The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification
Open accessAbstract
Machine learning bias and fairness have recently emerged as key issues due to the pervasive deployment of data-driven decision making in a variety of sectors and services. It has often been argued that unfair classifications can be attributed to bias in training data, but previous attempts to 'repair' training data have led to limited success. To circumvent shortcomings prevalent in data repairing approaches, such as those that weight training samples of the sensitive group (e.g. gender, race, financial status) based on their misclassification error, we present a process that iteratively adapts training sample weights with a theoretically grounded model. This model addresses different kinds of bias to better achieve fairness objectives, such as trade-offs between accuracy and disparate impact elimination or disparate mistreatment elimination. We show that, compared to previous fairness-aware approaches, our methodology achieves better or similar trades-offs between accuracy and unfairness mitigation on real-world and synthetic datasets.
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In the citing paper
“[181] also note that some classiiers may be unafected by the presence or speciic nuances of some biases, and others may be negatively afected by altering the training data in an attempt to mitigate them.”
From Fairness in machine learning: A survey (2024) · cites this paper as [181]
In the citing paper
“Weights can be introduced for multiple purposes: 1) to indicate a frequency count for an instance type (e.g.[52]), 2) to place lower/higher importance on łsensitivež training samples (e.g.[53,152,165]), or 3) to improve classiier stability (e.g.[181]).”
From Fairness in machine learning: A survey (2024) · cites this paper as [181]
In the citing paper
“Whereas, [181] irst build an unweighted classiier, learn the weights of samples, then retrain their classiier using these weights (in-processing).”
From Fairness in machine learning: A survey (2024) · cites this paper as [181]
In the citing paper
“However, as [117,181] note, classiier stability and robustness can be an issue.”
From Fairness in machine learning: A survey (2024) · cites this paper as [181]
Citations
Cites
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
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Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159
The Boosting Approach to Machine Learning: An Overview
Schapire, R. E. (2003) · Lecture notes in statistics · doi:10.1007/978-0-387-21579-2_9 · Cited by 2,101
Lipschitzian optimization without the Lipschitz constant
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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
Scaling Up the Accuracy of Naive-Bayes Classifiers : a Decision-Tree Hybrid
Kohavi, R. (1996) · Cited by 1,418
Data preprocessing techniques for classification without discrimination
Kamiran, F., & Calders, T. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-011-0463-8 · Cited by 1,305
Research works printed but not linked: 4
- Benjamin Fish, Jeremy Kun, and Adám D Lelkes. 2015. Fair boosting: a case study. In Workshop on Fairness, Accountability, and Transparency in Machine Learning.
- Kazuto Fukuchi and Jun Sakuma. 2015. Fairness-Aware Learning with Restriction of Universal Dependency using f-Divergences. arXiv preprint arXiv:1506.07721 (2015). · arXiv
- Anatoli Iouditski and Yuri Nesterov. 2014. Primal-dual subgradient methods for minimizing uniformly convex functions. arXiv preprint arXiv:1401.1792 (2014). · arXiv
- Michael Kearns, Seth Neel, Aaron Roth, and Zhiwei Steven Wu. 2017. Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness. arXiv preprint arXiv:1711.05144 (2017). · arXiv
Cited by
Sorted by citations
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812
Bias in data‐driven artificial intelligence systems—An introductory survey
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., et al. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1356 · Cited by 1,043
Self-citationA Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective
Roh, Y., Heo, G., & Whang, S. E. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2946162 · Cited by 1,083
Algorithmic fairness in artificial intelligence for medicine and healthcare
Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering · doi:10.1038/s41551-023-01056-8 · Cited by 750
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646
Metadata
- Authors
- Krasanakis, E., Spyromitros-Xioufis, E., Papadopoulos, S., & Kompatsiaris, Y.
- Affiliations
- Information Technologies Institute (GR)
- Centre of Research &Technology - Hellas (CERTH), Information Technologies Institute (ITI), Thessaloniki, Greece
- Venue
- Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
- Publisher
- ACM Press
- Published
- 2018
- Pages
- 853-862
- OpenAlex
- W2788241675
- Cited by
- 127
- References
- 39
- Citations by year
- 2026: 12 · 2025: 19 · 2024: 24 · 2023: 25 · 2022: 13 · 2021: 15 · 2020: 13 · 2019: 5 · 2018: 1
- FWCI
- 15.22
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- Funders
- European Commission (688363, 687786)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic fairness, bias mitigation, fairness-accuracy trade-off, sensitive groups, data repair
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