The DOI matches its Crossref record; 10 of the 11 references it cites were found in a registry or an index.
Building Classifiers with Independency Constraints
Abstract
In this paper we study the problem of classifier learning where the input data contains unjustified dependencies between some data attributes and the class label. Such cases arise for example when the training data is collected from different sources with different labeling criteria or when the data is generated by a biased decision process. When a classifier is trained directly on such data, these undesirable dependencies will carry over to the classifier's predictions. In order to tackle this problem, we study the classification with independency constraints problem: find an accurate model for which the predictions are independent from a given binary attribute. We propose two solutions for this problem and present an empirical validation.
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“Prior work on detecting discrimination has focused on measuring correlation or mutual information between inputs and outputs [79], discrepancies in the fractions of inputs that produce a given output [18, 19, 39-42, 87-89, 91], or discrepancies in output probability distributions [51].”
From Fairness testing: testing software for discrimination (2017) · cites this paper as [18]
In the citing paper
“Reducing discrimination (CV score) in classifiers [18, 40, 88, 91], as our evaluation has shown, often fails to remove causal discrimination and discrimination against certain groups.”
From Fairness testing: testing software for discrimination (2017) · cites this paper as [18]
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Discrimination-aware data mining
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Classifying without discriminating
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Self-citationMeasuring Discrimination in Socially-Sensitive Decision Records
Pedreschi, D., Ruggieri, S., & Turini, F. (2009) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972795.50 · Cited by 175
Nearest Neighbour Classification with Monotonicity Constraints
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Statistical Model for Rough Set Approach to Multicriteria Classification
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Items 201–300 of 456
What Is Fair? Defining Fairness in Machine Learning for Health
Gao, J., Chou, B., McCaw, Z. R., Thurston, H., Varghese, P., Hong, C., & Gronsbell, J. L. (2025) · Statistics in Medicine · doi:10.1002/sim.70234 · Cited by 17
“Pre-processing techniques include resampling [167] or reweighting samples [168] to correct imbalances among groups.”
BENN: Bias Estimation Using a Deep Neural Network
Giloni, A., Grolman, E., Hagemann, T., Fromm, R., Fischer, S., Elovici, Y., & Shabtai, A. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3172365 · Cited by 13
Enforcing fairness using ensemble of diverse Pareto-optimal models
Guardieiro, V., Raimundo, M. M., & Poco, J. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-023-00922-y · Cited by 13
“Reweighting (Calders et al. 2009) (Reweight): It modifies the logistic regression to consider different weights for individuals.”
BackgroundMinimax AUC Fairness: Efficient Algorithm with Provable Convergence
Yang, Z., Ko, Y. L., Varshney, K. R., & Ying, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i10.26405 · Cited by 11
Path-Specific Counterfactual Fairness for Recommender Systems
Zhu, Y., Ma, J., Wu, L., Guo, Q. S., Hong, L., & Li, J. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599462 · Cited by 11
Introduction to The Special Section on Bias and Fairness in AI
Calders, T., Ntoutsi, E., Pechenizkiy, M., Rosenhahn, B., & Ruggieri, S. (2021) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3468507.3468509 · Cited by 11
“Research on fairness in machine learning and data mining took o↵ in 2008-2010 with some of the first works on discrimination discovery in databases [1] and learning classification models with (non-discrimination) independency constraints [2; 3].”
BackgroundSelf-citationSeamless equal accuracy ratio for inclusive CTC speech recognition
Gao, H., Wang, X., Kang, S., Mina, R. J. L., Issa, D., Harvill, J. B., Sarı, L., Hasegawa‐Johnson, M., & Yoo, C. D. (2021) · Speech Communication · doi:10.1016/j.specom.2021.11.004 · Cited by 11
Synthesizing Fair Decision Trees via Iterative Constraint Solving
Wang, J., Li, Y., & Wang, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-13188-2_18 · Cited by 11
“Broadly speaking, there are three approaches: pre-processing [17,25,31], in-processing [11,19,24,30,33] and postprocessing [18,22], depending on whether the focus is on de-biasing the training data, the learning algorithm, or the classification output.”
BackgroundMethodInfluentialCausally motivated Shortcut Removal Using Auxiliary LabelsPreprint
Makar, M., Packer, B., Moldovan, D., Blalock, D. W., Halpern, Y., & D’Amour, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.06422 · Cited by 11
“Thus our work contributes to ongoing investigations about whether there is a trade-off between fari-ness and accuracy [Zhang et al., 2019, Calders et al., 2009, Johansson et al., 2019, Dutta et al., 2020, Zhao and Gordon, 2019].”
BackgroundAchieving Model Fairness in Vertical Federated LearningPreprint
Liu, C., Fan, Z., Zhou, Z., Shi, Y., Pei, J., Chu, L., & Zhang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08344 · Cited by 11
“First, the statistical (or demographic) parity refers to the property that the demographics of those receiving a certain outcome should be identical to the demographics of the population overall [9].”
BackgroundOnline Fairness Auditing through Iterative Refinement
Maneriker, P., Burley, C., & Parthasarathy, S. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599454 · Cited by 21
“AVOIR supports implementing an extensive range of group fairness criteria, including demographic parity [6], equal opportunity [22], disparate mistreatment [46], and combinations of these criteria.”
BackgroundCausal Intervention for Fairness in Multibehavior Recommendation
Wang, X., Wang, W., Feng, F., Rong, W., Yin, C., & Xiong, Z. (2024) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3379903 · Cited by 13
“Besides, group fairness indicates that decisions should be balanced for different groups distinguished by sensitive attributes like gender or race [6, 10, 58].”
BackgroundFair Models for Impartial Policies: Controlling Algorithmic Bias in Transport Behavioural Modelling
Vega-Gonzalo, M., & Christidis, P. (2022) · Sustainability · doi:10.3390/su14148416 · Cited by 11
When More Data Lead Us Astray: Active Data Acquisition in the Presence of Label Bias
Li, Y., De‐Arteaga, M., & Saar‐Tsechansky, M. (2022) · Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · doi:10.1609/hcomp.v10i1.21994 · Cited by 12
A minimax framework for quantifying risk-fairness trade-off in regression
Chzhen, E., & Schreuder, N. (2022) · The Annals of Statistics · doi:10.1214/22-aos2198 · Cited by 14
“A popular formalization of such notion is through the Demographic Parity constraint, initially introduced in the context of binary classification (Calders et al., 2009).”
MethodFair Machine Learning Under Partial Compliance
Dai, J. C., Fazelpour, S., & Lipton, Z. C. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462521 · Cited by 11
“The employers may either be fairness-conscious (compliant)— taking into account considerations of demographic parity [13, 24],”
BackgroundA Joint Fairness Model with Applications to Risk Predictions for Underrepresented Populations
Do, H., Nandi, S., Putzel, P., Smyth, P., & Zhong, J. (2022) · Biometrics · doi:10.1111/biom.13632 · Cited by 10
“demographic parity (Calders et al., 2009), equalized odds or equal opportunity (Hardt et al.”
BackgroundCounterfactual Fairness Is Basically Demographic Parity
Rosenblatt, L., & Witter, R. T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26691 · Cited by 17
Quantifying the Impact of User Attention on Fair Group Representation in Ranked ListsPreprint
Sapieżyński, P., Zeng, W., Robertson, R. E., Mislove, A. E., & Wilson, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.10437 · Cited by 10
“There is a large and growing literature on how to achieve these objectives in machine learning-based classifiers [5–7, 14, 17, 22, 28–33, 39, 55].”
BackgroundAdvancing Subgroup Fairness via Sleeping ExpertsPreprint
Blum, A. L., & Lykouris, T. (2019) · arXiv (Cornell University) · doi:10.4230/lipics.itcs.2020.55 · Cited by 22
Removing biased data to improve fairness and accuracyPreprint
Verma, S., Ernst, M. D., & Just, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.03054 · Cited by 27
A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making
Schoeffer, J. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3503811 · Cited by 9
“Regarding group fairness metrics, popular notions include demographic parity [6] (“predictions should be independent of sensitive information”) and equalized odds/equal opportunity [21] (“predictive error rates should be equal for all demographic groups”).”
BackgroundEnhancing Fairness in Credit Assessment: Mitigation Strategies and Implementation
Kisten, M., & Khosa, M. (2024) · IEEE Access · doi:10.1109/access.2024.3505836 · Cited by 9
“The algorithm adjusts the weights of the instances in the dataset to mitigate bias in the training data [17].”
Data augmentation via diffusion model to enhance AI fairness
Blow, C. H., Qian, L., Gibson, C. A., Obiomon, P., & Dong, X. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1530397 · Cited by 14
“One such approach is by based on the frequency counts associated with the sensitive attribute [13].”
Towards algorithms and models that we can trust: A theoretical perspective
Oneto, L., Ridella, S., & Anguita, D. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127798 · Cited by 12
Mazilu, L., Paton, N. W., Κωνσταντίνου, Ν., & Fernandes, A. A. A. (2020) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri49571.2020.00056 · Cited by 9
“One of them is reweighing, which adds extra-information to the dataset by assigning weights to the tuples [3], [13].”
BackgroundA study of bias mitigation strategies for speaker recognition
Peri, R., Somandepalli, K., & Narayanan, S. S. (2022) · Computer Speech & Language · doi:10.1016/j.csl.2022.101481 · Cited by 17
“Such trade-offs are studied extensively in the broader literature on Fair-ML (Zhao and Gordon, 2019; Calders et al., 2009; Haas, 2020; Du et al., 2020).”
BackgroundInfluentialA Sequentially Fair Mechanism for Multiple Sensitive Attributes
Hu, F., Ratz, P., & Arthur Charpentier, A. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i11.29143 · Cited by 11
“We center our focus on group fairness, particularly on the concept of Demographic Parity fairness (Calders, Kamiran, and Pechenizkiy 2009), with the objective of achieving independence between attributes and predictions while bypassing the use of labels.”
BackgroundCovariance-Robust Dynamic Watermarking
Olfat, M., Sloan, S., Hespanhol, P., Porter, M., Vasudevan, R., & Aswani, A. J. (2020) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc42340.2020.9304039 · Cited by 9
List-wise Fairness Criterion for Point Processes
Shang, J., Sun, M., & Lam, N. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403246 · Cited by 9
“There are many definitions of fairness such as group parity [4], equalized odds [9, 30], individual fairness [5], and counterfactual fairness [21].”
BackgroundRemoving Disparate Impact of Differentially Private Stochastic Gradient Descent on Model AccuracyPreprint
Xu, D., Du, W., & Wu, X. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03699 · Cited by 9
“These methods include: Massaging [Kamiran and Calders, 2009], Reweighting [Calders et al., 2009], Sampling [Kamiran and Calders, 2011], Disparate Impact Removal [Feldman et al., 2015], Causation-based Removal [Zhang et al., 2017] and Fair Representation Learning [Edwards and Storkey, 2016; Xie et…”
BackgroundMethodInfluentialFADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomes
Mishler, A., & Kennedy, E. H. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533167 · Cited by 9
Causality-Inspired Fair Representation Learning for Multimodal Recommendation
Chen, W., Chen, L., Ni, Y., & Zhao, Y. (2025) · ACM Transactions on Information Systems · doi:10.1145/3744240 · Cited by 8
“For example, statistical parity [5] encourages that different groups w.r.t. sensitive attributes are treated similarly in terms of recommendation outcome.”
Revisiting model fairness via adversarial examples
Zhang, T., Zhu, T., Li, J., Zhou, W., & Yu, P. S. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110777 · Cited by 8
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm Utility
Cui, S., Pan, W., Zhang, C., & Wang, F. (2021) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3447548.3467251 · Cited by 8
“Examples of such proxies include the proportion of examples classified as positive [5, 6], as well as the prediction performance metrics such as true/false positive rates and error rates [10, 11, 16, 20, 39].”
BackgroundCooperative Multi-Agent Fairness and Equivariant Policies
Grupen, N. A., Selman, B., & Lee, D. D. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i9.21166 · Cited by 12
“Existing work has shown both theoretically (Calders, Kamiran, and Pechenizkiy 2009; Kleinberg, Mullainathan, and Raghavan 2016; Zhao and Gordon 2019) and empirically (Dwork et al. 2012; Feldman et al. 2015; Kamiran and Calders 2009; Lahoti, Gummadi, and Weikum 2019; Pannekoek and Spigler 2021) that…”
Learning fair representations for accuracy parity
Quan, T., Zhu, F., Liu, Q., & Li, F. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105819 · Cited by 10
Learning Fair and Transferable RepresentationsPreprint
Oneto, L., Donini, M., Maurer, A., & Pontil, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.10673 · Cited by 8
“We measure fairness according to demographic parity [7] (for an extended analysis of the different fairness definitions see [33, 36]).”
MethodBriggs, E., & Hollmén, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-61527-7_2 · Cited by 8
Parity-based cumulative fairness-aware boosting
Iosifidis, V., Roy, A. G., & Ntoutsi, E. (2022) · Knowledge and Information Systems · doi:10.1007/s10115-022-01723-3 · Cited by 7
“Example methods include massaging [14] that changes the class-labels of carefully selected instances, re-weighting [9] that assigns different weights to the instances of the protected group, uniform- or preferential-sampling [11] and data augmentation [24] that enriches the minority group through pseudo-instances.”
MethodFranco, D., D’Amato, V., Pasa, L., Navarin, N., & Oneto, L. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126948 · Cited by 8
Graph Fairness Learning under Distribution Shifts
Li, Y., Wang, X., Xing, Y., Fan, S., Wang, R., Liu, Y., & Shi, C. (2024) · International World Wide Web Conference (WWW) · doi:10.1145/3589334.3645508 · Cited by 7
Achieving Private and Fair Truth Discovery in Crowdsourcing Systems
Wang, Z., Cheng, X., Su, S., & Wang, L. (2022) · Security and Communication Networks · doi:10.1155/2022/9281729 · Cited by 6
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data
Fang, M., Liu, J., Momma, M., & Sun, Y. (2022) · ACM Symposium on Access Control Models and Technologies (SACMAT) · doi:10.1145/3532105.3535023 · Cited by 8
“To date, there have been several fairness metrics for recommender systems proposed in the machine learning community, including individual fairness [18], group fairness [12], equalized odds [26] and demographic parity [11].”
BackgroundFairness in Multi-Task Learning via Wasserstein Barycenters
Hu, F., Ratz, P., & Arthur Charpentier, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43415-0_18 · Cited by 7
“In this paper, we focus on the Demographic Parity (DP) [8] that requires the independence between the sensitive feature and the predictions, while not relying on labels (for other notions of fairness, see Equality of odds or Equal opportunity [23]).”
BackgroundFairness Through the Lens of Proportional Equality
Biswas, A., & Mukherjee, S. (2019) · Adaptive Agents and Multi-Agents Systems · doi:10.65109/dzjo3042 · Cited by 7
Discrimination-Aware Association Rule Mining for Unbiased Data Analytics
Luo, L., Liu, W., Koprinska, I., & Chen, F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22729-0_9 · Cited by 7
“This includes removing the sensitive attribute, resampling [7] or relabeling some instances in the dataset to balance class labels for a certain sensitive attribute value [6, 8].”
MethodFairness-Aware Data Preparation for Entity Matching
Shahbazi, N., Wang, J., Miao, Z., & Bhutani, N. (2024) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde60146.2024.00268 · Cited by 6
Pre-processing Techniques to Mitigate Against Algorithmic Bias
Shahrezaei, M. H., Loughran, R., & McDaid, K. (2023) · Irish Conference on Artificial Intelligence and Cognitive Science (AICS) · doi:10.1109/aics60730.2023.10470759 · Cited by 6
“…group was sorted descending, and the Demotion group was sorted ascending: • Promotion: (female, income<=50) • Demotion: (male, income>50k) Selecting the top objects from each group which were the closest objects to the decision boundary can help to have less impact on the accuracy performance [7].”
BackgroundMethodInfluentialMachine Learning Fairness for Depression Detection Using EEG Data
Kwok, A. M. H., Cheong, J., Kalkan, S., & Güneş, H. (2025) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi60581.2025.10980986 · Cited by 13
“We then modify the original loss function (e.g., Cross-Entropy Loss) for a batch B : by directly incorporating β i into the loss function as follows: The weights are chosen according to: as suggested by Calders et al. [16].”
Measuring biases in AI-generated co-authorship networks
Kalhor, G., Ali, S., & Mashhadi, A. (2025) · EPJ Data Science · doi:10.1140/epjds/s13688-025-00555-9 · Cited by 8
Economic Theories of Distributive Justice for Fair Machine Learning
Gummadi, K. P., & Heidari, H. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308560.3320101 · Cited by 6
“We start by noting that parity-based definitions of algorithmic fairness, such as statistical parity [5], disparate impact [10], and equality of opportunity [12], can be thought of as seeking to minimize some form of inequality—for different notions of benefit or well-being .”
BackgroundFair Decision-Making for Food Inspections
Singh, S., Shah, B., Kanich, C., & Kash, I. A. (2022) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3551624.3555289 · Cited by 9
“Their work focuses on three common fairness objectives: Demographic Parity (DP) [5], Equal Opportunity (EOpp) [17], and Equality of Odds [17].”
BackgroundAbebe, S. A., Lucchese, C., & Orlando, S. (2022) · ACM Symposium on Applied Computing · doi:10.1145/3477314.3507319 · Cited by 6
“The bias mitigation approaches on either the training data or the learned model can be categorized into three main groups [8]: (1) Pre-processing approaches such as reweighting [5], massaging [17], aiming to eliminate discrimination at the data level; (2) In-”
BackgroundMethodLearning to Generate Equitable Text in Dialogue from Biased Training Data
Sicilia, A., & Alikhani, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.163 · Cited by 6
Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features
Elzayn, H., Black, E., Vossler, P., Jo, N., Goldin, J. S., & Ho, D. E. (2024) · IEEE Conference on Secure and Trustworthy Machine Learning (SaTML) · doi:10.1109/satml59370.2024.00016 · Cited by 9
Robustness of Fairness: An Experimental Analysis
Kamp, S., Zhao, A. L. L., & Kutty, S. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-93736-2_43 · Cited by 6
Howell, K., Barnes, M., Curtis, J. R., Engelberg, R. A., Lee, R. Y., Lober, W. B., Sibley, J., & Cohen, T. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-53352-6_25 · Cited by 6
Fair Division Without Disparate ImpactPreprint
Peysakhovich, A., & Kroer, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.02775 · Cited by 6
“a classifier for which individual should get a loan (Calders et al., 2009; Feldman et al., 2015; Zemel et al., 2013; Louizos et al., 2015).”
BackgroundMethodCrowds, Lending, Machine, and BiasPreprint
Fu, R., Huang, Y. H., & Singh, P. V. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3206027 · Cited by 6
“correlated with sensitive attributes, which are commonly referred as redundant encodings (Pedreshi et al. 2008). Several methods have been proposed to help correct the bias problem (Berk et al. 2017, Calders et al. 2009, Hardt et al. 2016, Kamiran et al. 2010, Pedreshi et al. 2008). While these methods achieve promisi…”
BackgroundMethodConvex Formulations for Fair Principal Component Analysis
Olfat, M., & Aswani, A. J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.3301663 · Cited by 9
Badar, M., & Fisichella, M. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8020016 · Cited by 5
“Reweighting [13] is another less intrusive pre-processing method presented in the literature to reduce discrimination.”
MethodThe Many Facets of Data Equity
Jagadish, H. V., Stoyanovich, J., & Howe, B. (2022) · Journal of Data and Information Quality · doi:10.1145/3533425 · Cited by 9
“Indeed, there has been a great deal of work on how to specify fairness in algorithmic decisionmaking, with several dozen definitions in the literature based on statistical independence conditions: predictions given protected attribute [14, 23, 74] (demographic parity, statistical parity); prediction accuracy condition…”
BackgroundBoundary-Based Fairness Constraints in Decision Trees and Random Forests
Nanfack, G., Delchevalerie, V., & Frénay, B. (2021) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2021.es2021-69 · Cited by 5
Privileged and unprivileged groups
Hort, M., & Sarro, F. (2022) · doi:10.1145/3524491.3527308 · Cited by 8
“We perform our experiments on two publicly available, real-world datasets, widely studied in the fairness literature [10, 14, 16, 22, 56, 57]: the German, and Bank dataset.”
BackgroundMethodSimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
Liu, T., Wang, H., Wang, Y., Wang, X., Su, L., & Gao, J. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26677 · Cited by 7
Fairness of Interaction in Ranking under Position, Selection, and Trust Bias
Ovaisi, Z., Saadatpanah, P., Sefati, S., Ohannessian, M. I., & Zheleva, E. (2024) · ACM Transactions on Recommender Systems · doi:10.1145/3652864 · Cited by 5
“For example, demographic parity aims at affecting — provide interaction with — each group equally [7, 19, 36].”
Efficient Exploration of the Rashomon Set of Rule-Set Models
Ciaperoni, M., Xiao, H., & Gionis, A. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3671818 · Cited by 5
Fair and optimal prediction via post‐processing
Zhao, H. (2024) · AI Magazine · doi:10.1002/aaai.12191 · Cited by 5
Promoting fairness in link prediction with graph enhancement
Liu, Y., Chen, H., & Imani, M. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1489306 · Cited by 6
“…learning can be broadly categorized into three approaches: (1) Pre-processing , where bias is mitigated at the data level before training begins (Calders et al., 2009; Kamiran and Calders, 2009; Feldman et al., 2015); (2) In-processing , where the machine learning model itself is modified by…”
BackgroundInfluentialCIKM 2021 Tutorial on Fairness of Machine Learning in Recommender Systems
Li, Y., Ge, Y., & Zhang, Y. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3483280 · Cited by 5
“The group fairness perspective for supervised learning usually implies constraints such as equalized odds [32, 61] and demographic parity [11].”
BackgroundSLIDE: A surrogate fairness constraint to ensure fairness consistency
Kim, K., Ohn, I., Kim, S., & Kim, Y. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.07.027 · Cited by 5
“Group fairness [4, 5, 6] focuses on treating fairly each sensitive group while individual fairness [7] focuses on treating fairly any similar individuals.”
Towards Fairer Classifier via True Fairness Score Path
Gu, B., Zhai, Z., Li, X., & Huang, H. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557109 · Cited by 5
Recover Fair Deep Classification Models via Altering Pre-trained Structure
Zhang, Y., Gao, S., & Huang, H. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_28 · Cited by 5
“To fit into different application scenario, researchers proposed various definitions for algorithmic fairness, including individual fairness [13], demographic parity [8], equal odds and equal opportunities [21], disparate treatment, impact, and mistreatment [49].”
BackgroundFair Data Generation and Machine Learning Through Generative Adversarial Networks
Wu, X., Xu, D., Yuan, S., & Zhang, L. (2022) · Intelligent systems reference library · doi:10.1007/978-3-030-91390-8_3 · Cited by 7
Introducing Positive Discrimination in Predictive Models
Verwer, S., & Calders, T. (2012) · Studies in applied philosophy, epistemology and rational ethics · doi:10.1007/978-3-642-30487-3_14 · Cited by 5
Self-citationFairness Criteria through the Lens of Directed Acyclic Graphs
Baer, B. R., Gilbert, D. E., & Wells, M. T. (2020) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780190067397.013.31 · Cited by 14
Dynamic Modeling and Equilibria in Fair Decision MakingPreprint
Williams, J., & Kolter, J. Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.06837 · Cited by 5
“For example, when applying demographic parity [3, 31] in a loan setting, if required representation proportition for both groups is too large, the policy will allow many people to receive loans they cannot repay, causing the group financial state to drop.”
BackgroundAdomavicius, G., & Yang, M. (2019) · Social Science Research Network · doi:10.2139/ssrn.3446944 · Cited by 3
“As one example, a predictive model sometimes achieves fairness at the expense of accuracy (Calders et al. 2009, Corbett Davies et al. 2017), and the trade-off between fairness and accuracy imposes tricky incentive problems for decision makers, e.g., a profit-maximizing firm under fairness…”
Neto, E. C. (2020) · arXiv (Cornell University) · Cited by 5
Neto, E. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.09466 · Cited by 5
Technical Challenges for Training Fair Neural NetworksPreprint
Cherepanova, V., Nanda, V., Goldblum, M., Dickerson, J. P., & Goldstein, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.06764 · Cited by 25
“Disparate Impact is a widely adopted notion that requires any decision making process’ outcomes to be independent of membership in a sensitive group (Calders et al., 2009; Barocas & Selbst, 2016; Chouldechova, 2017; Feldman et al., 2015):”
BackgroundPareto Efficient Fairness in Supervised Learning: From Extraction to TracingPreprint
Kamani, M. M., Forsati, R., Wang, J. Z., & Mahdavi, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01634 · Cited by 16
“The second approach tries to modify the existing classifiers to impose the fairness constraints [60, 27, 10, 11].”
MLCheck- Property-Driven Testing of Machine Learning ModelsPreprint
Sharma, A., Demir, C., Ngomo, A.-C. N., & Wehrheim, H. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.00741 · Cited by 5
“Moreover, we employ two fair-aware classifiers [5, 41], i.”
Fairness in Algorithmic Profiling: The AMAS Case
Achterhold, E., Mühlböck, M., Steiber, N., & Kern, C. A. (2025) · Minds and Machines · doi:10.1007/s11023-024-09706-9 · Cited by 12
“• Reweighing (RW) (Calders et al., 2009).”
de Lima, L. F. F. P., Ricarte, D. R. D., & Siebra, C. D. A. (2022) · Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI) · doi:10.1145/3535511.3535517 · Cited by 4
“ity of the predictions must be equal for both groups of the protected attribute, being the decision independent of the protected attribute [8].”
BackgroundIdentifying Reasons for Bias: An Argumentation-Based Approach
Waller, M., Rodrigues, O., & Cocarascu, O. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30165 · Cited by 4
Generalization in multi-objective machine learning
Súkeník, P., & Lampert, J. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10616-1 · Cited by 10
“Consequently, our results from Section 4 apply, yielding a unified understanding of the generalization properties of fairness-aware learning, e.g., regularization-based (Kamishima et al. , 2011) constraint-based (Calders et al. , 2009), or Pareto-based (Liu & Vicente, 2022; Navon et al. , 2021).”
Fairness-Aware Decision Tree Editing Based on Mixed- Integer Linear Optimization
Kanamori, K., & Arimura, H. (2021) · Transactions of the Japanese Society for Artificial Intelligence · doi:10.1527/tjsai.36-4_b-l13 · Cited by 4
Fontana, M., Naretto, F., Monreale, A., & Giannotti, F. (2022) · Frontiers in artificial intelligence and applications · doi:10.3233/faia220205 · Cited by 4
Fair Selection through Kernel Density Estimation
Jiang, X. S., Dai, Y., & Wu, Y. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191616 · Cited by 4
“Common pre-processing methods include Massaging [19], which changes the labels of some individuals around the decision boundaries to remove discrimination, Reweighting [5], which assigns weights to individuals to balance the majority and minority groups, and Preferential Sampling [20], which resamples subgroups to mak…”
BackgroundMethodInfluentialA statistical approach to detect disparity prone features in a group fairness setting
Pelegrina, G. D., Couceiro, M., & Duarte, L. T. (2023) · AI and Ethics · doi:10.1007/s43681-023-00363-9 · Cited by 4
“These methods fall into four main categories, namely, pre-processing [9–12], in-processing [13–15], Miguel Couceiro and Leonardo Tomazeli Duarte contributed equally to this work. post-processing [16–18], and hybrid-processing 1 [19–21].”
The epistemic ethical concerns involving algorithms in intelligent communication
Lin, J., & Chen, C. (2023) · Teknokultura Revista de Cultura Digital y Movimientos Sociales · doi:10.5209/tekn.87552 · Cited by 5
Fair Learning by Model Averaging
Fermanian, J., Guégan, D., & Liu, X. (2025) · Risk and Decision Analysis · doi:10.1177/15697371251321734 · Cited by 4
Online and Customizable Fairness-aware Learning
Zhang, W. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02568-2 · Cited by 4
“These approaches generally fall into one of the following categories: i) pre-processing approaches, ii) in-processing approaches and iii) post-processing approaches.”
The Random Conditional Distribution for Higher-Order Probabilistic InferencePreprint
Tavares, Z., Zhang, X., Minaysan, E., Burroni, J., Ranganath, R., & Solar-Lezama, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.10556 · Cited by 4
“on nested probabilistic programs with focus on inference can be found in [32] Algorithmic fairness and robustness have received much attentionrecently.Inmachinelearning,variousfairnessspecifications [3, 18, 19, 23] have been proposed and many methods [4, 7, 11, 12, 20] have been designed to train a model to satisfy th…”
MethodTowards a Measure of Individual Fairness for Deep LearningPreprint
Maughan, K., & Near, J. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.13650 · Cited by 4
“The bulk of previous work on fairness in machine learning attempts to improve fairness at training time, often by the introduction of new kinds of regularization [3, 8, 10, 32, 37–39].”
Generative Models for Security: Attacks, Defenses, and OpportunitiesPreprint
Bauer, L. A., & Bindschaedler, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.10139 · Cited by 14
“There have been many attempts to create truly fair models, from removing sensitive attributes from models representations [150, 273] to optimizing models to ignore sensitive attributes [33, 249].”
BackgroundPropose and Review: Practical Bias Mitigation for Machine ClassificationPreprint
Li, T., Tang, Z., Lu, T., & Zhang, X. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4139244 · Cited by 5
Hierarchical Reinforcement Learning for Long-term Fairness in Interactive Recommendation
Xia, C., Shi, X., Xie, H. B., Liu, Q., & Shang, M. (2024) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws62655.2024.00052 · Cited by 3
Metadata
- Authors
- Calders, T., Kamiran, F., & Pechenizkiy, M.
- Affiliations
- Eindhoven University of Technology (NL)
- Venue
- 2009 IEEE International Conference on Data Mining Workshops
- Publisher
- IEEE
- Published
- 2009-12
- Pages
- 13-18
- ISSN
- 2375-9259 (ISSN-L)
- OpenAlex
- W2116666691
- Cited by
- 468
- References
- 11
- Citations by year
- 2026: 16 · 2025: 45 · 2024: 64 · 2023: 51 · 2022: 65 · 2021: 79 · 2020: 60 · 2019: 48 · 2018: 17 · 2017: 5 · 2016: 2 · 2015: 2 · 2014: 1 · 2012: 5
- FWCI
- 2.58
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE ... International Conference on Data Mining workshops (publishedVersion)
- TU/e Research Portal (publishedVersion)
- TU/e Research Portal (submittedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- biased training data, classifier training
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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