Internet Policy Review 9 (4) (2020)

Recently, amid growing awareness that computer algorithms are not neutral tools but can cause harm by reproducing and amplifying bias, attempts to detect and prevent such biases have intensified. An approach that has received considerable attention in this regard is the Value Sensitive Design (VSD) methodology, which aims to contribute to both the critical analysis of (dis)values in existing technologies and the construction of novel technologies that account for specific desired values. This article provides a brief overview of the key features of the Value Sensitive Design approach, examines its contributions to understanding and addressing issues around bias in computer systems, outlines the current debates on algorithmic bias and fairness in machine learning, and discusses how such debates could profit from VSD-derived insights and recommendations. Relating these debates on values in design and algorithmic bias to research on cognitive biases, we conclude by stressing our collective duty to not only detect and counter biases in software systems, but to also address and remedy their societal origins.
Keywords Value sensitive design   Algorithmic bias  Human values  Fairness  Fairness in Machine Learning
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References found in this work BETA

Democratizing Algorithmic Fairness.Pak-Hang Wong - 2020 - Philosophy and Technology 33 (2):225-244.
Do Artifacts Have Politics?Langdon Winner - 1980 - Daedalus 109 (1):121--136.
What is Implicit Bias?Jules Holroyd, Robin Scaife & Tom Stafford - 2017 - Philosophy Compass 12 (10):e12437.
Human Values and the Design of Computer Technology.Batya Friedman (ed.) - 1997 - Center for the Study of Language and Inf.

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Disability, Fairness, and Algorithmic Bias in AI Recruitment.Nicholas Tilmes - 2022 - Ethics and Information Technology 24 (2).

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