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.
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
“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]
Citations
Cites
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
The foundations of cost-sensitive learning
Elkan, C. (2001) · Cited by 1,945
Domingos, P. (1999) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312220 · Cited by 1,325
Discrimination-aware data mining
Pedreshi, D., Ruggieri, S., & Turini, F. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401959 · Cited by 676
Chan, P. K., & Stolfo, S. J. (1998) · Cited by 452
Classifying without discriminating
Kamiran, F., & Calders, T. (2009) · 2009 2nd International Conference on Computer, Control and Communication · doi:10.1109/ic4.2009.4909197 · Cited by 409
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
Duivesteijn, W., & Feelders, A. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87479-9_38 · Cited by 90
Statistical Model for Rough Set Approach to Multicriteria Classification
Dembczyński, K., Greco, S., Kotłowski, W., & Słowiński, R. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74976-9_18 · Cited by 32
Learning decision trees for loss minimization in multi-class problems
Margineantu, D. D., & Dietterich, T. G. (1999) · Cited by 18
Cited by
Sorted by citations
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 4,567
“…may be possible to direct algorithms not to consider sensitive attributes that contribute to discrimination (Barocas and Selbst, 2015), such as gender or ethnicity (Calders et al., 2009; Kamiran and Calders, 2010; Schermer, 2011), based upon the emergence of discrimination in a particular context.”
Explainable AI: a review of machine learning interpretability methods
Linardatos, P., Papastefanopoulos, V., & Kotsiantis, S. (2020) · Entropy · doi:10.3390/e23010018 · Cited by 2,922
“Finally the idea behind the third preprocessing technique, as initially presented in [75], is to apply different weights to the instances of the dataset based on frequency counts with respect to the sensitive column.”
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
“[2] have also previously examined one method for changing the data in which different data items are given weights and the weights are adjusted to achieve fairness.”
BackgroundMethodData 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
“We further explore the problem of discrimination in [4] where we concentrate on the case where a labeled dataset is given, and one binary attribute B which we do not want the predictions to correlate with.”
BackgroundInfluentialSelf-citationBias in data‐driven artificial intelligence systems—An introductory survey
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., et al. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1356 · Cited by 1,043
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.
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
