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Bias In, Bias Out? Evaluating the Folk Wisdom

Authors Ashesh Rambachan, Jonathan Roth

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Author Details

Ashesh Rambachan
  • Department of Economics, Harvard University, Cambridge, MA, USA
Jonathan Roth
  • Microsoft Research, Redmond, WA, USA

Funding

  • Rambachan, Ashesh: Supported by the NSF Graduate Research Fellowship under Grant DGE1745303.
  • Roth, Jonathan: Supported by the NSF Graduate Research Fellowship under Grant DGE1144152.

Acknowledgements

We are grateful to Isaiah Andrews, Talia Gillis, Ed Glaeser, Nir Hak, Nathan Hendren, Larry Katz, Jens Ludwig, Sendhil Mullainathan, Paul Novosad, Aaron Roth, Ben Roth, Hannah Shaffer, Jann Spiess, and seminar participants at Harvard, the Federal Reserve Bank of Philadelphia, and Quantco for valuable comments and feedback.

Cite AsGet BibTex

Ashesh Rambachan and Jonathan Roth. Bias In, Bias Out? Evaluating the Folk Wisdom. In 1st Symposium on Foundations of Responsible Computing (FORC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 156, pp. 6:1-6:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.FORC.2020.6

Abstract

We evaluate the folk wisdom that algorithmic decision rules trained on data produced by biased human decision-makers necessarily reflect this bias. We consider a setting where training labels are only generated if a biased decision-maker takes a particular action, and so "biased" training data arise due to discriminatory selection into the training data. In our baseline model, the more biased the decision-maker is against a group, the more the algorithmic decision rule favors that group. We refer to this phenomenon as bias reversal. We then clarify the conditions that give rise to bias reversal. Whether a prediction algorithm reverses or inherits bias depends critically on how the decision-maker affects the training data as well as the label used in training. We illustrate our main theoretical results in a simulation study applied to the New York City Stop, Question and Frisk dataset.

Subject Classification

ACM Subject Classification
  • Applied computing → Economics
  • Computing methodologies → Machine learning
Keywords
  • fairness
  • selective labels
  • discrimination
  • training data

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