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
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
“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
Items 101–131 of 131
Hiemstra, A. M. F., Cassel, T., Born, M. P., & Liem, C. C. S. (2020) · Gedrag & Organisatie · doi:10.5117/2020.033.004.002 · Cited by 1
Towards an AutoML System for Fair Classifications∗
Lässig, N. (2023) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde55515.2023.00380 · Cited by 1
15. Making Algorithms Fair : Ethnographic Insights from Machine Learning Interventions
Kinder-Kurlanda, K. E., & Fahimi, M. (2024) · Amsterdam University Press eBooks · doi:10.1515/9789048556908-015 · Cited by 4
Evaluating Fairness of Mask R-CNN for Kidney Infection Detection based on Renal Scintigraphy
Wang, J., Wu, M., Guo, Y., Wu, H., & Wan, Z. (2024) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata62323.2024.10825439 · Cited by 1
A Multi-purpose Data Pre-processing Framework using Machine Learning for Enterprise Data Models
Venkata Ramana, B., & Narsimha, G. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120376 · Cited by 1
TODOS SÃO IGUAIS PERANTE O ALGORITMO? UMA RESPOSTA CULTURAL DO DIREITO À DISCRIMINAÇÃO ALGORÍTMICA
Duarte, A., & Negócio, R. D. V. (2022) · Direito Público · doi:10.11117/rdp.v18i100.5869 · Cited by 1
L'acte administratif algorithmique
Stepanov, A. (2026) · doi:10.70675/adc05be3za62ez4b4fz81e7zabf744e60dbf
TECHNOLOGY Discrimination Discovery and Prevention in Data Mining
Singh, J., Sane, S. S., & Wagh, K. K. (2014)
Ontology Based Data Mining Methodology for Discrimination Prevention
Nagabhushana, N., & Natarajan, S. (2014) · doi:10.5121/csit.2014.4904
Costa, L. (2016) · Law, governance and technology series · doi:10.1007/978-3-319-39198-4_4
A World of Ambient Intelligence
Costa, L. (2016) · Law, governance and technology series · doi:10.1007/978-3-319-39198-4_2
Methods and Tools for Interpretable Bayesian Variable Selection
Paasiniemi, M. (2018)
General Perspectives Toward the Impact of AI on Race and Society
Roberts, A. L., Richardson, B., Alikhademi, K., Drobina, E., & Gilbert, J. E. (2021) · Diversity and inclusion research · doi:10.1007/978-3-030-65417-7_18
Investigating Explanations that Target Training Data.
Anik, A. I., & Bunt, A. (2021) · IUI Workshops
Logic Constraints to Feature ImportancesPreprint
Picchiotti, N., & Gori, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.06596
The role of trustworthiness in automated decision-making systems and the law
Wang, B. T. (2022) · Queensland University of Technology · doi:10.5204/thesis.eprints.231388
Zobel, J., Vázquez-Abad, F. J., & Lin, P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271268
Improving Autoencoder-Based Anomaly Detection in Embedded Systems using Data TransformationPreprint
de Oliveira, J. P. G., Bastos-Filho, C. J. A., & Oliveira, S. C. (2022) · Research Square · doi:10.21203/rs.3.rs-1946828/v1
Digital Services and Algorithmic Discrimination
Tommasi, S. (2023) · SpringerBriefs in law · doi:10.1007/978-3-031-43640-6_3
Fair Classification with Ensembles
Turgut, Ö. (2023) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu58738.2023.10296790
Identifying Challenges of Governmental Automated Decision-Making
Schäferling, S. (2023) · Law, governance and technology series · doi:10.1007/978-3-031-48125-3_4 · Cited by 1
Arthur Charpentier, A. (2024) · Springer Actuarial · doi:10.1007/978-3-031-49783-4_5
Fundamentals of Actuarial Pricing
Arthur Charpentier, A. (2024) · Springer Actuarial · doi:10.1007/978-3-031-49783-4_2
Requirements Elicitation in the Age of AI: A Tool’s Multi-system Journey
Ahmad, K., Arora, C., Abdelrazek, M., Grundy, J., & Vasa, R. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-64182-4_4
Networked Employment DiscriminationPreprint
Rosenblat, A., Kneese, T., & boyd, D. (2017) · doi:10.31219/osf.io/dp5aq_v1
SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI
Giddens, S., Lang, X., & Liu, F. (2025) · ACM transactions on probabilistic machine learning. · doi:10.1145/3777907
Risks of Algorithmic Decision-Making in Administrative Procedures and Their Legal Regulation
杜, 帛. (2026) · Dispute Settlement · doi:10.12677/ds.2026.122041
Sarial‐Abi, G., & Giannetti, V. (2026) · British Journal of Management · doi:10.1111/1467-8551.70045
Navigating the nexus: the rule of law in the age of artificial intelligence (AI) advancements
Alrdaan, R. F. (2026) · The International Journal of Human Rights · doi:10.1080/13642987.2026.2630719
Berendsen, S., & Beauxis-Aussalet, E. (2026) · AI and Ethics · doi:10.1007/s43681-026-01375-x
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
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
