Algorithmic bias: Senses, sources, solutions

Philosophy Compass 16 (8):e12760 (2021)
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Abstract

Data‐driven algorithms are widely used to make or assist decisions in sensitive domains, including healthcare, social services, education, hiring, and criminal justice. In various cases, such algorithms have preserved or even exacerbated biases against vulnerable communities, sparking a vibrant field of research focused on so‐called algorithmic biases. This research includes work on identification, diagnosis, and response to biases in algorithm‐based decision‐making. This paper aims to facilitate the application of philosophical analysis to these contested issues by providing an overview of three key topics: What is algorithmic bias? Why and how can it occur? What can and should be done about it? Throughout, we highlight connections—both actual and potential—with philosophical ideas and concerns.

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Author Profiles

Sina Fazelpour
Northeastern University
David Danks
University of California, San Diego

References found in this work

Oppressive Things.Shen-yi Liao & Bryce Huebner - 2020 - Philosophy and Phenomenological Research 103 (1):92-113.
The Imperative of Integration.Elizabeth Anderson - 2010 - Princeton University Press.
Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.
Do artifacts have politics?Langdon Winner - 1980 - Daedalus 109 (1):121--136.
On the epistemic costs of implicit bias.Tamar Szabó Gendler - 2011 - Philosophical Studies 156 (1):33-63.

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