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Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads
Open accessAbstract
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.
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“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).”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Lambrecht and Tucker 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“However, this approach can be problematic in terms of fairness and privacy [13-15, 18, 19].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [19]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Even after eliminating all bias from the data, an algorithm's results may still vary across different data groups due to algorithmic bias [19, 82].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [19]
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“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.”
From Algorithmic management in a work context (2021) · cites this paper as Lambrecht and Tucker, 2019
In the citing paper
“Bias has also been reported in algorithmic advertisement, with opportunities for higherpaying jobs and jobs within the field of science and technology advertised to men more often than to women (Datta et al. 2015;Lambrecht and Tucker 2019).”
From The ethics of algorithms: key problems and solutions (2022) · cites this paper as Lambrecht and Tucker 2019
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“Increasing amounts of empirical evidence confirm this risk (Obermeyer et al. 2019;Datta et al. 2015;Lambrecht and Tucker 2019).”
From Artificial intelligence and work: a critical review of recent research from the social sciences (2024) · cites this paper as Lambrecht and Tucker 2019
In the citing paper
“An additional system that incorporated post-hoc explainability was designed by Lamy et al. [123] and provided local explanations of the preference model using rainbow boxes [56].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [56]
In the citing paper
“Some identities (and not others) are recognized as a pedestrian,44 or fit for an STEM career,69 or in need of medical care.1 Some are ignored and made invisible altogether.”
From Algorithmic injustice: A relational ethics approach (2021) · cites this paper as [69]
In the citing paper
“an advertisement model that underpromoted ads for Science, Technology, Engineering, and Mathematics (STEM) careers to young women (Lambrecht & Tucker, 2019).”
From Diminished diversity-of-thought in a standard large language model (2024) · cites this paper as Lambrecht & Tucker, 2019
In the citing paper
“Proposition 6e. The greater the algorithm bias , the lower the probability of building trust in AI algorithms over time.”
From Why do users trust algorithms? A review and conceptualization of initial trust and trust over time (2022) · cites this paper as Lambrecht & Tucker, 2019
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Arreola, R. B. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583270 · Cited by 1
Soft Ethics and the Governance of AI
Floridi, L. (2023) · Oxford University Press eBooks · doi:10.1093/oso/9780198883098.003.0006 · Cited by 1
Mitigating Bias in Algorithmic Decision-making: Evaluating Claims and Practices
Raghavan, M. (2023) · ACM eBooks · doi:10.1145/3603195.3603212 · Cited by 1
Fair Robust Active Learning by Joint Inconsistency
Wu, T.-H., Su, H.-T., Chen, S.-T., & Hsu, W. H. (2023) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw60793.2023.00390 · Cited by 1
Metadata
- Authors
- Lambrecht, A., & Tucker, C.
- Affiliations
- Boston College (US)
- Venue
- Management Science
- Published
- 2019-07
- Volume
- 65
- Issue
- 7
- Pages
- 2966-2981
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2550925836
- Cited by
- 854
- References
- 26
- Citations by year
- 2026: 108 · 2025: 128 · 2024: 139 · 2023: 163 · 2022: 109 · 2021: 120 · 2020: 73 · 2019: 12
- FWCI
- 137.66
- Type
- journal article
- Language
- en
- Open access
- Locations
- Management Science (publishedVersion)
- London Business School Research Online (London Business School) (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Names, Identity, and Discrimination Research
- Social Sciences › Gender Studies › Media, Gender, and Advertising
- Keywords
- algorithmic bias, gender discrimination, online advertising, STEM careers, crowding out, demographic targeting, digital platforms, women in STEM
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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