Non-Discriminatory Machine Learning through Convex Fairness Criteria
Abstract
We introduce a novel technique to achieve non-discrimination in machine learning without sacrificing convexity and probabilistic interpretation. We also propose a new notion of fairness for machine learning called the weighted proportional fairness and show that our technique satisfies this subjective fairness criterion.
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When extending the classifier's (convex) loss function with fairness terms, researchers typically seek to balance fairness and accuracy.
“When extending the classiier's (convex) loss function with fairness terms, researchers typically seek to balance fairness and accuracy (e.g.[5,26,36,52,57,98,119,141,153,168,208,314]).”
From Fairness in machine learning: A survey (2024) · cites this paper as [119]
Regularization approaches are often non-convex in nature or achieve convexity at the cost of probabilistic interpretation.
“Key challenges for regularization approaches are: 1) they are often non-convex in nature or achieve convexity at the cost of probabilistic interpretation [119];”
From Fairness in machine learning: A survey (2024) · cites this paper as [119]
Cites
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501 · see citing works
Charging and rate control for elastic traffic
Kelly, F. P. (1997) · European Transactions on Telecommunications · doi:10.1002/ett.4460080106 · Cited by 2,979 · see citing works
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works
Certifying and Removing Disparate Impact
Feldman, M., Friedler, S. A., Moeller, J., Scheidegger, C., & Venkatasubramanian, S. (2015) · Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2783258.2783311 · Cited by 1,827 · see citing works
Inherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.itcs.2017.43 · Cited by 943 · see citing works
Tufekci, Z. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14517 · Cited by 780 · see citing works
Three naive Bayes approaches for discrimination-free classification.
Calders, T., & Verwer, S. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0190-x · Cited by 764 · see citing works
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · cited 1× · see citing works
“There has also been a significant amount of work in computational social science towards achieving fairness in data collection, social networks etc (Olteanu et al. 2016),(Tufekci 2014),(Morstatter and Liu 2017),(Hajian, Bonchi, and Castillo 2016).”
BackgroundAutomated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discrimination
Datta, A., Tschantz, M. C., & Datta, A. (2015) · Proceedings on Privacy Enhancing Technologies · doi:10.1515/popets-2015-0007 · Cited by 676 · see citing works
Zafar, M. B., Valera, I., Gomez Rodriguez, M., & Gummadi, K. P. (2017) · Proceedings of the 26th International Conference on World Wide Web · doi:10.1145/3038912.3052660 · Cited by 632 · see citing works
Classifying without discriminating
Kamiran, F., & Calders, T. (2009) · 2009 2nd International Conference on Computer, Control and Communication · doi:10.1109/ic4.2009.4909197 · Cited by 409 · see citing works
Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining
Hajian, S., Bonchi, F., & Castillo, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2945386 · Cited by 334 · see citing works
Fairness in Learning: Classic and Contextual Bandits
Joseph, M., Kearns, M., Morgenstern, J., & Roth, A. (2016) · arXiv · doi:10.48550/arxiv.1605.07139 · Cited by 199 · cited 1× · see citing works
“Very recently, there has been some work towards defining the notions of envy-freeness (Zafar et al. 2017a), Rawlsian and meritocratic fairness (Joseph et al. 2016a),(Joseph et al. 2016b), (Jabbari et al. 2017) in machine learning.”
BackgroundBiddle, D. (2005) · Cited by 101 · see citing works
Can ‘predictive policing' prevent crime before it happens?
(2016) · AAAS Articles DO Group · doi:10.1126/science.aah7353 · Cited by 38 · see citing works
Flagged first, then by citations
3 of the 18 references couldn’t be matched to a known paper.
Cited by
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A Survey on Bias and Fairness in Machine Learning
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Dong, Y., Ma, J., Wang, S., Chen, C., & Li, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2023.3265598 · Cited by 145 · see citing works
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“The main challenge with these approaches is that they usually result in a non-convex optimization function [11].”
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- Authors
- Goel, N., Yaghini, M., & Faltings, B.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society
- Publisher
- ACM
- Published
- 2018-12-27
- Pages
- 116-116
- ISSN
- 3065-8365 (ISSN-L)
- OpenAlex
- W2788284633
- Semantic Scholar
- 7a2b6c19f86fa72742beca534ea962edfffd689b
- Cited by
- 79
- References
- 18
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 6 · 2023: 12 · 2022: 13 · 2021: 16 · 2020: 17 · 2019: 8 · 2018: 1
- FWCI
- 18.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings of the AAAI/ACM Conference on AI Ethics and Society (publishedVersion)
- Topics
- Social Sciences › Safety Research › 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
- algorithmic fairness, non-discrimination, convex optimization, probabilistic interpretation, fairness criteria
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