How Algorithms Discriminate Based on Data They Lack: Challenges, Solutions, and Policy Implications

Abstract Organizations often employ data-driven models to inform decisions that can have a significant impact on people's lives (e.g., university admissions, hiring). In order to protect people's privacy and prevent discrimination, these decision-makers may choose to delete or avoid collecting social category data, like sex and race. In this article, we argue that such censoring can exacerbate discrimination by making biases more difficult to detect. We begin by detailing how computerized decisi

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ethics.ai (1 March 2018), “How Algorithms Discriminate Based on Data They Lack: Challenges, Solutions, and Policy Implications,” evidence record 8412, https://ethics.ai/record/8412 (originally published by OpenAlex).

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