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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Eva Ascarza & Ayelet Israeli, 2022. "Eliminating unintended bias in personalized policies using bias-eliminating adapted trees (BEAT)," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(11), pages 2115293119-, March.
    • Handle: RePEc:nas:journl:v:119:y:2022:p:e2115293119
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