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The Cost of Fairness in AI: Evidence from E-Commerce

Author

Listed:
  • Moritz Zahn

    (Goethe University Frankfurt
    ETH Zurich)

  • Stefan Feuerriegel

    (ETH Zurich)

  • Niklas Kuehl

    (Karlsruhe Institute of Technology)

Abstract

Contemporary information systems make widespread use of artificial intelligence (AI). While AI offers various benefits, it can also be subject to systematic errors, whereby people from certain groups (defined by gender, age, or other sensitive attributes) experience disparate outcomes. In many AI applications, disparate outcomes confront businesses and organizations with legal and reputational risks. To address these, technologies for so-called “AI fairness” have been developed, by which AI is adapted such that mathematical constraints for fairness are fulfilled. However, the financial costs of AI fairness are unclear. Therefore, the authors develop AI fairness for a real-world use case from e-commerce, where coupons are allocated according to clickstream sessions. In their setting, the authors find that AI fairness successfully manages to adhere to fairness requirements, while reducing the overall prediction performance only slightly. However, they find that AI fairness also results in an increase in financial cost. Thus, in this way the paper’s findings contribute to designing information systems on the basis of AI fairness.

Suggested Citation

  • Moritz Zahn & Stefan Feuerriegel & Niklas Kuehl, 2022. "The Cost of Fairness in AI: Evidence from E-Commerce," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(3), pages 335-348, June.
    • Handle: RePEc:spr:binfse:v:64:y:2022:i:3:d:10.1007_s12599-021-00716-w
    DOI: 10.1007/s12599-021-00716-w
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    References listed on IDEAS

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