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Publications
  • 2021
  • Article
  • Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society

Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring

By: Tom Sühr, Sophie Hilgard and Himabindu Lakkaraju
  • Format:Electronic
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Abstract

Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases, leading to the proposal of fair ranking algorithms (e.g., Det-Greedy) which increase exposure of underrepresented candidates. However, there is little to no work that explores whether fair ranking algorithms actually improve real world outcomes (e.g., hiring decisions) for underrepresented groups. Furthermore, there is no clear understanding as to how other factors (e.g., job context, inherent biases of the employers) may impact the efficacy of fair ranking in practice.
In this work, we analyze various sources of gender biases in online hiring platforms, including the job context and inherent biases of employers and establish how these factors interact with ranking algorithms to affect hiring decisions. To the best of our knowledge, this work makes the first attempt at studying the interplay between the aforementioned factors in the context of online hiring. We carry out a largescale user study simulating online hiring scenarios with data from TaskRabbit, a popular online freelancing site. Our results demonstrate that while fair ranking algorithms generally improve the selection rates of underrepresented minorities, their effectiveness relies heavily on the job contexts and candidate profiles.

Citation

Sühr, Tom, Sophie Hilgard, and Himabindu Lakkaraju. "Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society 4th (2021).
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About The Author

Himabindu Lakkaraju

Technology and Operations Management
→More Publications

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    Evaluating Explainability for Graph Neural Networks

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    Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations

    By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
More from the Authors
  • When Algorithms Explain Themselves: AI Adoption and Accuracy of Experts' Decisions By: Himabindu Lakkaraju and Chiara Farronato
  • Evaluating Explainability for Graph Neural Networks By: Chirag Agarwal, Owen Queen, Himabindu Lakkaraju and Marinka Zitnik
  • Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations By: Tessa Han, Suraj Srinivas and Himabindu Lakkaraju
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