Mitigating Bias in Set Selection with Noisy Protected Attributes

Anay Mehrotra · L. Elisa Celis

37 citations0/0 files storedACM Conference on Fairness, Accountability, and Transparency (FAccT)

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Ethics and Social Impacts of AI Privacy-Preserving Technologies in Data Discrimination and Equality Law

Abstract

Subset selection algorithms are ubiquitous in AI-driven applications, including, online recruiting portals and image search engines, so it is imperative that these tools are not discriminatory on the basis of protected attributes such as gender or race. Currently, fair subset selection algorithms assume that the protected attributes are known as part of the dataset. However, protected attributes may be noisy due to errors during data collection or if they are imputed (as is often the case in real-world settings). While a wide body of work addresses the effect of noise on the performance of machine learning algorithms, its effect on fairness remains largely unexamined. We find that in the presence of noisy protected attributes, in attempting to increase fairness without considering noise, one can, in fact, decrease the fairness of the result!

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References

2004Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination 2018Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification 2016Equality of Opportunity in Supervised Learning 2003Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination 2017Counterfactual Fairness 2010Three naive Bayes approaches for discrimination-free classification 2018Fairness of Exposure in Rankings 2005Constructed Criteria

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