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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“While Fairlearn [4] was used as a post-processing baseline for binary classification, our primary focus is on adapting pre-processing techniques from [10, 22], and [23] for multi-class settings.”
InfluentialLee, Y. A., Tang, T., Huang, Y., Bian, J., Shi, L., & Guo, J. S. (2025) · medRxiv · doi:10.1101/2025.11.11.25339938
“This includes evaluating algorithmic behavior using fairness metrics such as demographic parity, equalized odds, and predictive rate parity, 14,15,16,17 all of which aim to prevent systematic disadvantages across protected subgroups.”
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“The Demographic Parity ( DP ), defined as evaluates the average pairwise disparity in positive prediction rates across groups [34].”
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“Examples include Calders et al. (2009); Kamiran and Calders (2009); ˇZliobaite et al. (2011); Kamiran and Calders (2012); Zemel et al. (2013); Fish et al. (2015); Feldman et al. (2015); Beutel et al. (2017).”
BackgroundFairwashing: the risk of rationalization
Aïvodji, U., Arai, H., Fortineau, O., Gambs, S., Hara, S., & Tapp, A. (2019) · arXiv · doi:10.48550/arxiv.1901.09749 · Cited by 54
“With respect to fairness, among the numerous existing definitions (Narayanan, 2018), we rely on the demographic parity (Calders et al., 2009) as the fairness metric.”
BackgroundMethodNoise-tolerant fair classification
Lamy, A. L., Zhong, Z., Menon, A. K., & Verma, N. (2019) · arXiv · doi:10.48550/arxiv.1901.10837 · Cited by 36
“Recently, much progress has been made on devising appropriate measures of fairness (Calders et al., 2009; Dwork et al., 2011; Feldman, 2015; Hardt et al., 2016; Zafar et al., 2017b,a; Kusner et al., 2017; Kim et al., 2018; Speicher et al., 2018; Heidari et al., 2019), and means of achieving them…”
Influential
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
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