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Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models

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Žliobaitė, Indrė, Custers, Bart·2016·Artificial Intelligence and Law

DOI 10.1007/s10506-016-9182-5

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Authors: Žliobaitė, I., & Custers, B.
Affiliations: University of Helsinki (FI)
Leiden University (NL)
Corresponding author: Žliobaitė, Indrė
ORCID: Žliobaitė, Indrė
Custers, Bart
Venue: Artificial Intelligence and Law
Publisher: Springer Science and Business Media LLC
Published: 2016-05-07
Volume: 24
Issue: 2
Pages: 183-201
ISSN: 0924-8463 (ISSN-L)
1572-8382
DOI: 10.1007/s10506-016-9182-5
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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