Mitigating Bias in Set Selection with Noisy Protected Attributes

Understand

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.