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

Anja Lambrecht () and Catherine Tucker ()
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Anja Lambrecht: Marketing, London Business School, London NW1 4SA, United Kingdom
Catherine Tucker: Marketing, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142

Management Science, 2019, vol. 65, issue 7, 2966-2981

Abstract: 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.

Keywords: algorithmic bias; online advertising; algorithms; artificial intelligence (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (100)

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