The DOI matches its Crossref record; 14 of the 15 references it cites were found in a registry or an index.
Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Even if companies make efforts not to include such sensitive (synthetic) data in their analyses, the technology used can still indirectly lead to the systematic disadvantaging of certain (groups of) consumers (Calders & Žliobaitė, 2013).”
From Discrimination grounds and personalised pricing: Consumer perceptions of fairness, norm alignment, legality, and trust in markets (2024) · cites this paper as Calders & Žliobaitė, 2013
In the citing paper
“Even if companies do not directly use sensitive data in their analyses, neutral characteristics can still be correlated to sensitive characteristics (Calders & Žliobaitė, 2013;Solove, 2024).”
From Discrimination grounds and personalised pricing: Consumer perceptions of fairness, norm alignment, legality, and trust in markets (2024) · cites this paper as Calders & Žliobaitė, 2013
In the citing paper
“Data pre-processing removes the discrimination information from the historical data (target or input variables) and then applies regular machine learning approaches for model estimation”
From The Discriminating (Pricing) Actuary (2023) · cites this paper as Calders and Žliobait˙ e (2013)
In the citing paper
“Weights can be introduced for multiple purposes: 1) to indicate a frequency count for an instance type (e.g.[52]), 2) to place lower/higher importance on łsensitivež training samples (e.g.[53,152,165]), or 3) to improve classiier stability (e.g.[181]).”
From Fairness in machine learning: A survey (2024) · cites this paper as [53]
Citations
Cites
Seel, N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5195 · Cited by 19,482
Data mining and knowledge discovery: making sense out of data
Feyyad, U. M. (1996) · IEEE Expert · doi:10.1109/64.539013 · Cited by 4,644
Learning in the Presence of Concept Drift and Hidden Contexts
Widmer, G., & Kubat, M. (1996) · Machine Learning · doi:10.1023/a:1018046501280 · Cited by 1,857
Learning and evaluating classifiers under sample selection bias
Zadrozny, B. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015425 · Cited by 800
Three naive Bayes approaches for discrimination-free classification.
Calders, T., & Verwer, S. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0190-x · Cited by 764
Self-citationMeasuring racial discrimination
(2005) · Choice Reviews Online · doi:10.5860/choice.42-4949 · Cited by 547
Accident risk and risk-taking behaviour among young drivers
Jonah, B. A. (1986) · Accident Analysis & Prevention · doi:10.1016/0001-4575(86)90041-2 · Cited by 472
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-citationDiscrimination Aware Decision Tree Learning
Kamiran, F., Calders, T., & Pechenizkiy, M. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.50 · Cited by 303
Self-citationThe impact of changing populations on classifier performance
Kelly, M. G., Hand, D. J., & Adams, N. M. (1999) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/312129.312285 · Cited by 226
Research works printed but not linked: 2
- Kamiran, F., Calders, T.: Classification with no discrimination by preferential sampling. In: Proceedings of the 19th Annual Machine Learning Conference of Belgium and the Netherlands (BENELEARN 2010), pp. 1–6 (2010)
- Turner, A., Skidmore, F.: Introduction, Summary, and Recommendations. In: Turner, A., Skidmore, F. (eds.) Mortgage Lending Discrimination: A Review of Existing Evidence (Urban Institute Monograph Series on Race and Discrimination), pp. 1–22. Urban Institute Press, Washington, DC (1999)
Cited by
Sorted by citations
Barocas, S., & Selbst, A. D. (2014) · doi:10.2139/ssrn.2477899 · Cited by 3,048
Bias 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
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0279-x · Cited by 709
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646
Optimized Pre-Processing for Discrimination Prevention
Calmon, F. P., Wei, D., Vinzamuri, B., Ramamurthy, K. N., & Varshney, K. R. (2017) · neural information processing systems · Cited by 472
Metadata
- Authors
- Calders, T., & Žliobaitė, I.
- Affiliations
- Eindhoven University of Technology (NL)
- Bournemouth University (GB)
- Corresponding author
- Calders, Toon
- Venue
- Studies in Applied Philosophy, Epistemology and Rational Ethics
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 43-57
- ISSN
- 2192-6255 (ISSN-L)
- 2192-6263
- OpenAlex
- W2118025696
- Cited by
- 128
- References
- 15
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 10 · 2023: 14 · 2022: 14 · 2021: 20 · 2020: 12 · 2019: 10 · 2018: 6 · 2017: 17 · 2016: 5 · 2015: 1 · 2014: 6 · 2013: 3
- FWCI
- 17.19
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Studies in applied philosophy, epistemology and rational ethics (publishedVersion)
- Dépôt institutionnel de l'Université libre de Bruxelles (Université Libre de Bruxelles) (submittedVersion)
- TU/e Research Portal (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Discrimination and Equality Law
- Keywords
- algorithmic discrimination, algorithmic fairness, data mining, automated decision support, data quality, prediction accuracy, gender bias, ethnic prejudice
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