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

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  • Susan Athey
  • Dean Karlan
  • Emil Palikot
  • Yuan Yuan

Abstract

Online platforms often face the challenge of being both fair (i.e., non-discriminatory) and efficient (i.e., maximizing revenue). Using computer vision algorithms and observational data from a micro-lending marketplace, we find that the choices that online borrowers make when creating online profiles impact both of these objectives. We further support this finding 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 the impact of the feature on lender demand. We then evaluate counterfactual platform policies based on the changeable profile features, and identify approaches that can ameliorate the fairness-efficiency tension.

Suggested Citation

  • Susan Athey & Dean Karlan & Emil Palikot & Yuan Yuan, 2022. "Smiles in Profiles: Improving Fairness and Efficiency Using Estimates of User Preferences in Online Marketplaces," NBER Working Papers 30633, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:30633
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    More about this item

    JEL classification:

    • D0 - Microeconomics - - General
    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • J0 - Labor and Demographic Economics - - General
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

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