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
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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]
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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 ›
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