Measuring discrimination in algorithmic decision making
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Bias can be either positive or negative, whereas fairness is only concerned with negative bias or discrimination.
“Another difference is that bias can be either positive or negative, whereas fairness is only concerned with negative bias or discrimination [36].”
From Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023) · cites this paper as [36]
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References 54 · Cited by 272
- Authors
- Žliobaitė, I.
- Affiliations
- University of Helsinki (FI)
- Corresponding author
- Žliobaitė, Indrė
- ORCID
- Žliobaitė, Indrė
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2017-03-31
- Volume
- 31
- Issue
- 4
- Pages
- 1060-1089
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2597425331
- Cited by
- 272
- References
- 54
- Citations by year
- 2026: 22 · 2025: 35 · 2024: 48 · 2023: 36 · 2022: 37 · 2021: 34 · 2020: 23 · 2019: 30 · 2018: 5 · 2017: 1 · 2016: 1
- FWCI
- 37.66
- Type
- journal article
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Data Mining and Knowledge Discovery (publishedVersion)
- Työväentutkimus Vuosikirja (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Discrimination and Equality Law
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic discrimination, discrimination measures, algorithmic fairness, automated decision-making, predictive modeling, performance standards, fairness evaluation
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.
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