The DOI matches its Crossref record; 53 of the 54 references it cites were found in a registry or an index.
Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction
Open accessAbstract
As algorithms are increasingly used to make important decisions that affect human lives, ranging from social benefit assignment to predicting risk of criminal recidivism, concerns have been raised about the fairness of algorithmic decision making. Most prior works on algorithmic fairness normatively prescribe how fair decisions ought to be made. In contrast, here, we descriptively survey users for how they perceive and reason about fairness in algorithmic decision making. A key contribution of this work is the framework we propose to understand why people perceive certain features as fair or unfair to be used in algorithms. Our framework identifies eight properties of features, such as relevance, volitionality and reliability, as latent considerations that inform people»s moral judgments about the fairness of feature use in decision-making algorithms. We validate our framework through a series of scenario-based surveys with 576 people. We find that, based on a person»s assessment of the eight latent properties of a feature in our exemplar scenario, we can accurately (> 85%) predict if the person will judge the use of the feature as fair. Our findings have important implications. At a high-level, we show that people»s unfairness concerns are multi-dimensional and argue that future studies need to address unfairness concerns beyond discrimination. At a low-level, we find considerable disagreements in people»s fairness judgments. We identify root causes of the disagreements, and note possible pathways to resolve them.
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Cites
Using thematic analysis in psychology
Braun, V., & Clarke, V. (2006) · Qualitative Research in Psychology · doi:10.1191/1478088706qp063oa · Cited by 194,414
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Model selection and multimodel inference: a practical information-theoretic approach.
Guthery, F. S., Burnham, K. P., & Anderson, D. R. (2003) · The Journal of Wildlife Management · doi:10.2307/3802723 · Cited by 41,495
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011) · Perspectives on Psychological Science · doi:10.1177/1745691610393980 · Cited by 9,996
Gigerenzer, G., & Gaissmaier, W. (2011) · Annual Review of Psychology · doi:10.1146/annurev-psych-120709-145346 · Cited by 4,037
Running experiments on Amazon Mechanical Turk
Paolacci, G., Chandler, J., & Ipeirotis, P. G. (2010) · Judgment and Decision Making · doi:10.1017/s1930297500002205 · Cited by 3,767
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
Flagged first, then by citations
1 of the 54 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Bernard Gert and Joshua Gert. 2017. The Definition of Morality. In The Stanford Encyclopedia of Philosophy, Edward N. Zalta (Ed.). Metaphysics Research Lab, Stanford University.
- Pew Research Center. 2016. 2016 Party Identification Detailed Tables. (2016). http: //www.people-press.org/2016/09/13/2016-party-identification-detailed-tables/
- Bilal Qureshi, Faisal Kamiran, Asim Karim, and Salvatore Ruggieri. 2016. Causal Discrimination Discovery Through Propensity Score Analysis. arXiv preprint arXiv:1608.03735 (2016). · arXiv
Cited by
Sorted by citations
Items 101–200 of 209
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Saxena, N. A. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3314314 · Cited by 21
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Explainable AI as evidence of fair decisions
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Lee, M. S. A., & Floridi, L. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3559407 · Cited by 13
Juijn, G., Stoimenova, N., Reis, J., & Nguyen, D. (2023) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3600211.3604677 · Cited by 13
Utz, S., Wolfers, L. N., & Göritz, A. S. (2021) · Human-Machine Communication · doi:10.30658/hmc.3.3 · Cited by 12
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Leben, D. (2025) · The MIT Press eBooks · doi:10.7551/mitpress/15740.001.0001 · Cited by 8
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Johnson, B., Bartola, J., Angell, R., Keith, K. A., Witty, S., Giguere, S., & Brun, Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09951 · Cited by 7
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Learning Paradigms for Hybrid Decision-Making Systems
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Rastegarpanah, B., Crovella, M. E., & Gummadi, K. P. (2020) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3386392.3399568 · Cited by 8
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Langer, M., Baum, K. L., & Schlicker, N. (2023) · doi:10.31234/osf.io/ke256 · Cited by 6
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Lees, A., Balashankar, A., Welty, C., & Subramanian, L. (2019) · arXiv (Cornell University) · Cited by 5
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Sarkar, P., & Liem, C. C. S. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637360 · Cited by 6
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Lloyd, H. R. (2024) · Philosophical Studies · doi:10.1007/s11098-024-02224-5 · Cited by 5
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Recki, L., Esau, M., Lawo, D., & Stevens, G. (2023) · Mensch und Computer (MuC) · doi:10.1145/3603555.3603562 · Cited by 4
Berg-Postweiler, J., Decker, M., & Leicht-Scholten, C. (2024) · Springer eBooks · doi:10.1007/978-3-031-54700-3_15 · Cited by 4
Schenk, P. O., & Kern, C. A. (2024) · AStA Wirtschafts- und Sozialstatistisches Archiv · doi:10.1007/s11943-024-00344-2 · Cited by 11
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Discrimination in Algorithmic Decision Making: From Principles to Measures and Mechanisms
Zafar, M. B. (2019) · MPG.PuRe (Max Planck Society) · doi:10.22028/d291-27726 · Cited by 4
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Morse, L., Teodorescu, M. H., Awwad, Y., & Kane, G. C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3690570 · Cited by 5
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Naudts, L. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3298847 · Cited by 4
Development and validation of a scale assessing perceived trustworthiness in large language modelsPreprint
Yankouskaya, A., Barajeeh, B., Babiker, A., Alshakhsi, S. A. A., Ma, Y. T., Ho, C. S. M., & Ali, R. (2025) · Research Square · doi:10.21203/rs.3.rs-7738215/v1 · Cited by 5
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Lyons, H., Velloso, E., & Miller, T. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449180 · Cited by 4
Time Can Invalidate Algorithmic Recourse
Toni, G. D., Teso, S., Lepri, B., & Passerini, A. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732008 · Cited by 13
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Olson, L., Fischer, R. A.-L., Kunneman, F., & Guzmán, E. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732183 · Cited by 3
Wang, A., Bai, X., Barocas, S., & Blodgett, S. L. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732046 · Cited by 10
Hellwig, P., & Maier, G. W. (2023) · Springer eBooks · doi:10.1007/978-3-031-26104-6_4 · Cited by 4
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Watkins, E. A. (2021) · IUI Workshops · Cited by 3
Hohma, E., Burnell, R., Corrigan, C. C., & Luetge, C. (2022) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2022.3211073 · Cited by 3
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Keller, B., Lünich, M., & Marcinkowski, F. (2022) · Advances in higher education and professional development book series · doi:10.4018/978-1-7998-9247-2.ch006 · Cited by 4
Gamified Text Testing for Sustainable Fairness
Takan, S., Ergün, D., & Katipoğlu, G. (2023) · Sustainability · doi:10.3390/su15032292 · Cited by 3
Langer, M., Hunsicker, T., Feldkamp, T., König, C. J., & Grgić-Hlača, N. (2021) · arXiv (Cornell University) · Cited by 3
Wang, B., Luo, L., & Wang, X. (2024) · Economics and Politics · doi:10.1111/ecpo.12325 · Cited by 2
Gajavalli, S. H., Koizumi, J., & Hasan, R. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791102 · Cited by 3
Rentsch, S. (2023) · Sozialwissenschaften und Berufspraxis · doi:10.1007/978-3-658-38268-1_2 · Cited by 2
Krysztoforska, M., Docherty, N., & Biega, A. J. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580717 · Cited by 2
How Does Predictive Information Affect Human Ethical Preferences?
Narayanan, S., Yu, G., Tang, W., Ho, C.-J., & Yin, M. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534165 · Cited by 2
Recki, L., Kroth, K. M., Krauß, V., Klöckner, L., Pakusch, C., Bossauer, P., Böhm, L., Peters, F., Stöbitsch, A., & Boden, A. (2024) · Springer eBooks · doi:10.1007/978-3-662-68706-2_5 · Cited by 2
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Chen, W., Sotiraki, K., Chang, I., Kantarcıoğlu, M., & Popa, R. A. (2021) · IACR Cryptology ePrint Archive · Cited by 2
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Meyer, A. P., Kim, Y.-S., D'Antoni, L., & Albarghouthi, A. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713524 · Cited by 1
Boden, A., Stevens, G., Recki, L., Bossauer, P., & Schreiber, D. (2024) · Springer eBooks · doi:10.1007/978-3-662-68706-2 · Cited by 2
Rada, C., Dina, M. R., & Lunga, R.-A. (2025) · Scientific Reports · doi:10.1038/s41598-025-03043-2 · Cited by 1
AI and Access to Justice: Digital Gap Debunked?
Zubir, A. H., & Azmi, I. M. (2025) · SpringerBriefs in applied sciences and technology · doi:10.1007/978-3-031-97931-6_16 · Cited by 2
AI Resource Allocation: On the Contribution of Distributive and Procedural Fairness
Esch, C., Werner, D., Gross, M. E., Kals, E., & Pfeuffer, C. U. (2025) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3765766.3765776 · Cited by 1
Bhargava, V., Kar, A., Gupta, S., Yadav, C., Singh, K., & Lata, P. (2026) · AI and Ethics · doi:10.1007/s43681-025-00946-8 · Cited by 1
Challenges of Applying Data-Driven Persona Development
Jansen, B. J. J., Salminen, J. O., Jung, S., & Guan, K. (2021) · Synthesis lectures on human-centered informatics · doi:10.1007/978-3-031-02231-9_6 · Cited by 1
Telukunta, M., & Nadendla, V. S. S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-74630-7_17 · Cited by 1
Tempo: Helping Data Scientists and Domain Experts Collaboratively Specify Predictive Modeling Tasks
Sivaraman, V., Vaishampayan, A., Li, X., Buck, B. R., Ma, Z., Boyce, R. D., & Perer, A. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713664 · Cited by 2
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Sengewald, J., Schlichter, A., Siepermann, M., & Lackes, R. (2025) · Lecture notes in information systems and organisation · doi:10.1007/978-3-031-80119-8_4 · Cited by 1
López, J. M. C., Escobar, B. R. P., Paredes, O. M. A., Coaquira, E. H. B., & Carita-Choquecahua, A. (2026) · Athenea · doi:10.47460/athenea.v7i23.128 · Cited by 1
Examination of Fairness in Scheduling Tasks with Heterogeneous Resources
Erdős, S., & Kővári, B. (2021) · International Conference on Soft Computing & Machine Intelligence (ISCMI) · doi:10.1109/iscmi53840.2021.9654963 · Cited by 1
Beyond traditional assumptions in fair machine learningPreprint
Kilbertus, N. (2020) · arXiv (Cornell University) · doi:10.17863/cam.59055 · Cited by 1
Mitigating Bias in Algorithmic Systems: A Fish-Eye View of Problems and Solutions Across Domains.Preprint
Orphanou, K., Otterbacher, J., Kleanthous, S., Batsuren, K., Giunchiglia, F., Bogina, V., Tal, A. S., Hartman, A., & Kuflik, T. (2021) · arXiv (Cornell University) · Cited by 1
A Scoping Review On The Impact Of Algorithm Bias On The Perceived FairnessPreprint
Saryazdi, A. H., & Mirhoseini, M. (2023) · Research Square · doi:10.21203/rs.3.rs-3428999/v1 · Cited by 1
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Kim, S., Oh, P., & Lee, J. (2024) · doi:10.31234/osf.io/zpqrt · Cited by 1
Unfairness towards subjective opinions in Machine LearningPreprint
Balayn, A., Bozzon, A., & Szlávik, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02455
Telukunta, M., & Nadendla, V. S. S. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.04383
Fairness Preferences, Actual and Hypothetical: A Study of Crowdworker IncentivesPreprint
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Non-Comparative Fairness for Human-Auditing and Its Relation to Traditional Fairness NotionsPreprint
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Anik, A. I., & Bunt, A. (2021) · IUI Workshops
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Christoforou, E., Orphanou, K., Nicolaou, N., Stavrakis, E., & Otterbacher, J. (2023) · International Conference on Behavioral, Economic, and Socio-Cultural Computing · doi:10.1109/besc59560.2023.10386355
Chen, J. (2024) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2024-2479 · Cited by 1
Professor GPT: Having a Large Language Model Write a Commentary on Freedom of AssemblyPreprint
Engel, C., & Kruse, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4994131
A Study on the Application of Data Mining-based Crime Prediction Models in Criminal Justice
Zhao, L. (2024) · Journal of Combinatorial Mathematics and Combinatorial Computing · doi:10.61091/jcmcc123-40
Ahrweiler, P., Abe, J., & Neumann, M. (2025) · Artificial intelligence, simulation and society · doi:10.1007/978-3-031-71678-2_1
(2025) · The MIT Press eBooks · doi:10.7551/mitpress/15740.003.0013
Leveraging Technology-Driven Nudging for Decision-Making in Criminal Justice - Expert Perspectives
Jayathilake, H. M., Asadi, A. R., Akinremi, T. P., Appiah, J. K., Ibitoye, O. O., & Said, H. (2025) · Issues in Informing Science and Information Technology · doi:10.28945/5508
Social Influence and the Perceived Fairness of Algorithmic Decisions: An Exploratory Study
Kleanthous, S., Ioannou, A., & Papandreou, F. (2025) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3708319.3733679
Fairness trade-offs in hiring: what people prefer and what engineers can build
Burda, P., & van Otterloo, S. (2026) · Conference on Human Centred Artificial Intelligence - Education and Practice (HCAIep) · doi:10.1145/3777490.3777496
Designing for Resistance: Analyzing Data Work Among Direct Service Providers
Head, C. D., Piper, A. M., & Mazmanian, M. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790611
Metadata
- Authors
- Grgic-Hlaca, N., Redmiles, E. M., Gummadi, K. P., & Weller, A.
- Affiliations
- Max Planck Institute for Software Systems (MPI-SWS) & Saarland University, Saarbrücken, Germany
- University of Maryland, College Park (US)
- Max Planck Institute for Software Systems (DE)
- University of Cambridge (GB)
- Venue
- Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW '18
- Publisher
- ACM Press
- Published
- 2018
- Pages
- 903-912
- OpenAlex
- W2964316623
- Cited by
- 207
- References
- 54
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 26 · 2023: 25 · 2022: 35 · 2021: 39 · 2020: 20 · 2019: 23 · 2018: 4
- FWCI
- 38.49
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- Funders
- National Science Foundation Graduate Research Fellowship Program (DGE 1322106)
- Leverhulme Trust via the CFI
- The Alan Turing Institute under EPSRC grant (EP/N510129/1 & TU/B/000074)
- David MacKay Newton research fellowship at Darwin College
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Life Sciences › Neuroscience › Cognitive Neuroscience › Psychology of Moral and Emotional Judgment
- Keywords
- algorithmic fairness, perceived fairness, algorithmic decision-making, feature relevance, volitionality, reliability, moral judgment, fairness judgments
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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