The DOI matches its Crossref record; 8 of the 10 references it cites were found in a registry or an index.
Classifying without discriminating
Abstract
Classification models usually make predictions on the basis of training data. If the training data is biased towards certain groups or classes of objects, e.g., there is racial discrimination towards black people, the learned model will also show discriminatory behavior towards that particular community. This partial attitude of the learned model may lead to biased outcomes when labeling future unlabeled data objects. Often, however, impartial classification results are desired or even required by law for future data objects in spite of having biased training data. In this paper, we tackle this problem by introducing a new classification scheme for learning unbiased models on biased training data. Our method is based on massaging the dataset by making the least intrusive modifications which lead to an unbiased dataset. On this modified dataset we then learn a non-discriminating classifier. The proposed method has been implemented and experimental results on a credit approval dataset show promising results: in all experiments our method is able to reduce the prejudicial behavior for future classification significantly without loosing too much predictive accuracy.
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In the citing paper
“Much of the previous work has primarily focused on settings where the outcome or protected variable is binary.”
From An algorithm for removing sensitive information: Application to race-independent recidivism prediction (2019) · cites this paper as Kamiran and Calders (2009)
In the citing paper
“Methodology for achieving statistical parity has centered on removing information about the protected variables from the training data by transforming the training data.”
From An algorithm for removing sensitive information: Application to race-independent recidivism prediction (2019) · cites this paper as Kamiran and Calders (2009)
In the citing paper
“This procedure is limited to binary outcome data and the adjusted data is not re-usable in the sense that one could not then use the covariates to estimate relationships with other outcome variables and still be ensured of nondiscriminatory outcomes.”
From An algorithm for removing sensitive information: Application to race-independent recidivism prediction (2019) · cites this paper as Kamiran and Calders (2009)
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 [39]
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Metadata
- Authors
- Kamiran, F., & Calders, T.
- Affiliations
- Eindhoven University of Technology (NL)
- Venue
- 2009 2nd International Conference on Computer, Control and Communication
- Publisher
- IEEE
- Published
- 2009-02
- Pages
- 1-6
- OpenAlex
- W2157928966
- Cited by
- 409
- References
- 10
- Citations by year
- 2026: 15 · 2025: 26 · 2024: 43 · 2023: 57 · 2022: 48 · 2021: 52 · 2020: 51 · 2019: 46 · 2018: 27 · 2017: 11 · 2016: 5 · 2015: 7 · 2014: 3 · 2013: 1 · 2012: 8
- FWCI
- 28.77
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer, Control and Communication (publishedVersion)
- TU/e Research Portal (publishedVersion)
- TU/e Research Portal (submittedVersion)
- 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 discrimination, biased training data, racial discrimination, credit approval, algorithmic fairness, prediction accuracy
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 ›
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