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] note that some classifiers may be unaffected by the presence or specific nuances of some biases, and others may be negatively affected by altering the training data in an attempt to mitigate them.
“[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]
Weights can be introduced to improve classifier stability.
“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]
[181] first build an unweighted classifier, learn the weights of samples, then retrain their classifier using these weights (in-processing).
“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]
Classifier stability and robustness can be an issue with reweighing.
“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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The Critical Catalog: Library Information Systems, Tricksterism, and Social Justice
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When Fair Classification Meets Noisy Protected Attributes
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Probing into the Fairness of Large Language Models: A Case Study of ChatGPT
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The Gap on Gap: Tackling the Problem of Differing Data Distributions in Bias-Measuring Datasets
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Feature Engineering for Various Data Types in Data Science
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Fair and Private Data Preprocessing through Microaggregation
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Boosting Fair Classifier Generalization through Adaptive Priority Reweighing
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FACS-GCN: Fairness-Aware Cost-Sensitive Boosting of Graph Convolutional Networks
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Adaptive Priority Reweighing for Generalizing Fairness Improvement
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Assessing and Enforcing Fairness in the AI Lifecycle
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- 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
- Social Sciences › Safety Research › 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
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.
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