The DOI matches its Crossref record; 23 of the 32 references it cites were found in a registry or an index.
An Intersectional Definition of Fairness
Abstract
We propose differential fairness, a multi-attribute definition of fairness in machine learning which is informed by intersectionality, a critical lens arising from the humanities literature, leveraging connections between differential privacy and legal notions of fairness. We show that our criterion behaves sensibly for any subset of the set of protected attributes, and we prove economic, privacy, and generalization guarantees. We provide a learning algorithm which respects our differential fairness criterion. Experiments on the COMPAS criminal recidivism dataset and census data demonstrate the utility of our methods.
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Cites
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“The AI community has responded by developing a broad array of mathematical formulations of fairness and learning algorithms which aim to satisfy them [3], [10], [13], [20], [24].”
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Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
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“A number of studies have subsequently demonstrated that bias and fairness issues in AI are both harmful and pervasive [1], [4], [5].”
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Cited by
Sorted by citations
Generative Adversarial Networks for face generation: A survey
Kammoun, A., Slama, R., Tabia, H., Ouni, T., & Abid, M. (2022) · ACM Computing Surveys · doi:10.1145/1122445.1122456 · Cited by 370
50 Years of Test (Un)fairness: Lessons for Machine Learning
Hutchinson, B., & Mitchell, M. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287600 · Cited by 297
“ality can be modeled. This might include, for example, how to quantify fairness along one dimension (e.g., age) conditioned on another dimension (e.g., skin tone), as recent work has begun to address [27, 39]. 6 CONCLUSIONS The spike in interest in test fairness in the 1960s arose during a time of social and political…”
BackgroundRethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approaches
Weinberg, L. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13196 · Cited by 127
Rigotti, C., & Fosch‐Villaronga, E. (2024) · Computer law & security review · doi:10.1016/j.clsr.2024.105966 · Cited by 211
Algorithmic inclusion: Shaping the predictive algorithms of artificial intelligence in hiring
Kelan, E. K. (2024) · Human Resource Management Journal · doi:10.1111/1748-8583.12511 · Cited by 106
Metadata
- Authors
- Foulds, J. R., Islam, R., Keya, K. N., & Pan, S.
- Affiliations
- University of Maryland, Baltimore County (US)
- Venue
- 2020 IEEE 36th International Conference on Data Engineering (ICDE)
- Publisher
- IEEE
- Published
- 2020-04
- Pages
- 1918-1921
- ISSN
- 1084-4627 (ISSN-L)
- OpenAlex
- W3032340379
- Cited by
- 233
- References
- 32
- Citations by year
- 2026: 35 · 2025: 24 · 2024: 33 · 2023: 85 · 2022: 26 · 2021: 23 · 2020: 5
- FWCI
- 11.12
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Data Engineering (ICDE) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- intersectionality, algorithmic fairness, differential privacy, protected characteristics, generalization guarantees, privacy guarantees, census data, learning algorithms
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