Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads

Published Online:https://doi.org/10.1287/mnsc.2018.3093

We explore data from a field test of how an algorithm delivered ads promoting job opportunities in the science, technology, engineering and math fields. This ad was explicitly intended to be gender neutral in its delivery. Empirically, however, fewer women saw the ad than men. This happened because younger women are a prized demographic and are more expensive to show ads to. An algorithm that simply optimizes cost-effectiveness in ad delivery will deliver ads that were intended to be gender neutral in an apparently discriminatory way, because of crowding out. We show that this empirical regularity extends to other major digital platforms.

This paper was accepted by Joshua Gans, business strategy.

This article appears in INFORMS Analytics Collections Vol. 13: Diversity & Inclusion: Analytics for Social Impact.

Visit this collection for free access to more articles showcasing how to put diversity, equity, and inclusion at the center of decision sciences.

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