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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
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
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190
CAUSALITY: MODELS, REASONING AND INFERENCE
Hitchcock, C., & Pearl, J. (2001) · The Philosophical Review · doi:10.2307/3182612 · Cited by 4,827
Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012) · Proceedings of the 3rd Innovations in Theoretical Computer Science Conference · doi:10.1145/2090236.2090255 · Cited by 3,501
Causality: models, reasoning, and inference
(2000) · Choice Reviews Online · doi:10.5860/choice.38-1494a · Cited by 2,968
Equality of Opportunity in Supervised Learning
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
Kusner, M. J., Loftus, J. R., Russell, C., & Silva, R. (2017) · arXiv · doi:10.48550/arxiv.1703.06856 · Cited by 995
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
Research works printed but not linked: 2
- 2016. Machine Bias. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. (May 2016 ). Online ; accessed February 2018. 2016. Machine Bias. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing. (May 2016). Online; accessed February 2018.
- 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
Machine Learning Testing: Survey, Landscapes and Horizons
Zhang, J. M., Harman, M., Ma, L., & Liu, Y. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2019.2962027 · Cited by 897
Ferrara, E. (2023) · Sci · doi:10.3390/sci6010003 · Cited by 875
“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 [49–51].”
MethodAlgorithmic bias: review, synthesis, and future research directions
Kordzadeh, N., & Ghasemaghaei, M. (2022) · European Journal of Information Systems · doi:10.1080/0960085x.2021.1927212 · Cited by 770
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763
“Researchers have also developed a growing body of evaluation metrics for measuring fairness (Dwork et al., 2012; Kearns et al., 2017; Heidari et al., 2018; Verma and Rubin, 2018).”
BackgroundAldoseri, A., Al‐Khalifa, K. N., & Hamouda, A. M. S. (2023) · Applied Sciences · doi:10.3390/app13127082 · Cited by 908
Algorithmic Fairness: Choices, Assumptions, and Definitions
Mitchell, S., Potash, E., Barocas, S., D'Amour, A., & Lum, K. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042720-125902 · Cited by 687
Trustworthy Artificial Intelligence: A Review
Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2023) · ACM Computing Surveys · doi:10.1145/3491209 · Cited by 709
Bias and Fairness in Large Language Models: A Survey
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., & Ahmed, N. K. (2024) · Computational Linguistics · doi:10.1162/coli_a_00524 · Cited by 1,245
A Review on Fairness in Machine Learning
Pessach, D., & Shmueli, E. (2023) · ACM Computing Surveys · doi:10.1145/3494672 · Cited by 598
Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
“Fairness can be applicable at multiple granularities of system behavior [66, 242, 339].”
BackgroundMethodInfluentialKärkkäinen, K., & Joo, J. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00159 · Cited by 546
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646
Pros and cons of GAN evaluation measures: New developments
Borji, A. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103329 · Cited by 381
Accessing Artificial Intelligence for Clinical Decision-Making
Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.645232 · Cited by 326
Pagano, T. P., Loureiro, R. B., Lisboa, F. V. N., Peixoto, R. M., Guimarães, G. A. D. S., Cruz, G. O. R., Araujo, M. M., dos Santos, L. L., Cruz, M. A. S., Oliveira, E. L. S., et al. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010015 · Cited by 421
Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature
Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022) · Big Data & Society · doi:10.1177/20539517221115189 · Cited by 316
Bias and Discrimination in AI: A Cross-Disciplinary Perspective
Ferrer, X., Nuenen, T. V., Such, J. M., Cote, M., & Criado, N. (2021) · IEEE Technology and Society Magazine · doi:10.1109/mts.2021.3056293 · Cited by 308
“While reviews such as [16] offer an extensive description of some of these metrics, we will discuss the intuition behind the most common types of metrics used in the literature below.”
BackgroundFletcher, R. R., Nakeshimana, A., & Olubeko, O. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.561802 · Cited by 305
“While it is possible to define many different types of fairness criteria in terms of probability or statistics (Barocas et al., 2017; Kusner et al., 2017; Verma and Rubin, 2018), we describe here the three most common definitions for fairness:”
Algorithmic fairness in education
Kizilcec, R. F., & Lee, H. (2022) · Routledge eBooks · doi:10.4324/9780429329067-10 · Cited by 339
Oneto, L., & Chiappa, S. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-43883-8_7 · Cited by 327
Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K., Dickerson, J. P., & Shah, C. S. (2024) · ACM Computing Surveys · doi:10.1145/3677119 · Cited by 723
Self-citationCounterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
Verma, S., Boonsanong, V., Hoang, M., Hines, K. E., Dickerson, J. P., & Shah, C. (2020) · arXiv · doi:10.48550/arxiv.2010.10596 · Cited by 267
Self-citationA high-level overview of AI ethics
Kazim, E., & Koshiyama, A. S. (2021) · Patterns · doi:10.1016/j.patter.2021.100314 · Cited by 269
Fairness in recommender systems: research landscape and future directions
Deldjoo, Y., Jannach, D., Bellogín, A., Difonzo, A., & Zanzonelli, D. (2023) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-023-09364-z · Cited by 290
A clarification of the nuances in the fairness metrics landscape
Castelnovo, A., Crupi, R., Greco, G., Regoli, D., Penco, I. G., & Cosentini, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-07939-1 · Cited by 292
Machine learning and algorithmic fairness in public and population health
Mhasawade, V., Zhao, Y., & Chunara, R. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00373-4 · Cited by 233
Counterfactual Explanations for Machine Learning: A Review.Preprint
Verma, S., Dickerson, J. P., & Hines, K. (2020) · arXiv (Cornell University) · Cited by 223
Self-citationA Survey on Adversarial Recommender Systems
Deldjoo, Y., Noia, T. D., & Merra, F. A. (2021) · ACM Computing Surveys · doi:10.1145/3439729 · Cited by 244
Sánchez-Monedero, J., Dencik, L., & Edwards, L. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372849 · Cited by 196
Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review
Huang, J., Galal, G., Etemadi, M., & Vaidyanathan, M. (2022) · JMIR Medical Informatics · doi:10.2196/36388 · Cited by 186
Discrimination, Bias, Fairness, and Trustworthy AI
Varona, D., & Suárez, J. L. (2022) · Applied Sciences · doi:10.3390/app12125826 · Cited by 223
Investigating Bias and Fairness in Facial Expression Recognition
Tian, X., White, J. C., Kalkan, S., & Güneş, H. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-65414-6_35 · Cited by 174
Artificial Intelligence Governance For Businesses
Schneider, J., Abraham, R., Meske, C., & Brocke, J. V. (2022) · Information Systems Management · doi:10.1080/10580530.2022.2085825 · Cited by 273
A review of predictive policing from the perspective of fairness
Alikhademi, K., Drobina, E., Prioleau, D., Richardson, B., Purves, D., & Gilbert, J. E. (2022) · Artificial Intelligence and Law · doi:10.1007/s10506-021-09286-4 · Cited by 167
A sociotechnical view of algorithmic fairness
Dolata, M., Feuerriegel, S., & Schwabe, G. (2021) · Information Systems Journal · doi:10.1111/isj.12370 · Cited by 210
Algorithmic fairness in computational medicine
Xu, J., Xiao, Y., Wang, W. H., Ning, Y., Shenkman, E. A., Bian, J., & Wang, F. (2022) · eBioMedicine · doi:10.1016/j.ebiom.2022.104250 · Cited by 170
Marcinkowski, F., Kieslich, K., Starke, C., & Lünich, M. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372867 · Cited by 159
Understanding Human-Centred AI: a review of its defining elements and a research agenda
Schmager, S., Pappas, I. O., & Vassilakopoulou, P. (2025) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2448719 · Cited by 215
AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications
Chen, P., Wu, L., & Wang, L. (2023) · Applied Sciences · doi:10.3390/app131810258 · Cited by 155
Kontokosta, C. E., & Hong, B. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102503 · Cited by 174
Interventional Fairness: Causal Database Repair for Algorithmic Fairness
Salimi, B., Rodriguez, L., Howe, B., & Suciu, D. (2019) · Proceedings of the 2019 International Conference on Management of Data · doi:10.1145/3299869.3319901 · Cited by 145
Training confounder-free deep learning models for medical applications
Zhao, Q., Adeli, E., & Pohl, K. M. (2020) · Nature Communications · doi:10.1038/s41467-020-19784-9 · Cited by 160
Fairness, Equality, and Power in Algorithmic Decision-Making
Kasy, M., & Abebe, R. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445919 · Cited by 170
The Moral Limits of Predictive Practices: The Case of Credit-Based Insurance Scores
Kiviat, B. (2019) · American Sociological Review · doi:10.1177/0003122419884917 · Cited by 147
Trustworthy Graph Neural Networks: Aspects, Methods, and Trends
Zhang, H., Wu, B. Y., Yuan, X., Pan, S., Tong, H., & Pei, J. (2024) · Proceedings of the IEEE · doi:10.1109/jproc.2024.3369017 · Cited by 205
Understanding artificial intelligence ethics and safetyPreprint
Leslie, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05684 · Cited by 308
Drukker, K., Chen, W., Gichoya, J. W., Gruszauskas, N. P., Kalpathy-Cramer, J., Koyejo, S., Myers, K. J., Sá, R. C., Sahiner, B., Whitney, H. M., et al. (2023) · Journal of Medical Imaging · doi:10.1117/1.jmi.10.6.061104 · Cited by 149
Ethical Considerations in AI-Based Recruitment
Mujtaba, D. F., & Mahapatra, N. R. (2019) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas48451.2019.8937920 · Cited by 178
Fairness in rankings and recommendations: an overview
Pitoura, E., Stefanidis, K., & Koutrika, G. (2021) · The VLDB Journal · doi:10.1007/s00778-021-00697-y · Cited by 177
Understanding and Mitigating Bias in Imaging Artificial Intelligence
Tejani, A. S., Ng, Y. S., Xi, Y., & Rayan, J. C. (2024) · RadioGraphics · doi:10.1148/rg.230067 · Cited by 138
A survey on bias in visual datasets
Fabbrizzi, S., Papadopoulos, S., Ntoutsi, E., & Kompatsiaris, I. Y. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103552 · Cited by 164
Operationalising AI Governance through Ethics-Based Auditing: An Industry Case Study
Mökander, J., & Floridi, L. (2022) · AI and Ethics · doi:10.1007/s43681-022-00171-7 · Cited by 187
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646
FairFace: Face Attribute Dataset for Balanced Race, Gender, and AgePreprint
Kärkkäinen, K., & Joo, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.04913 · Cited by 129
The Butterfly Effect in artificial intelligence systems: Implications for AI bias and fairness
Ferrara, E. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100525 · Cited by 178
Operational Research: methods and applications
Petropoulos, F., Gilbert Laporte, A., Aktaş, E., Alumur, S. A., Archetti, C., Ayhan, H., Battarra, M., Bennell, J. A., Bourjolly, J.-M., Boylan, J. E., et al. (2023) · Journal of the Operational Research Society · doi:10.1080/01605682.2023.2253852 · Cited by 161
A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
Kaluarachchi, T. I., Reis, A., & Nanayakkara, S. C. (2021) · Sensors · doi:10.3390/s21072514 · Cited by 135
Vale, D., El-Sharif, A., & Ali, M. (2022) · AI and Ethics · doi:10.1007/s43681-022-00142-y · Cited by 124
Garrido-Muñoz, I., Montejo-Ráez, A., Martínez-Santiago, F., & Ureña-López, L. A. (2021) · Applied Sciences · doi:10.3390/app11073184 · Cited by 123
A guide to formulating fairness in an optimization model
Chen, V. X., & Hooker, J. (2023) · Annals of Operations Research · doi:10.1007/s10479-023-05264-y · Cited by 139
The Landscape and Gaps in Open Source Fairness Toolkits
Lee, M. S. A., & Singh, J. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445261 · Cited by 129
Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression
Park, Y., Hu, J., Singh, M., Sylla, I., Dankwa‐Mullan, I., Koski, E., & Das, A. K. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.3909 · Cited by 116
InFoRM: Individual Fairness on Graph Mining
Kang, J. L., He, J., Maciejewski, R., & Tong, H. (2020) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3394486.3403080 · Cited by 113
Xie, J., Sun, L., & Zhao, Y. F. (2025) · Engineering · doi:10.1016/j.eng.2024.04.024 · Cited by 112
Laux, J. M., Wachter, S., & Mittelstadt, B. (2024) · Computer law & security review · doi:10.1016/j.clsr.2024.105957 · Cited by 113
Algorithmic Political Bias in Artificial Intelligence Systems
Peters, U. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00512-8 · Cited by 111
This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology
Mulligan, D. K., Kroll, J. A., Kohli, N., & Wong, R. Y. (2019) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3359221 · Cited by 111
Fairness and accountability of AI in disaster risk management: Opportunities and challenges
Gevaert, C. M., Carman, M., Rosman, B., Georgiadou, Y., & Soden, R. (2021) · Patterns · doi:10.1016/j.patter.2021.100363 · Cited by 127
Policy advice and best practices on bias and fairness in AI
Álvarez, J. M., Colmenarejo, A. B., Elobaid, A., Fabbrizzi, S., Fahimi, M., Ferrara, A., Ghodsi, S., Mougan, C., Papageorgiou, I. T., Lobo, P. R., et al. (2024) · Ethics and Information Technology · doi:10.1007/s10676-024-09746-w · Cited by 147
Algorithmic Fairness in Mortgage Lending: from Absolute Conditions to Relational Trade-offs
Lee, M. S. A., & Floridi, L. (2021) · Minds and Machines · doi:10.1007/s11023-020-09529-4 · Cited by 106
Privacy and Fairness in Federated Learning: On the Perspective of Tradeoff
Chen, H., Zhu, T., Zhang, T., Zhou, W., & Yu, P. S. (2023) · ACM Computing Surveys · doi:10.1145/3606017 · Cited by 118
Pessach, D., & Shmueli, E. (2023) · Springer eBooks · doi:10.1007/978-3-031-24628-9_37 · Cited by 235
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
Wang, C., Han, B., Patel, B., & Rudin, C. (2023) · Journal of Quantitative Criminology · doi:10.1007/s10940-022-09545-w · Cited by 101
Should College Dropout Prediction Models Include Protected Attributes?
Yu, R., Lee, H., & Kizilcec, R. F. (2021) · ACM Conference on Learning @ Scale (L@S) · doi:10.1145/3430895.3460139 · Cited by 101
Fairness Testing: A Comprehensive Survey and Analysis of Trends
Chen, Z., Zhang, J. M., Hort, M., Harman, M., & Sarro, F. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3652155 · Cited by 152
Balayn, A., Lofi, C., & Houben, G.-J. (2021) · The VLDB Journal · doi:10.1007/s00778-021-00671-8 · Cited by 98
Fairness Metrics: A Comparative Analysis
Garg, P., Villasenor, J. D., & Foggo, V. (2020) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata50022.2020.9378025 · Cited by 113
Papakyriakopoulos, O., Hegelich, S., Serrano, J. C. M., & Marco, F. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372843 · Cited by 95
Tackling COVID-19 Through Responsible AI Innovation: Five Steps in the Right Direction
Leslie, D. (2020) · Harvard Data Science Review · doi:10.1162/99608f92.4bb9d7a7 · Cited by 103
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
Kallus, N., Mao, X., & Zhou, A. (2022) · Management Science · doi:10.1287/mnsc.2020.3850 · Cited by 93
Fair decision making using privacy-protected data
Pujol, D., McKenna, R., Kuppam, S., Hay, M., Machanavajjhala, A., & Miklau, G. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372872 · Cited by 111
FAHT: An Adaptive Fairness-aware Decision Tree Classifier
Zhang, W., & Ntoutsi, E. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/205 · Cited by 85
Algorithms for Ethical Decision-Making in the Clinic: A Proof of Concept
Meier, L. J., Hein, A., Diepold, K. J., & Buyx, A. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2022.2040647 · Cited by 98
Berman, A., Licht, K. D. F., & Carlsson, V. (2024) · Technology in Society · doi:10.1016/j.techsoc.2024.102471 · Cited by 137
Highly Accurate, But Still Discriminatory
Köchling, A., Riazy, S., Wehner, M., & Simbeck, K. (2020) · Business & Information Systems Engineering · doi:10.1007/s12599-020-00673-w · Cited by 107
Fairness Perceptions of Artificial Intelligence: A Review and Path Forward
Narayanan, D., Nagpal, M., McGuire, J., Schweitzer, S., & Cremer, D. D. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2210890 · Cited by 99
Public procurement of artificial intelligence systems: new risks and future proofing
Hickok, M. (2024) · AI & SOCIETY · doi:10.1007/s00146-022-01572-2 · Cited by 81
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in PythonPreprint
Baniecki, H., Kretowicz, W., Piątyszek, P., Wisniewski, J., & Biecek, P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14406 · Cited by 105
"Ignorance and Prejudice" in Software Fairness
Zhang, J. M., & Harman, M. (2021) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse43902.2021.00129 · Cited by 88
Learning Fair Node Representations with Graph Counterfactual Fairness
Ma, J., Guo, R., Wan, M., Yang, L., Zhang, A., & Li, J. (2022) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3488560.3498391 · Cited by 92
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Finocchiaro, J., Maio, R., Monachou, F., Patro, G. K., Raghavan, M., Stoica, A.-A., & Tsirtsis, S. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445912 · Cited by 89
Morse, L., Teodorescu, M. H., Awwad, Y., & Kane, G. C. (2021) · Journal of Business Ethics · doi:10.1007/s10551-021-04939-5 · Cited by 90
Petersen, E., Potdevin, Y., Mohammadi, E., Zidowitz, S., Breyer, S., Nowotka, D., Henn, S., Pechmann, L., Leucker, M., Rostalski, P., et al. (2022) · IEEE Access · doi:10.1109/access.2022.3178382 · Cited by 92
What Do People Really Want When They Say They Want "Explainable AI?" We Asked 60 Stakeholders.
Brennen, A. (2020) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3334480.3383047 · Cited by 76
A unifying and general account of fairness measurement in recommender systems
Amigó, E., Deldjoo, Y., Mizzaro, S., & Bellogín, A. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103115 · Cited by 81
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
