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Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models
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“Many of the methods to tackle discrimination in AI systems implicitly assume that organisations hold these sensitive data”
From Discrimination, artificial intelligence, and algorithmic decision-making (2025) · cites this paper as Žliobaitė and Custers 2016
Citations
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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
Fairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021) · Sociological Methods & Research · doi:10.1177/0049124118782533 · Cited by 988
“Under these circumstances, it is possible for a risk instrument to have predictive parity but not be well calibrated.”
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Measuring discrimination in algorithmic decision making
Žliobaitė, I. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0506-1 · Cited by 272
Self-citation
Metadata
- Authors
- Žliobaitė, I., & Custers, B.
- Affiliations
- University of Helsinki (FI)
- Leiden University (NL)
- Corresponding author
- Žliobaitė, Indrė
- Venue
- Artificial Intelligence and Law
- Published
- 2016-05-07
- Volume
- 24
- Issue
- 2
- Pages
- 183-201
- ISSN
- 0924-8463 (ISSN-L)
- 1572-8382
- OpenAlex
- W2392403817
- Cited by
- 125
- References
- 38
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 18 · 2023: 15 · 2022: 13 · 2021: 22 · 2020: 17 · 2019: 17 · 2018: 5 · 2017: 7
- FWCI
- 23.63
- Type
- journal article
- Language
- en
- Open access
- green
- papers.ssrn.com
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Artificial Intelligence and Law (publishedVersion)
- SSRN Electronic Journal (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Data Archiving and Networked Services (DANS) (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 › Digitalization, Law, and Regulation
- Keywords
- algorithmic discrimination, sensitive personal data, data-driven decision making, algorithmic fairness, prediction model, race, data privacy regulation, data minimization, data bias, data mining, personal data protection law
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