Philosophy and Technology 33 (2):225-244 (2020)
Authors | |
Abstract |
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 have taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework.
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Keywords | Algorithmic Bias Machine Learning Fairness Democratization Accountability for Reasonableness |
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DOI | 10.1007/s13347-019-00355-w |
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References found in this work BETA
Between Facts and Norms: Contributions to a Discourse Theory of Law and Democracy.Jürgen Habermas (ed.) - 1996 - Polity.
The Ethics of Algorithms: Mapping the Debate.Brent Mittelstadt, Patrick Allo, Mariarosaria Taddeo, Sandra Wachter & Luciano Floridi - 2016 - Big Data and Society 3 (2).
Between Facts and Norms: Contributions to a Discourse Theory of Law and Democracy.Frank I. Michelman & Jurgen Habermas - 1996 - Journal of Philosophy 93 (6):307.
View all 32 references / Add more references
Citations of this work BETA
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.
The ethics of algorithms: key problems and solutions.Andreas Tsamados, Nikita Aggarwal, Josh Cowls, Jessica Morley, Huw Roberts, Mariarosaria Taddeo & Luciano Floridi - 2022 - AI and Society 37 (1):215-230.
Transparency as Design Publicity: Explaining and Justifying Inscrutable Algorithms.Michele Loi, Andrea Ferrario & Eleonora Viganò - 2020 - Ethics and Information Technology 23 (3):253-263.
Conservative AI and social inequality: conceptualizing alternatives to bias through social theory.Mike Zajko - forthcoming - AI and Society:1-10.
Steering Representations—Towards a Critical Understanding of Digital Twins.Paulan Korenhof, Vincent Blok & Sanneke Kloppenburg - 2021 - Philosophy and Technology 34 (4):1751-1773.
View all 14 citations / Add more citations
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