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Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces

Author

Listed:
  • Athey, Susan

    (Stanford U)

  • Karlan, Dean

    (Northwestern U)

  • Palikot, Emil

    (Stanford U)

  • Yuan, Yuan

    (Carnegie Mellon U)

Abstract

Online platforms often face challenges being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a microlending marketplace, we find that choices made by borrowers creating online profiles impact both of these objectives. We further support this conclusion with a web-based randomized survey experiment. In the experiment, we create profile images using Generative Adversarial Networks that differ in a specific feature and estimate its impact on lender demand. We then counterfactually evaluate alternative platform policies and identify particular approaches to influencing the changeable profile photo features that can ameliorate the fairness-efficiency tension.

Suggested Citation

  • Athey, Susan & Karlan, Dean & Palikot, Emil & Yuan, Yuan, 2022. "Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces," Research Papers 4071, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:4071
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    Cited by:

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    3. Greenwald, Daniel L. & Howell, Sabrina T. & Li, Cangyuan & Yimfor, Emmanuel, 2024. "Regulatory arbitrage or random errors? Implications of race prediction algorithms in fair lending analysis," Journal of Financial Economics, Elsevier, vol. 157(C).

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    More about this item

    JEL classification:

    • D0 - Microeconomics - - General
    • D41 - Microeconomics - - Market Structure, Pricing, and Design - - - Perfect Competition
    • J0 - Labor and Demographic Economics - - General
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

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