Fairness definitions explained

UCI Machine Learning Repository

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CAUSALITY: MODELS, REASONING AND INFERENCE

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Fairness through awareness

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Causality: models, reasoning, and inference

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Equality of Opportunity in Supervised Learning

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Fair prediction with disparate impact: a study of bias in recidivism prediction instruments

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Algorithmic Decision Making and the Cost of Fairness

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Counterfactual Fairness

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Fairness in Criminal Justice Risk Assessments: The State of the Art

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Inherent trade-offs in the fair determination of risk scores

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Longitudinal Fairness with Censorship

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Should learning analytics models include sensitive attributes? Explaining the why

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Predicting youth at high risk of aging out of foster care using machine learning methods

Ahn, E., Gil, Y., & Putnam‐Hornstein, E. (2021) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2021.105059 · Cited by 38

Fair Enough: Searching for Sufficient Measures of Fairness

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Health equity assessment of machine learning performance (HEAL): a framework and dermatology AI model case study

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Automatic Identification of Harmful, Aggressive, Abusive, and Offensive Language on the Web: A Survey of Technical Biases Informed by Psychology Literature

Balayn, A., Yang, J., Szlávik, Z., & Bozzon, A. (2021) · ACM Transactions on Social Computing · doi:10.1145/3479158 · Cited by 38

Algorithmic decision-making in financial services: economic and normative outcomes in consumer credit

Sargeant, H. (2022) · AI and Ethics · doi:10.1007/s43681-022-00236-7 · Cited by 53

ASTRAEA: Grammar-based Fairness Testing

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Differential Tweetment

Ball-Burack, A., Lee, M. S. A., Cobbe, J., & Singh, J. P. (2021) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3442188.3445875 · Cited by 38

Enter the metrics: critical theory and organizational operationalization of AI ethics

Krijger, J. (2021) · AI & Society · doi:10.1007/s00146-021-01256-3 · Cited by 44

Discover and Mitigate Unknown Biases with Debiasing Alternate Networks

Li, Z., Hoogs, A. J., & Xu, C. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19778-9_16 · Cited by 39

Evaluating Proposed Fairness Models for Face Recognition Algorithms

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Shortcut Learning Explanations for Deep Natural Language Processing: A Survey on Dataset Biases

Dogra, V., Verma, S., Kavita, K., Woźniak, M., Shafi, J., & Ijaz, M. F. (2024) · IEEE Access · doi:10.1109/access.2024.3360306 · Cited by 36

Owning Decisions: AI Decision-Support and the Attributability-Gap

Zeiser, J. (2024) · Science and Engineering Ethics · doi:10.1007/s11948-024-00485-1 · Cited by 52

Ethics in the Age of Algorithms: Unravelling the Impact of Algorithmic Unfairness on Data Analytics Recommendation Acceptance

Ghasemaghaei, M., & Kordzadeh, N. (2024) · Information Systems Journal · doi:10.1111/isj.12572 · Cited by 39

Testing Machine Learning Algorithms for Balanced Data Usage

Sharma, A., & Wehrheim, H. (2019) · IEEE International Conference on Software Testing, Verification and Validation (ICST) · doi:10.1109/icst.2019.00022 · Cited by 32

Towards automated verification of smart contract fairness

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When Fairness Meets Consistency in AHP Pairwise Comparisons

Dodevska, Z., Radovanović, S., Petrović, A., & Delibašić, B. (2023) · Mathematics · doi:10.3390/math11030604 · Cited by 37

Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service”

Lewicki, K., Lee, M. S. A., Cobbe, J., & Singh, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581463 · Cited by 30

PreCoF: counterfactual explanations for fairness

Goethals, S., Martens, D., & Calders, T. (2023) · Machine Learning · doi:10.1007/s10994-023-06319-8 · Cited by 38

Fairness for machine learning software in education: A systematic mapping study

Pham, N., Ngoc, H. P., & Nguyen‐Duc, A. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112244 · Cited by 48

Towards Formal Fairness in Machine Learning

Ignatiev, A., Cooper, M., Siala, M., Hébrard, E., & Marques-Silva, J. P. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58475-7_49 · Cited by 36

Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classification

Islam, M. T., Fariha, A., Meliou, A., & Salimi, B. (2022) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3514221.3517841 · Cited by 33

A Framework and Benchmarking Study for Counterfactual Generating Methods on Tabular Data

Oliveira, R., & Martens, D. (2021) · Applied Sciences · doi:10.3390/app11167274 · Cited by 36

On the Advantages of Distinguishing Between Predictive and Allocative Fairness in Algorithmic Decision-Making

Beigang, F. (2022) · Minds and Machines · doi:10.1007/s11023-022-09615-9 · Cited by 35

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective

Tang, Z., Zhang, J., & Zhang, K. (2023) · ACM Computing Surveys · doi:10.1145/3597199 · Cited by 42

Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images

Wang, R., Kuo, P., Chen, L.-C., Seastedt, K. P., Gichoya, J. W., & Celi, L. A. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105047 · Cited by 34

AI and discriminative decisions in recruitment: Challenging the core assumptions

Seppälä, P., & Małecka, M. (2024) · Big Data & Society · doi:10.1177/20539517241235872 · Cited by 85

OmniFair: A Declarative System for Model-Agnostic Group Fairness in Machine Learning

Zhang, H., Chu, X., Asudeh, A., & Navathe, S. B. (2021) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3448016.3452787 · Cited by 31

Mitigating Racial Biases in Toxic Language Detection with an Equity-Based Ensemble Framework

Halevy, M., Harris, C., Bruckman, A., Yang, D., & Howard, A. (2021) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3465416.3483299 · Cited by 36

Fairness-Enhancing Interventions in Stream Classification

Iosifidis, V., Tran, T. N. H., & Ntoutsi, E. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-27615-7_20 · Cited by 29

Dense neural networks in knee osteoarthritis classification: a study on accuracy and fairness

Moustakidis, S., Papandrianos, N. I., Christodolou, E., Papageorgiou, E. I., & Tsaopoulos, D. E. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05459-5 · Cited by 30

A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial NetworksPreprint

Deldjoo, Y., Noia, T. D., & Merra, F. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.10322 · Cited by 29

Integrating learners’ knowledge background to improve course recommendation fairness: A multi-graph recommendation method based on contrastive learning

Ma, W., Chen, W., Lu, L., & Fan, X. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103750 · Cited by 34

RAI Guidelines: Method for Generating Responsible AI Guidelines Grounded in Regulations and Usable by (Non-)Technical Roles

Constantinides, M., Bogucka, E. P., Quercia, D., Kallio, S., & Tahaei, M. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3686927 · Cited by 38

Critical Perspectives on Governance Mechanisms for AI/ML Systems

Stark, L., Greene, D., & Hoffmann, A. L. (2020) · Springer eBooks · doi:10.1007/978-3-030-56286-1_9 · Cited by 32

FairOD: Fairness-aware Outlier Detection

Shekhar, S., Shah, N., & Akoglu, L. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462517 · Cited by 30

Equalized odds postprocessing under imperfect group informationPreprint

Awasthi, P., Kleindeßner, M., & Morgenstern, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.03284 · Cited by 28

Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination

Kallus, N., Mao, X., & Zhou, A. (2019) · arXiv · doi:10.48550/arxiv.1906.00285 · Cited by 28

Whither bias goes, I will go: An integrative, systematic review of algorithmic bias mitigation.

Hickman, L., Huynh, C., Gass, J., Booth, B. M., Kuruzovich, J., & Tay, L. (2024) · Journal of Applied Psychology · doi:10.1037/apl0001255 · Cited by 36

Causal Multi-level Fairness

Mhasawade, V., & Chunara, R. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462587 · Cited by 30

Facing the Challenges of Developing Fair Risk Scoring Models

Szepannek, G., & Lübke, K. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.681915 · Cited by 30

An information theoretic approach to reducing algorithmic bias for machine learning

Kim, J.-Y., & Cho, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.09.081 · Cited by 31

Fairness and Risk: An Ethical Argument for a Group Fairness Definition Insurers Can Use

Baumann, J., & Loi, M. (2023) · Philosophy & Technology · doi:10.1007/s13347-023-00624-9 · Cited by 35

Exploring bias and prediction metrics to characterise the fairness of machine learning for equity-centered public health decision-making: a narrative review

Raza, S., Shaban-Nejad, A., Dolatabadi, E., & Mamiya, H. (2024) · IEEE Access · doi:10.1109/access.2024.3509353 · Cited by 25

The Fairness of Credit Scoring Models

Hurlin, C., Perignon, C., & Saurin, S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3785882 · Cited by 26

Can Synthetic Data be Fair and Private? A Comparative Study of Synthetic Data Generation and Fairness Algorithms

Liu, Q., Deho, O. B., Vadiee, F., Khalil, M., Joksimovíc, S., & Siemens, G. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706546 · Cited by 29

Fairness in the Eyes of the Data: Certifying Machine-Learning Models

Segal, S., Adi, Y., Benny Pinkas, B., Baum, C., Ganesh, C., & Keshet, J. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462554 · Cited by 31

Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics

Draws, T., Tintarev, N., Gadiraju, U., Bozzon, A., & Timmermans, B. (2020) · SIGKDD Explorations · doi:10.1145/3468507.3468515 · Cited by 36

The agency of computer vision models as optical instruments

Smits, T., & Wevers, M. (2021) · Visual Communication · doi:10.1177/1470357221992097 · Cited by 29

Ethical Redress of Racial Inequities in AI: Lessons from Decoupling Machine Learning from Optimization in Medical Appointment Scheduling

Shanklin, R., Samorani, M., Harris, S. L., & Santoro, M. A. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00590-8 · Cited by 34

There is an elephant in the room: towards a critique on the use of fairness in biometrics

Valdivia, A., Corbera-Serrajòrdia, J., & Swianiewicz, A. (2022) · AI and Ethics · doi:10.1007/s43681-022-00249-2 · Cited by 30

Politik in der digitalen Gesellschaft

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Fair and Transparent Student Admission Prediction Using Machine Learning Models

Raftopoulos, G., Davrazos, G., & Kotsiantis, S. B. (2024) · Algorithms · doi:10.3390/a17120572 · Cited by 32

Computational pathology: A comprehensive review of recent developments in digital and intelligent pathology

Huang, Q., Wu, S., Ou, Z., & Gao, Y. (2025) · Intelligent Oncology · doi:10.1016/j.intonc.2025.03.004 · Cited by 29

Fairness by Explicability and Adversarial SHAP Learning

Hickey, J. M., Stefano, P. G. D., & Vasileiou, V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67664-3_11 · Cited by 30

Reflexive Design for Fairness and Other Human Values in Formal Models

Fish, B., & Stark, L. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462518 · Cited by 32

Deep fair models for complex data: Graphs labeling and explainable face recognition

Franco, D., Navarin, N., Donini, M., Anguita, D., & Oneto, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.109 · Cited by 26

Toward Learning Trustworthily from Data Combining Privacy, Fairness, and Explainability: An Application to Face Recognition

Franco, D., Oneto, L., Navarin, N., & Anguita, D. (2021) · Entropy · doi:10.3390/e23081047 · Cited by 28

Fair Voice Biometrics: Impact of Demographic Imbalance on Group Fairness in Speaker Recognition

Fenu, G., Marras, M., Medda, G., & Meloni, G. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1857 · Cited by 25

Fair Decision-making Under Uncertainty

Zhang, W., & Weiss, J. C. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00100 · Cited by 29

Fair detection of poisoning attacks in federated learning on non-i.i.d. data

Singh, A., Blanco-Justicia, A., & Domingo-Ferrer, J. (2023) · Data mining and knowledge discovery · doi:10.1007/s10618-022-00912-6 · Cited by 30

Towards fairness-aware multi-objective optimization

Yu, G., Ma, L., Wang, X., Du, W., Du, W., & Jin, Y. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01668-w · Cited by 32

FACT: A Diagnostic for Group Fairness Trade-offsPreprint

Kim, J. S., Chen, J., & Talwalkar, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.03424 · Cited by 32

Auditing the fairness of place-based crime prediction models implemented with deep learning approaches

Wu, J., Abrar, S. M., Awasthi, N., & Frías-Martínez, V. (2023) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2023.101967 · Cited by 23

Domain adaptation for bias mitigation in affective computing: use cases for facial emotion recognition and sentiment analysis systems

Singhal, P., Gokhale, S., Shah, A., Jain, D. K., Walambe, R. A., Ekárt, A., & Kotecha, K. V. (2025) · Discover Applied Sciences · doi:10.1007/s42452-025-06659-1 · Cited by 26

The Invisible Arms Race: Digital Trends in Illicit Goods Trafficking and AI-Enabled Responses

Mademlis, I., Mancuso, M., Paternoster, C., Evangelatos, S., Finlay, E., Hughes, J. G., Grammatikis, P. I. R., Sarigiannidis, P. G., Stavropoulos, G., Votis, K., et al. (2024) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2024.3514683 · Cited by 24

Towards a Better Understanding of Evaluating Trustworthiness in AI Systems

Kemmerzell, N., Schreiner, A., Khalid, H., Schalk, M., & Bordoli, L. (2025) · ACM Computing Surveys · doi:10.1145/3721976 · Cited by 34

Fairness through Equality of Effort

Huan, W., Wu, Y., Zhang, L., & Wu, X. (2020) · International World Wide Web Conference (WWW) · doi:10.1145/3366424.3383558 · Cited by 23

Quality Management of Machine Learning Systems

Santhanam, P. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-62144-5_1 · Cited by 29

Legal perspective on possible fairness measures – A legal discussion using the example of hiring decisions

Hauer, M. P., Kevekordes, J., & Haeri, M. A. (2021) · Computer law & security review · doi:10.1016/j.clsr.2021.105583 · Cited by 28

Integrating Psychometrics and Computing Perspectives on Bias and Fairness in Affective Computing: A case study of automated video interviews

Booth, B. M., Hickman, L., Subburaj, S. K., Tay, L., Woo, S. E., & D’Mello, S. K. (2021) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3106615 · Cited by 24

Counterfactually Fair Automatic Speech Recognition

Sar, L., Hasegawa‐Johnson, M., & Yoo, C. D. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3126949 · Cited by 26

Fairness in Agreement With European Values

Colmenarejo, A. B., Nannini, L., Rieger, A., Scott, K. M., Zhao, X., Patro, G. K., Kasneci, G., & Kinder-Kurlanda, K. E. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534158 · Cited by 35

IF-City: Intelligible Fair City Planning to Measure, Explain and Mitigate Inequality

Lyu, Y., Lu, H., Lee, M. K., Schmitt, G., & Lim, B. Y. (2023) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2023.3239909 · Cited by 24

Ethical Considerations and Fairness in the Use of Artificial Intelligence for Neuroradiology

Filippi, C. G., Stein, J. M., Wang, Z., Bakas, S., Liu, Y., Chang, P., Lui, Y. W., Hess, C. P., Barboriak, D. P., Flanders, A. E., et al. (2023) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7963 · Cited by 32

Predictive algorithms in the delivery of public employment services

Körtner, J., & Bonoli, G. (2023) · Edward Elgar Publishing eBooks · doi:10.4337/9781800880887.00037 · Cited by 30

Using Artificial Intelligence in the workplace

Pero, A. S. D., Wyckoff, P., & Vourc'h, A. (2022) · OECD social employment and migration working papers · doi:10.1787/840a2d9f-en · Cited by 42

On the relation of causality- versus correlation-based feature selection on model fairness

Saarela, M. (2024) · ACM Symposium on Applied Computing · doi:10.1145/3605098.3636018 · Cited by 22

CFARnet: Deep learning for target detection with constant false alarm rate

Diskin, T., Beer, Y., Okun, U., & Wiesel, A. (2024) · Signal Processing · doi:10.1016/j.sigpro.2024.109543 · Cited by 22

Social-minded Measures of Data Quality

Pitoura, E. (2020) · Journal of Data and Information Quality · doi:10.1145/3404193 · Cited by 26

FairTrade: Achieving Pareto-Optimal Trade-Offs between Balanced Accuracy and Fairness in Federated Learning

Badar, M., Sikdar, S., Nejdl, W., & Fisichella, M. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i10.28971 · Cited by 22

Counterfactual Explanations for Machine Learning: Challenges RevisitedPreprint

Verma, S., Dickerson, J. P., & Hines, K. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.07756 · Cited by 51

Adaptive Sampling Strategies to Construct Equitable Training Datasets

Cai, W., Encarnación, R., Chern, B., Corbett‐Davies, S., Bogen, M., Bergman, S., & Goel, S. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533203 · Cited by 21

Examining and mitigating gender bias in text emotion detection task

Odbal, O., Zhang, G., & Ananiadou, S. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.057 · Cited by 21

Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups

Ramachandran, S., & Rattani, A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-37731-0_40 · Cited by 21

Fairness Without Demographic Data: A Survey of Approaches

Ashurst, C., & Weller, A. (2023) · Equity and Access in Algorithms, Mechanisms, and Optimization · doi:10.1145/3617694.3623234 · Cited by 21

Science and values: a two-way direction

Ratti, E., & Federica Russo, B. (2024) · European Journal for Philosophy of Science · doi:10.1007/s13194-024-00567-8 · Cited by 22

Triangular Trade-off between Robustness, Accuracy, and Fairness in Deep Neural Networks: A Survey

Li, J., & Li, G. (2024) · ACM Computing Surveys · doi:10.1145/3645088 · Cited by 30

The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africa

Asiedu, M., Dieng, A., Haykel, I., Rostamzadeh, N., Pfohl, S., Nagpal, C., Nagawa, M., Oppong, A., Koyejo, S., & Heller, K. (2024) · ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO) · doi:10.1145/3689904.3694708 · Cited by 31

Employees’ perceptions of the fairness of AI-based performance prediction features

Majrashi, K. (2025) · Cogent Business & Management · doi:10.1080/23311975.2025.2456111 · Cited by 28

Learning Fair and Transferable Representations with Theoretical Guarantees

Oneto, L., Donini, M., Pontil, M., & Maurer, A. (2020) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa49011.2020.00015 · Cited by 20

Risk Identification Questionnaire for Detecting Unintended Bias in the Machine Learning Development Lifecycle

Lee, M. S. A., & Singh, J. P. (2021) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3461702.3462572 · Cited by 20

Using Edge Cases to Disentangle Fairness and Solidarity in AI Ethics

Brusseau, J. (2021) · PhilPapers (PhilPapers Foundation) · Cited by 20

Fair classification by loss balancing via fairness-aware batch sampling

Kim, D., Park, S., Hwang, S., & Byun, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.11.018 · Cited by 20

Visual Auditor: Interactive Visualization for Detection and Summarization of Model Biases

Munechika, D., Wang, Z. J., Reidy, J., Rubin, J., Gade, K., Kenthapadi, K., & Chau, D. H. (2022) · Visualization · doi:10.1109/vis54862.2022.00018 · Cited by 20

Integrating Fairness in the Software Design Process: An Interview Study With HCI and ML Experts

Ryan, S., Nadal, C., & Doherty, G. J. (2023) · IEEE Access · doi:10.1109/access.2023.3260639 · Cited by 24

Preventing Discriminatory Decision-making in Evolving Data Streams

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