Cited by 468Machine Learning and Data ClassificationMachine Learning and AlgorithmsData Mining and Machine Learning Applications

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Building Classifiers with Independency Constraints

Calders, Toon, Kamiran, Faisal, Pechenizkiy, Mykola·2009·2009 IEEE International Conference on Data Mining Workshops

DOI 10.1109/icdmw.2009.83

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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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Background
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Background
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Background
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BackgroundMethodInfluential
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Background
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BackgroundMethodInfluential
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Background
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Method
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Background
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BackgroundMethodInfluential
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“In response, researchers have started to develop a number of approaches to encourage fairness in various statistical or machine learning problems [13, 18, 26, 35, 53, 77, 82].”
Background
Remove Bias from ML Model
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Chzhen, E., & Schreuder, N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.07158 · Cited by 2
“To address the above shortcoming, we further restrict the space of possible predictions to those satisfying Demographic Parity (DP) (Calders et al., 2009, 2013).”
Stable predictions for health related anticausal prediction tasks affected by selection biases: the need to deconfound the test set featuresPreprint
Neto, E. C., Snyder, P., Sieberts, S. K., & Omberg, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04128 · Cited by 1
Towards Auditability for Fairness in Deep LearningPreprint
Ngong, I. C., Maughan, K., & Near, J. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.00106 · Cited by 1
“The majority of work on fairness in AI has focused on ensuring group fairness [8, 31, 32, 4, 25, 10, 7, 26, 35, 30, 34, 20, 22, 9, 14, 17].”
Background
Fairness-guided SMT-based Rectification of Decision Trees and Random ForestsPreprint
Zhang, J., Beschastnikh, I., Mechtaev, S., & Roychoudhury, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.11001 · Cited by 1
“c) Beyond Group Fairness: Our approach focuses on group fairness [6], which requires members of the sensitive groups to be classified at approximately the same rate.”
Everything is Relative: Understanding Fairness with Optimal TransportPreprint
Kwegyir-Aggrey, K., Santorella, R., & Brown, S. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.10349 · Cited by 3
“A classifier is fair under demographic parity if the classifier’s predictions are independent from the sensitive attribute Calders et al. [2009]: ED[h(X)|S = 1] = ED[h(X)|S = 0].”
Background
Explainability-aided Domain Generalization for Image ClassificationPreprint
Schmidt, R. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.01742 · Cited by 3
“Group fairness [25], also known as demographic parity or statistical parity, aims at equalizing benefits across groups with respect to protected characteristics (e.”
Background
A Joint Fairness Model with Applications to Risk Predictions for Under-represented Populations.Preprint
Do, H., Nandi, S., Putzel, P., Smyth, P., & Zhong, J. (2021) · PubMed · Cited by 1
Towards Model-Agnostic Post-Hoc Adjustment for Balancing Ranking Fairness and Algorithm UtilityPreprint
Cui, S., Pan, W., Zhang, C., & Wang, F. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.08267 · Cited by 3
“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].”
Background
Bias-Tolerant Fair ClassificationPreprint
Zhang, Y., Zhou, F., Li, Z., Wang, Y., & Chen, F. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.03207 · Cited by 1
“Related Work Fairness in machine learning Most algorithmic fairness approaches in the literature incorporate fairness constraints into the objective function (Zafar et al., 2017a,b; Calders et al., 2009; Agarwal et al., 2018; Kamishima et al., 2012) for optimization.”
BackgroundMethod
Characterizing and Quantifying Performance Heterogeneity in Cardiovascular Risk Prediction Models — A Step Towards Improved Disease Risk AssessmentPreprint
Kartoun, U., Khurshid, S. S., Kwon, B. C., Patel, A. P., Batra, P., Philippakis, A., Khera, A. V., Ellinor, P. T., Lubitz, S. A., & Ng, K. (2021) · Research Square · doi:10.21203/rs.3.rs-936366/v1 · Cited by 1
Randomized Classifiers vs Human Decision-Makers: Trustworthy AI May Have to Act Randomly and Society Seems to Accept ThisPreprint
Erdélyi, G., Erdélyi, O. J., & Estivill‐Castro, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.07545 · Cited by 5
Interpolating Item and User Fairness in Multi-Sided RecommendationsPreprint
Chen, Q., Liang, J. C. N., Golrezaei, N., & Bouneffouf, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4476512 · Cited by 6
Positivity-free Policy Learning with Observational DataPreprint
Zhao, P., Chambaz, A., Josse, J., & Yang, S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.06969 · Cited by 2
“For randomized predictions f : X ×S → ∆(A), popular fairness criteria include demographic parity (DP) [Calders et al., 2009]: E[f(X,S) | S = s] = E[f(X,S) | S = s′], ∀s, s′ ∈ S, (7)”
Background
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
Assessing Algorithm Fairness Requires Adjustment for Risk Distribution Differences: Re-considering the Equal Opportunity CriterionPreprint
Hegarty, S. E., Linn, K. A., Zhang, H., Teeple, S., Albert, P. S., Parikh, R. B., Courtright, K. R., Kent, D. M., & Chen, J. (2025) · medRxiv · doi:10.1101/2025.01.31.25321489 · Cited by 2
DAAR: A Discrimination-Aware Association Rule Classifier for Decision Support
Luo, L., Liu, W., Koprinska, I., & Chen, F. (2017) · Lecture notes in computer science · doi:10.1007/978-3-662-55608-5_3
Histogram-Based Asymmetric Relabeling for Learning from Only Positive and Unlabeled Data
Arjannikov, T., & Tzanetakis, G. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.000-8
“Relabeling data as a pre-processing data mining step has also been shown to work well for discrimination-aware classification [15]–[18].”
Method
Enumeration of Distinct Support Vectors for Interactive Decision MakingPreprint
Kanamori, K., Hara, S., Ishihata, M., & Arimura, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01876
“To evaluate the fairness of the model m for the sensitive attribute z ∈ {−1, 1}, we used demographic parity (DP) (Calders et al., 2009) defined by δ(m |z) :=”
Method
Predictive Modeling of Asynchronous Event Sequence Data
Shang, J. (2020) · doi:10.31390/gradschool_dissertations.5250
Abstraction and Prediction Algorithms: A Harm-reduction Framework
Desilvestro, A. (2020) · Tuwhera (Auckland University of Technology)
Fair Learning: an optimal transport based approach
Gordaliza, P. (2020) · UVaDOC UVaDOC University of Valladolid Documentary Repository (University of Valladolid) · doi:10.35376/10324/43392
Avaliação de Discriminação em Aprendizagem de Máquina usando Técnicas de Interpretabilidade.
Cesaro, J. (2020) · doi:10.11606/d.3.2020.tde-31052021-114333
THE PRICE OF DIVERSITY
(2005) · Routledge eBooks · doi:10.4324/9780203976142-22
FARF: A Fair and Adaptive Random Forests ClassifierPreprint
Zhang, W., Bifet, A., Zhang, X., Weiss, J. C., & Nejdl, W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.07403
“These studies have typically adopted one or more of the three following strategies: i) Pre-processing solutions [8,9,10] aim to eliminate discrimination at the data level, including the most popular ones massaging [11] and reweighting [12].”
Background
Costs and Benefits of Fair Regression
Zhao, H. (2024) · doi:10.55277/researchhub.mz9wqvk1
“Tradeoff between Fairness and Accuracy Although it has long been empirically observed that there is an inherent tradeoff between accuracy and statistical parity in both classification and regression problems (Calders et al., 2009; Zafar et al., 2015; Zliobaite, 2015; Berk et al., 2017; Corbett-Davies et al., 2017; Zha…”
BackgroundMethodInfluential
FAIRLEARN:Configurable and Interpretable Algorithmic FairnessPreprint
Kulshrestha, A., & Safro, I. (2021) · arXiv (Cornell University)
Offsetting Unequal Competition Through RL-Assisted Incentive Schemes
Koley, P., Maiti, A., Bhattacharya, S., & Ganguly, N. (2022) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2022.3140659
“The work presented here is in line with the design of fair incentive schemes, which is an important area in fair machine learning [59]–[62].”
Result
Towards Discrimination-Free Classification via Fairness-Aware Bagging
Papagiannopoulos, P., Aridas, C. K., Kanellopoulos, Y., & Makris, C. H. (2021) · Panhellenic Conference on Informatics · doi:10.1145/3503823.3503858
“Reweighing improved slightly the Fairness of the base model while it reduced just a bit its predictive power.”
BackgroundInfluential
Interpretable and Anti-Bias Machine Learning Models for Human Event Sequence Data
Zhou, Z. (2022) · Louisiana State University · doi:10.31390/gradschool_dissertations.5740
“There are many classic definitions of fairness including group parity [8] and individual fairness [16].”
Background

Authors: Calders, T., Kamiran, F., & Pechenizkiy, M.
Affiliations: Eindhoven University of Technology (NL)
ORCID: Calders, Toon
Kamiran, Faisal
Pechenizkiy, Mykola
Venue: 2009 IEEE International Conference on Data Mining Workshops
Publisher: IEEE
Published: 2009-12
Pages: 13-18
ISSN: 2375-9259 (ISSN-L)
DOI: 10.1109/icdmw.2009.83
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

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