Algorithms propagate gender bias in the marketplace—with consumers’ cooperation

Abstract

Recent research shows that algorithms learn societal biases from large text corpora. We examine the marketplace-relevant consequences of such bias for consumers. Based on billions of documents from online text corpora, we first demonstrate that from gender biases embedded in language, algorithms learn to associate women with more negative consumer psychographic attributes than men (e.g., associating women more closely with impulsive vs. planned investors). Second, in a series of field experiments, we show that such learning results in the delivery of gender-biased digital advertisements and product recommendations. Specifically, across multiple platforms, products, and attributes, we find that digital advertisements containing negative psychographic attributes (e.g., impulsive) are more likely to be delivered to women compared to men, and that search engine product recommendations are similarly biased, which influences consumer's consideration sets and choice. Finally, we empirically examine consumer's role in co-producing algorithmic gender bias in the marketplace and observe that consumers reinforce these biases by accepting gender stereotypes (i.e., clicking on biased ads). We conclude by discussing theoretical and practical implications.