  In this paper, we derive an algorithmic fairness metric from the fairness
notion of equal opportunity for equally qualified candidates for recommendation
algorithms commonly used by two-sided marketplaces. We borrow from the economic
literature on discrimination to arrive at a test for detecting bias that is
solely attributable to the algorithm, as opposed to other sources such as
societal inequality or human bias on the part of platform users. We use the
proposed method to measure and quantify algorithmic bias with respect to gender
of two algorithms used by LinkedIn, a popular online platform used by job
seekers and employers. Moreover, we introduce a framework and the rationale for
distinguishing algorithmic bias from human bias, both of which can potentially
exist on a two-sided platform where algorithms make recommendations to human
users. Finally, we discuss the shortcomings of a few other common algorithmic
fairness metrics and why they do not capture the fairness notion of equal
opportunity for equally qualified candidates.
