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.

中文翻译：

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算法在消费者的合作下在市场上传播性别偏见

最近的研究表明，算法可以从大型文本语料库中学习社会偏见。我们研究了这种偏见对消费者造成的与市场相关的后果。基于在线文本语料库中的数十亿份文档，我们首先证明，根据语言中嵌入的性别偏见，算法学会将女性与比男性更消极的消费者心理属性联系起来（例如，将女性与冲动与计划更紧密地联系起来）投资者）。其次，在一系列现场实验中，我们表明这种学习会导致提供带有性别偏见的数字广告和产品推荐。具体来说，在多个平台、产品和属性中，我们发现，与男性相比，包含负面心理属性（例如冲动）的数字广告更有可能投放给女性，并且搜索引擎产品推荐同样存在偏见，这会影响消费者的偏好。考虑因素和选择。最后，我们实证检验了消费者在市场上共同产生算法性别偏见中的作用，并观察到消费者通过接受性别刻板印象（即点击有偏见的广告）来强化这些偏见。最后我们讨论了理论和实践意义。