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

Author

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

Abstract

Online platforms often have conflicting goals: they face tradeoffs between increasing efficiency and reducing disparities, where the latter may relate to objectives such as the longer-term health of the marketplace or the organization’s mission. We examine how participants’ profile pictures shape this trade-off in the context of a peer-to-peer lending platform. We develop and apply an approach to estimate marketplace participants’ preferences for different profile features, distinguishing between (i) "type" (e.g., gender, age) and (ii) "style" (e.g., smiling in the photo). Relative to type, style features are easier to change, and platforms may be more willing to encourage such changes. Our approach starts by using causal inference methods together with computer vision algorithms applied to observational data to identify type and style features of profiles that appear to affect demand for transactions. We further decompose type-based disparities into a component driven by demand for certain types and a component that arises because different types have different distributions of style features; we find that style differences exacerbate type-based disparities. To improve internal validity, we then carry out two randomized survey experiments using generative models to create multiple versions of profile images that differ in one feature at a time. We then evaluate counterfactual platform policies based on the changeable profile features and identify approaches that can ameliorate the disparity-efficiency tension. We identify marketplace feedback effects, where encouraging certain style choices attracts participants who value these choices.

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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    Cited by:

    1. Teng Ye & Jingnan Zheng & Junhui Jin & Jingyi Qiu & Wei Ai & Qiaozhu Mei, 2024. "Using Artificial Intelligence to Unlock Crowdfunding Success for Small Businesses," Papers 2407.09480, arXiv.org.
    2. Chen, Yutong, 2024. "Does the gig economy discriminate against women? Evidence from physicians in China," Journal of Development Economics, Elsevier, vol. 169(C).
    3. Mohammad Mosaffa & Omid Rafieian & Hema Yoganarasimhan, 2025. "Visual Polarization Measurement Using Counterfactual Image Generation," Papers 2503.10738, arXiv.org.
    4. 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).
    5. Yan Asadchy & Andres Karjus & Ksenia Mukhina & Maximilian Schich, 2024. "Perceived gendered self-representation on Tinder using machine learning," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    6. Isamar Troncoso & Lan Luo, 2023. "Look the Part? The Role of Profile Pictures in Online Labor Markets," Marketing Science, INFORMS, vol. 42(6), pages 1080-1100, November.

    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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