The DOI matches its Crossref record; 20 of the 21 references it cites were found in a registry or an index.
Fairness definitions explained
Abstract
Algorithm fairness has started to attract the attention of researchers in AI, Software Engineering and Law communities, with more than twenty different notions of fairness proposed in the last few years. Yet, there is no clear agreement on which definition to apply in each situation. Moreover, the detailed differences between multiple definitions are difficult to grasp. To address this issue, this paper collects the most prominent definitions of fairness for the algorithmic classification problem, explains the rationale behind these definitions, and demonstrates each of them on a single unifying case-study. Our analysis intuitively explains why the same case can be considered fair according to some definitions and unfair according to others.
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In the citing paper
“Parity on the different metrics across disaggregated population subgroups corresponds to how fairness is often defined [37, 48].”
From Model Cards for Model Reporting (2019) · cites this paper as [48]
In the citing paper
“While similar concepts have been operationalized for machine learning classification tasks (Mehrabi et al. 2021; Verma and Rubin 2018), less has been done in the NLP space, which may contain more ambiguity than classification for tasks like language generation.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Verma and Rubin 2018
In the citing paper
“To combat bias, this paper has highlighted strategies such as robust data augmentation, the application of counterfactual fairness, and the imperative for diverse, representative datasets alongside unbiased data collection methods [49][50][51].”
From Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies (2023) · cites this paper as [51]
In the citing paper
“As we discussed in §2.3 scholars have proposed a number of competing ways to quantify fairness [46,75,93,148], some of which are mathematically incompatible with each other [26,51,80].”
From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [148]
In the citing paper
“As a result, a classifier trained without regard to the protected attribute not only fails to remove discrimination, but it can complicate the detection and mitigation of discrimination downstream via in-processing or post-processing techniques [9,10,18,24,25,34,43,51], such as those we describe next.”
From Interventional Fairness: Causal Database Repair for Algorithmic Fairness (2019) · cites this paper as [51]
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“However, it was shown that statistical definitions are insufficient [8, 10, 12, 16].”
BackgroundMethodInfluentialInherent trade-offs in the fair determination of risk scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017) · Schloss Dagstuhl – Leibniz-Zentrum für Informatik · doi:10.4230/lipics.itcs.2017.43 · Cited by 943 · cited 7×
“Well-calibration [16].”
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Metadata
- Authors
- Verma, S., & Rubin, J.
- Affiliations
- Indian Institute of Technology Kanpur (IN)
- University of British Columbia (CA)
- Venue
- Proceedings of the International Workshop on Software Fairness
- Publisher
- ACM
- Published
- 2018-05-29
- Pages
- 1-7
- OpenAlex
- W2809878087
- Cited by
- 1,124
- References
- 21
- Citations by year
- 2026: 102 · 2025: 157 · 2024: 188 · 2023: 197 · 2022: 168 · 2021: 170 · 2020: 103 · 2019: 38 · 2018: 1
- FWCI
- 140.65
- Type
- conference paper
- Language
- en
- Open access
- closed
- 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 fairness, algorithmic classification, fairness metrics
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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.
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