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

By Anja Lambrecht, Catherine E. Tucker. 2019

504 citations recorded by the source corpus.

. We explore data from a ﬁ eld test of how an algorithm delivered ads promoting job opportunities in the science, technology, engineering and math ﬁ elds. 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.

Subfields: Economics and Econometrics, Marketing, Sociology and Political Science, AI Ethics, Fairness & Society

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Citations in the corpus

Algorithmic management in a work context (2021, background): “Lambrecht and Tucker (2019), for example, discovered fewer women than men were shown ads about STEM careers even though the ads were gender neutral by design.”
Six Human-Centered Artificial Intelligence Grand Challenges (2023, unknown): “In the technology sector, studies show that search engine targeted advertising shows high-paying jobs significantly more frequently to males than females (Lambrecht & Tucker, 2019).”
Individual Fairness in Pipelines (2020, background): “19 Otherwise, repeat the mechanism.”
Mitigating Gender Bias in Captioning Systems (2020, background): “Gender Bias in Dataset: Gender bias in dataset has been studied in a variety of domains[25, 24, 26, 27, 28, 29], especially in natural language processing [10, 30, 31, 32, 33, 34, 11].”
Conversational AI: Social and Ethical Considerations (2019, background): “…this, as bias is found in: detecting skin tones in pedestrians [52], predictive policing systems and justice [38], the display of STEM career ads [27], recidivism algorithms [2], politics of search engines [21], medical applications [13], automatic speech recognition [44], and in hiring…”
Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021, methodology): “This algorithmic discrimination is also observed in systems such as targeted advertisements where gender discrimination occurred in the display of STEM career ads [56,57].”
Artificial Intelligence and Marketing: Pitfalls and Opportunities (2020, background): “For instance, in online targeted advertising, Lambrecht and Tucker (2019) demonstrated that since women are more highly priced target consumers than men, real-time-bidding competition is fiercer.”
Identifying and Reducing Gender Bias in Word-Level Language Models (2019, background): “Models automating resume screening have also proved to have a heavy gender bias favoring male candidates (Lambrecht and Tucker, 2018).”
A strategic framework for artificial intelligence in marketing (2020, background): “Researchers have also shown that gender bias can occur without any conscious (or unconscious) attempt to produce a biased outcome — using only an unbiased algorithm (Lambrecht and Tucker 2019).”