Eliminating unintended bias in personalized policies using bias-eliminating adapted trees (BEAT)

Eva Ascarza and Ayelet Israeli
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Eva Ascarza: a Marketing Unit, Harvard Business School, Harvard University, Boston, MA 02163
Ayelet Israeli: a Marketing Unit, Harvard Business School, Harvard University, Boston, MA 02163

Proceedings of the National Academy of Sciences, 2022, vol. 119, issue 11, e2115293119

Abstract: Decision makers now use algorithmic personalization for resource allocation decisions in many domains (e.g., medical treatments, hiring decisions, product recommendations, or dynamic pricing). An inherent risk of personalization is disproportionate targeting of individuals from certain protected groups. Existing solutions that firms use to avoid this bias often do not eliminate the bias and may even exacerbate it. We propose BEAT (bias-eliminating adapted trees) to ensure balanced allocation of resources across individuals—guaranteeing both group and individual fairness—while still leveraging the value of personalization. We validate our method using simulations as well as an online experiment with N = 3,146 participants. BEAT is easy to implement in practice, has desirable scalability properties, and is applicable to many personalization problems.

Keywords: algorithmic bias; personalization; targeting; fairness; discrimination (search for similar items in EconPapers)
Date: 2022
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