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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Each is checked against this paper’s own text: does it say what the citing paper says it does?
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]
Citations
Cites
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Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159
Algorithmic Decision Making and the Cost of Fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098095 · Cited by 1,149
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Fairness in Criminal Justice Risk Assessments: The State of the Art
Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2021) · Sociological Methods & Research · doi:10.1177/0049124118782533 · Cited by 988 · cited 7×
“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].”
BackgroundResultInfluential
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- Matt J. Kusner Joshua R. Loftus Chris Russell and Ricardo Silva. 2017. Counter-factual Fairness. In Advances in Neural Information Processing Systems. Matt J. Kusner Joshua R. Loftus Chris Russell and Ricardo Silva. 2017. Counter-factual Fairness. In Advances in Neural Information Processing Systems.
Cited by
Sorted by citations
A Survey on Bias and Fairness in Machine Learning
Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2022) · ACM Computing Surveys · doi:10.1145/3457607 · Cited by 5,812
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681
“FalseOmissionRate, which measure the fraction of negative (not smiling) and positive (smiling) predictions that are incorrectly predicted to be positive and negative, respectively, are also reported. [48] • Together, these four metrics provide values for different errors that can be calculated from the confusion matri…”
BackgroundInfluentialBias in data‐driven artificial intelligence systems—An introductory survey
Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., et al. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1356 · Cited by 1,043
Baker, R. S., & Hawn, A. (2022) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-021-00285-9 · Cited by 993
Seyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021) · Nature Medicine · doi:10.1038/s41591-021-01595-0 · Cited by 928
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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