Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence
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[114,211] investigated visual mechanisms to better display issues with data to users, yet these approaches generally lack support for novice users.
“[114,211] investigated visual mechanisms to better display łissuesž with data to users, yet these approaches generally lack support for novice users [24].”
From Fairness in machine learning: A survey (2024) · cites this paper as [24]
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- Authors
- Berendt, B., & Preibusch, S.
- Affiliations
- KU Leuven (BE)
- Microsoft Research (United Kingdom) (GB)
- Corresponding author
- Berendt, Bettina
- Venue
- Artificial Intelligence and Law
- Published
- 2014-01-10
- Volume
- 22
- Issue
- 2
- Pages
- 175-209
- ISSN
- 0924-8463 (ISSN-L)
- 1572-8382
- OpenAlex
- W2018633013
- Cited by
- 44
- References
- 50
- Citations by year
- 2026: 1 · 2025: 2 · 2023: 9 · 2022: 3 · 2021: 1 · 2020: 5 · 2019: 7 · 2018: 6 · 2017: 6 · 2016: 2 · 2015: 1 · 2014: 1
- FWCI
- 9.31
- Type
- journal article
- Language
- en
- Open access
- green
- lirias.kuleuven.be
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Artificial Intelligence and Law (publishedVersion)
- Lirias (submittedVersion, other-oa)
- Funders
- Fonds Wetenschappelijk Onderzoek
- Vlaamse regering
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- algorithmic discrimination, loan approval decisions, decision support, discrimination prevention, discrimination detection
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