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In this paper, we derive an algorithmic fairness metric from the fairnessnotion of equal opportunity for equally qualified candidates for recommendationalgorithms commonly used by two-sided marketplaces. We borrow from the economicliterature on discrimination to arrive at a test for detecting bias that issolely attributable to the algorithm, as opposed to other sources such associetal inequality or human bias on the part of platform users. We use theproposed method to measure and quantify algorithmic bias with respect to genderof two algorithms used by LinkedIn, a popular online platform used by jobseekers and employers. Moreover, we introduce a framework and the rationale fordistinguishing algorithmic bias from human bias, both of which can potentiallyexist on a two-sided platform where algorithms make recommendations to humanusers. Finally, we discuss the shortcomings of a few other common algorithmicfairness metrics and why they do not capture the fairness notion of equalopportunity for equally qualified candidates.

Choosing an algorithmic fairness metric for an online marketplace: Detecting and quantifying algorithmic bias on LinkedIn