machine learning) would be ethically preferable to another type of sentencing algorithm (e.g., a model based on old-fashioned (...)
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  5. Describing Texts for Algorithms: How They Prescribe Operations and Integrate Cases. Reflections Based on Ancient Chinese Mathematical Sources.Karine Chemla - 2015 - In Karine Chemla & Jacques Virbel (eds.), Texts, Textual Acts and the History of Science. Springer International Publishing.
     
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  6.  34
    Minds beyond brains and algorithms.Jan M. Zytkow - 1990 - Behavioral and Brain Sciences 13 (4):691-692.
  7.  42
    Understanding user sensemaking in fairness and transparency in algorithms: algorithmic sensemaking in over-the-top platform.Donghee Shin, Joon Soo Lim, Norita Ahmad & Mohammed Ibahrine - forthcoming - AI and Society:1-14.
    A number of artificial intelligence systems have been proposed to assist users in identifying the issues of algorithmic fairness and transparency. These AI systems use diverse bias detection methods from various perspectives, including exploratory cues, interpretable tools, and revealing algorithms. This study explains the design of AI systems by probing how users make sense of fairness and transparency as they are hypothetical in nature, with no specific ways for evaluation. Focusing on individual perceptions of fairness and transparency, this study (...)
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  8. The Role of Imagination in Social Scientific Discovery: Why Machine Discoverers Will Need Imagination Algorithms.Michael Stuart - 2019 - In Mark Addis, Fernand Gobet & Peter Sozou (eds.), Scientific Discovery in the Social Sciences. Springer Verlag.
    When philosophers discuss the possibility of machines making scientific discoveries, they typically focus on discoveries in physics, biology, chemistry and mathematics. Observing the rapid increase of computer-use in science, however, it becomes natural to ask whether there are any scientific domains out of reach for machine discovery. For example, could machines also make discoveries in qualitative social science? Is there something about humans that makes us uniquely suited to studying humans? Is there something about machines that would bar them from (...)
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  9.  57
    Multipopulation Management in Evolutionary Algorithms and Application to Complex Warehouse Scheduling Problems.Yadong Yu, Haiping Ma, Mei Yu, Sengang Ye & Xiaolei Chen - 2018 - Complexity 2018:1-14.
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  10.  11
    SAT-based MaxSAT algorithms.Carlos Ansótegui, Maria Luisa Bonet & Jordi Levy - 2013 - Artificial Intelligence 196 (C):77-105.
  11. Abolish! Against the Use of Risk Assessment Algorithms at Sentencing in the US Criminal Justice System.Katia Schwerzmann - 2021 - Philosophy and Technology 1:1-22.
    In this article, I show why it is necessary to abolish the use of predictive algorithms in the US criminal justice system at sentencing. After presenting the functioning of these algorithms in their context of emergence, I offer three arguments to demonstrate why their abolition is imperative. First, I show that sentencing based on predictive algorithms induces a process of rewriting the temporality of the judged individual, flattening their life into a present inescapably doomed by its past. (...)
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  12.  5
    Towards fixed-parameter tractable algorithms for abstract argumentation.Wolfgang Dvořák, Reinhard Pichler & Stefan Woltran - 2012 - Artificial Intelligence 186 (C):1-37.
  13.  8
    Can AI-Based Decisions be Genuinely Public? On the Limits of Using AI-Algorithms in Public Institutions.Alon Harel & Gadi Perl - 2024 - Jus Cogens 6 (1):47-64.
    AI-based algorithms are used extensively by public institutions. Thus, for instance, AI algorithms have been used in making decisions concerning punishment providing welfare payments, making decisions concerning parole, and many other tasks which have traditionally been assigned to public officials and/or public entities. We develop a novel argument against the use of AI algorithms, in particular with respect to decisions made by public officials and public entities. We argue that decisions made by AI algorithms cannot count (...)
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  14.  21
    Abolish! Against the Use of Risk Assessment Algorithms at Sentencing in the US Criminal Justice System.Katia Schwerzmann - 2021 - Philosophy and Technology 34 (4):1883-1904.
    In this article, I show why it is necessary to abolish the use of predictive algorithms in the US criminal justice system at sentencing. After presenting the functioning of these algorithms in their context of emergence, I offer three arguments to demonstrate why their abolition is imperative. First, I show that sentencing based on predictive algorithms induces a process of rewriting the temporality of the judged individual, flattening their life into a present inescapably doomed by its past. (...)
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  15.  71
    People Prefer Moral Discretion to Algorithms: Algorithm Aversion Beyond Intransparency.Johanna Jauernig, Matthias Uhl & Gari Walkowitz - 2022 - Philosophy and Technology 35 (1):1-25.
    We explore aversion to the use of algorithms in moral decision-making. So far, this aversion has been explained mainly by the fear of opaque decisions that are potentially biased. Using incentivized experiments, we study which role the desire for human discretion in moral decision-making plays. This seems justified in light of evidence suggesting that people might not doubt the quality of algorithmic decisions, but still reject them. In our first study, we found that people prefer humans with decision-making discretion (...)
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  16.  44
    The Role of Questions, Circumstances, and Algorithms in Belief.Jens Kipper, Alexander W. Kocurek & Zeynep Soysal - 2022 - In Marco Degano, Tom Roberts, Giorgio Sbardolini & Marieke Schouwstra (eds.), Proceedings of the 23rd Amsterdam Colloquium. pp. 181-187.
    A recent approach to the problem of logical omniscience holds that belief is question-sensitive: what an agent believes depends on what question they try to answer (Pérez Carballo, 2016; Yalcin, 2018; Hoek, 2022). While the question-sensitive approach can avoid some logical omniscience problems, we argue that it suffers from nearby problems. First, these accounts all validate closure principles that are just as implausible as the ones it was designed to avoid. Second, question-sensitivity by itself isn’t suitable for explaining many kinds (...)
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  17.  35
    From Impact to Importance: The Current State of the Wisdom-of-Crowds Justification of Link-Based Ranking Algorithms.George Masterton & Erik J. Olsson - 2017 - Philosophy and Technology 31 (4):593-609.
    In a legendary technical report, the Google founders sketched a wisdom-of-crowds justification for PageRank arguing that the algorithm, by aggregating incoming links to webpages in a sophisticated way, tracks importance on the web. On this reading of the report, webpages that have a high impact as measured by PageRank are supposed to be important webpages in a sense of importance that is not reducible to mere impact or popularity. In this paper, we look at the state of the art regarding (...)
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  18.  19
    Digital, politics, and algorithms: Governing digital data through the lens of data protection.Rocco Bellanova - 2017 - European Journal of Social Theory 20 (3):329-347.
    Many actors mobilize the cognitive, legal and technical tool-box of data protection when they discuss and address controversial issues such as digital mass surveillance. Yet, critical approaches to the digital only barely explore the politics of data protection in relation to data-driven governance. Building on governmentality studies and Actor-Network-Theory, this article analyses the potential and limits of using data protection to critique the ‘digital age’. Using the conceptual tool of dispositifs, it sketches an analytics of data protection and the emergence (...)
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  19.  52
    Transparency as design publicity: explaining and justifying inscrutable algorithms.Michele Loi, Andrea Ferrario & Eleonora Viganò - 2020 - Ethics and Information Technology 23 (3):253-263.
    In this paper we argue that transparency of machine learning algorithms, just as explanation, can be defined at different levels of abstraction. We criticize recent attempts to identify the explanation of black box algorithms with making their decisions (post-hoc) interpretable, focusing our discussion on counterfactual explanations. These approaches to explanation simplify the real nature of the black boxes and risk misleading the public about the normative features of a model. We propose a new form of algorithmic transparency, that (...)
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  20.  13
    Classifier systems and genetic algorithms.L. B. Booker, D. E. Goldberg & J. H. Holland - 1989 - Artificial Intelligence 40 (1-3):235-282.
  21.  19
    Smart criminal justice: exploring the use of algorithms in the Swiss criminal justice system.Monika Simmler, Simone Brunner, Giulia Canova & Kuno Schedler - 2023 - Artificial Intelligence and Law 31 (2):213-237.
    In the digital age, the use of advanced technology is becoming a new paradigm in police work, criminal justice, and the penal system. Algorithms promise to predict delinquent behaviour, identify potentially dangerous persons, and support crime investigation. Algorithm-based applications are often deployed in this context, laying the groundwork for a ‘smart criminal justice’. In this qualitative study based on 32 interviews with criminal justice and police officials, we explore the reasons why and extent to which such a smart criminal (...)
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  22.  3
    Privacy preserving region optimal algorithms for symmetric and asymmetric DCOPs.Tal Grinshpoun, Tamir Tassa, Vadim Levit & Roie Zivan - 2019 - Artificial Intelligence 266 (C):27-50.
  23.  8
    Verifiable implementations of geometric algorithms using finite precision arithmetic.Victor J. Milenkovic - 1988 - Artificial Intelligence 37 (1-3):377-401.
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  24.  11
    Analysis and modification of graphic data compression algorithms.Bouza M. K. - 2020 - Artificial Intelligence Scientific Journal 25 (4):32-40.
    The article examines the algorithms for JPEG and JPEG-2000 compression of various graphic images. The main steps of the operation of both algorithms are given, their advantages and disadvantages are noted. The main differences between JPEG and JPEG-2000 are analyzed. It is noted that the JPEG-2000 algorithm allows re-moving visually unpleasant effects. This makes it possible to highlight important areas of the image and improve the quality of their compression. The features of each step of the algorithms (...)
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  25.  14
    Nonverbal knowledge as algorithms.Chris Mortensen - 1987 - Behavioral and Brain Sciences 10 (3):487-488.
  26.  7
    Experimental evaluation of preprocessing algorithms for constraint satisfaction problems.Rina Dechter & Itay Meiri - 1994 - Artificial Intelligence 68 (2):211-241.
  27.  29
    How to program autonomous vehicle (AV) crash algorithms: an Islamic ethical perspective.Ezieddin Elmahjub & Junaid Qadir - 2023 - Journal of Information, Communication and Ethics in Society 21 (4):452-467.
    Purpose Fully autonomous self-driving cars not only hold the potential for significant economic and environmental advantages but also introduce complex ethical dilemmas. One of the highly debated issues, known as the “trolley problems,” revolves around determining the appropriate actions for a self-driving car when faced with an unavoidable crash. Currently, the discourse on autonomous vehicle (AV) crash algorithms is primarily shaped by Western ethical traditions, resulting in a Eurocentric bias due to the dominant economic and political influence of the (...)
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  28. The Political Theory of Data: Institutions, Algorithms, & Formats in Racial Redlining.Colin Koopman - 2022 - Political Theory 50 (2):337-361.
    Despite widespread recognition of an emergent politics of data in our midst, we strikingly lack a political theory of data. We readily acknowledge the presence of data across our political lives, but we often do not know how to conceptualize the politics of all those data points—the forms of power they constitute and the kinds of political subjects they implicate. Recent work in numerous academic disciplines is evidence of the first steps toward a political theory of data. This article maps (...)
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  29.  15
    Consciousness, Free Energy and Cognitive Algorithms.Thomas Rabeyron & Alain Finkel - 2020 - Frontiers in Psychology 11:550803.
  30.  11
    Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms.Kristof Meding & Thilo Hagendorff - 2024 - Philosophy and Technology 37 (1):1-22.
    Fairness in machine learning (ML) is an ever-growing field of research due to the manifold potential for harm from algorithmic discrimination. To prevent such harm, a large body of literature develops new approaches to quantify fairness. Here, we investigate how one can divert the quantification of fairness by describing a practice we call “fairness hacking” for the purpose of shrouding unfairness in algorithms. This impacts end-users who rely on learning algorithms, as well as the broader community interested in (...)
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  31.  11
    Theory and algorithms for plan merging.David E. Foulser, Ming Li & Qiang Yang - 1992 - Artificial Intelligence 57 (2-3):143-181.
  32.  11
    Cloud ethics: Algorithms and the attributes of ourselves and others.Paul Lewis - 2022 - Contemporary Political Theory 21 (S3):118-121.
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  33.  9
    The Business of Algorithms.Wim Vandekerckhove - 2019 - Philosophy of Management 18 (2):203-210.
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  34. Special Session on Intelligent Algorithms for Game Theory-Estimating the Contingency of RD Proj.Changsheng Yi, Wansheng Tang & Ying Liu - 2006 - In O. Stock & M. Schaerf (eds.), Lecture Notes in Computer Science. Springer Verlag. pp. 4114--819.
     
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  35.  19
    Comparative Performance of Intelligent Algorithms for System Identification and Control.M. A. Hossain, A. A. M. Madkour, K. P. Dahal & H. Yu - 2008 - Journal of Intelligent Systems 17 (4):313-330.
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  36.  9
    Big Archive-Assisted Ensemble of Many-Objective Evolutionary Algorithms.Wen Zhong, Jian Xiong, Anping Lin, Lining Xing, Feilong Chen & Yingwu Chen - 2021 - Complexity 2021:1-17.
    Multiobjective evolutionary algorithms have witnessed prosperity in solving many-objective optimization problems over the past three decades. Unfortunately, no one single MOEA equipped with given parameter settings, mating-variation operator, and environmental selection mechanism is suitable for obtaining a set of solutions with excellent convergence and diversity for various types of MaOPs. The reality is that different MOEAs show great differences in handling certain types of MaOPs. Aiming at these characteristics, this paper proposes a flexible ensemble framework, namely, ASES, which is (...)
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  37.  12
    Law and algorithms in the public domain.Dag Wiese Schartum - 2016 - Etikk I Praksis - Nordic Journal of Applied Ethics 1 (1):15-26.
    This article explains and discusses the relationship between traditional legislative processes and the development of automated government decision-making systems. The juridical aspects of systems development should be regarded as invisible quasi-legislation. The author investigates and discusses possible ways of changing the legislative process with a view to increasing and improving political involvement in processes today often regarded as mere implementation, and thereby safeguard that important parts of the law of our computerised society is situated in the public domain.
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  38.  49
    An Algorithmic Impossible-Worlds Model of Belief and Knowledge.Zeynep Soysal - 2024 - Review of Symbolic Logic 17 (2):586-610.
    In this paper, I develop an algorithmic impossible-worlds model of belief and knowledge that provides a middle ground between models that entail that everyone is logically omniscient and those that are compatible with even the most egregious kinds of logical incompetence. In outline, the model entails that an agent believes (knows) φ just in case she can easily (and correctly) compute that φ is true and thus has the capacity to make her actions depend on whether φ. The model thereby (...)
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  39.  57
    Brain–Computer Interfaces: Lessons to Be Learned from the Ethics of Algorithms.Andreas Wolkenstein, Ralf J. Jox & Orsolya Friedrich - 2018 - Cambridge Quarterly of Healthcare Ethics 27 (4):635-646.
    :Brain–computer interfaces are driven essentially by algorithms; however, the ethical role of such algorithms has so far been neglected in the ethical assessment of BCIs. The goal of this article is therefore twofold: First, it aims to offer insights into whether the problems related to the ethics of BCIs can be better grasped with the help of already existing work on the ethics of algorithms. As a second goal, the article explores what kinds of solutions are available (...)
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  40. 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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  41. Semantical Mutation, Algorithms and Programs.Porto André - 2015 - Dissertatio (S1):44-76.
    This article offers an explanation of perhaps Wittgenstein’s strangest and least intuitive thesis – the semantical mutation thesis – according to which one can never answer a mathematical conjecture because the new proof alters the very meanings of the terms involved in the original question. Instead of basing our justification on the distinction between mere calculation and proofs of isolated propositions, characteristic of Wittgenstein’s intermediary period, we generalize it to include conjectures involving effective procedures as well.
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  42.  6
    Homogeneity Test of Many-to-One Risk Differences for Correlated Binary Data under Optimal Algorithms.Keyi Mou & Zhiming Li - 2021 - Complexity 2021:1-29.
    In clinical studies, it is important to investigate the effectiveness of different therapeutic designs, especially, multiple treatment groups to one control group. The paper mainly studies homogeneity test of many-to-one risk differences from correlated binary data under optimal algorithms. Under Donner’s model, several algorithms are compared in order to obtain global and constrained MLEs in terms of accuracy and efficiency. Further, likelihood ratio, score, and Wald-type statistics are proposed to test whether many-to-one risk differences are equal based on (...)
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  43.  11
    Structure-driven algorithms for truth maintenance.Rina Dechter & Avi Dechter - 1996 - Artificial Intelligence 82 (1-2):1-20.
  44.  31
    Mechanical Jurisprudence and Domain Distortion: How Predictive Algorithms Warp the Law.Dasha Pruss - 2021 - Philosophy of Science 88 (5):1101-1112.
    The value-ladenness of computer algorithms is typically framed around issues of epistemic risk. In this article, I examine a deeper sense of value-ladenness: algorithmic methods are not only themselves value-laden but also introduce value into how we reason about their domain of application. I call this domain distortion. In particular, using insights from jurisprudence, I show that the use of recidivism risk assessment algorithms presupposes legal formalism and blurs the distinction between liability assessment and sentencing, which distorts how (...)
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  45. Formal Specification with Alloy: Specification of Algorithms.Jan van Eijck - unknown
    Overview • Alloy peculiarity • Alloy utilities • Assignments and pre- and postconditions in Alloy • Alloy for automated logical reasoning • Alloy specifications of algorithms • On your to do list: – Look through the example code in these slides, – make sure you understand what is happening. Note: Alloy Peculiarity..
     
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  46.  51
    On the existence of fair matching algorithms.F. Masarani & S. S. Gokturk - 1989 - Theory and Decision 26 (3):305-322.
  47.  28
    Learning Linear Causal Structure Equation Models with Genetic Algorithms.Shane Harwood & Richard Scheines - unknown
    Shane Harwood and Richard Scheines. Learning Linear Causal Structure Equation Models with Genetic Algorithms.
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  48.  3
    Heterogeneous active agents, II: Algorithms and complexity.Thomas Eiter & V. S. Subrahmanian - 1999 - Artificial Intelligence 108 (1-2):257-307.
  49.  8
    Cloud ethics: Algorithms and the attributes of ourselves and others.Paul Lewis - 2020 - Contemporary Political Theory:1-4.
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  50.  7
    The distributed breakout algorithms.Katsutoshi Hirayama & Makoto Yokoo - 2005 - Artificial Intelligence 161 (1-2):89-115.
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