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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“[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]
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A Survey on Bias and Fairness in Machine Learning
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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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