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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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
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
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048
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
Jones, D. R., Perttunen, C. D., & Stuckman, B. E. (1993) · Journal of Optimization Theory and Applications · doi:10.1007/bf00941892 · Cited by 2,005
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
Flagged first, then by citations
1 of the 39 references couldn’t be matched to a known paper.
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
Items 101–128 of 128
Fairness-aware Outlier EnsemblePreprint
Liu, H., Ma, F., He, S., Chen, J., & Gao, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.09419 · Cited by 6
O Funcionamento Diferencial dos Itens na Indução de Classificadores Mais Justos
Minatel, D. (2024) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/t.55.2024.tde-06012025-175603 · Cited by 1
Cao, Y., Berend, D., Tolmach, P., Levy, M., Amit, G., Shabtai, A., Elovici, Y., & Liu, Y. (2020) · arXiv (Cornell University)
Prior Signal Editing for Graph Filter Posterior Fairness ConstraintsPreprint
Krasanakis, E., Papadopoulos, S., Kompatsiaris, I. Y., & Symeonidis, A. L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.12397
Self-citationToward Analyzing the Diversity of Extractive Summaries
Hudson, A. D. (2022) · doi:10.15760/etd.7950
Fic Graph - Quantifying Fairness with Sensitive AttributePreprint
Akintande, O. J., & Olubusoye, O. E. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4352107
Group Fairness in Outlier Detection Ensembles
Mishra, G., & Kumar, R. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-19-7892-0_39
Fair Machine Learning-An Analytical Study Based on CiteSpace
Luo, X., Cui, J., & Ma, S. (2023) · International Conference on Consumer Electronics and Computer Engineering (ICCECE) · doi:10.1109/iccece58074.2023.10135360
Fairness with adaptive weight in network attack detection
Wen, X., Wang, N., Sun, Y., Xu, F., Zhang, D., & Zhao, X. (2023) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads60453.2023.00190
Arthur Charpentier, A. (2024) · Springer Actuarial · doi:10.1007/978-3-031-49783-4_10
Pascual-Triana, J. D., Fernández, A., Nováis, P., & Herrera, F. (2025) · Statistics · doi:10.1080/02331888.2025.2476029
Maximum Entropy Logistic Regression for Demographic Parity in Supervised Classification
Vromman, F. V., Courtain, S., Leleux, P., de Schaetzen, C., Beghein, E., Kneip, A., & Saerens, M. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74650-5_11
Fairness in Optimization and ML: A Survey Part 2
Roberts-Licklider, K., & Trafali̇s, T. B. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-81010-7_17
Data-Driven Innovation for Trustworthy AIPreprint
Siddharth, L., & Luo, J. (2025) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/a3d6z_v2
Multi-View Echocardiographic Embedding for Accessible AI DevelopmentPreprint
Tohyama, T., Han, A., Yoon, D., Paik, K., Gow, B. J., Izath, N., Kpodonu, J., & Celi, L. A. (2025) · medRxiv · doi:10.1101/2025.08.15.25333725 · Cited by 1
Moorthy, A. K., Pochimireddy, J. T. R., Saradhi, V. V., & Prasad, B. (2025) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3770078
OoDBench+: Quantifying and Understanding Two Dimensions of Out-of-Distribution Generalization
Ye, N., Li, K., Wu, F., Zhou, J., Bai, H., Yu, R., Hong, L., Zhou, F., Li, Z., Zhu, J., et al. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3628027 · Cited by 2
Mitigating Algorithmic Bias Through Sampling: The Role of Group Size and Sample Selection
Shahrezaei, M. H., Loughran, R., & McDaid, K. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-12717-4_3
FairDIF: debiasing classifiers with item response theory and differential item functioning
Minatel, D., Parmezan, A. R. S., dos Santos, N. R., Curi, M. L. D. L. C., Prudêncio, R. B. C., & Lopes, A. D. A. (2026) · AI and Ethics · doi:10.1007/s43681-026-01055-w
WFE-YOLO: A LIGHTWEIGHT PIG BEHAVIOR DETECTION MODEL FOR LIVESTOCK FARMING APPLICATIONS
Lv, J., Guangjie, W. A. N. G., ZHANG, M., & Li, F. (2026) · INMATEH Agricultural Engineering · doi:10.35633/inmateh-78-99
MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness
Monnier, J., George, T., Tarnec, C., Guyard, F., & Kountouris, M. (2026) · IEEE Conference on Artificial Intelligence (CAI) · doi:10.1109/cai68641.2026.11536423
Beyond Base Metric: The Critical and Overlooked Role of Meta-Metrics in Intersectional Fairness
Monnier, J., George, T., Guyard, F., Tarnec, C., & Kountouris, M. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812417
Weighted Sampling with Frequency-Aware Spatial Attention for Imbalanced Image Classification
Zhang, S., & Li, P. (2026) · Journal of Imaging · doi:10.3390/jimaging12070293
Societal Challenges for Linkage
Vatsalan, D., Asghar, H., & Kâafar, M. A. (2026) · Advances in information security · doi:10.1007/978-3-032-21932-9_10
Firth, Z., Mirheidari, B., Hussain, T., Fisher, H. L., Arseneault, L., Caspi, A., Moffitt, T. E., Wickersham, A., Downs, J. M., & Cummins, N. (2026) · doi:10.2196/preprints.106740
A Hybridized Framework for Bias Mitigation Through Adversarial De-Biasing and Threshold Calibration
Kundu, P., Panda, S., Mukherjee, A., Tripathy, H. K., & Hussein, M. H. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-26898-3_15
CD-Fair: Causal Disentanglement Representation Learning for Fair Dermatological Diagnosis
Zhao, P., & Ding, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38407-2_35
Rabiee, M., Rahmani, F., & Boozary, P. (2026) · Environmental Management Economics and Policy · doi:10.1016/j.jemecp.2026.100027
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
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
