The Fairness of Credit Scoring Models
Open accessThe claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
AI fairness is applied with bias migration strategies to counteract bias in credit-scoring models and ensure equitable access to loans and credit opportunities for all individuals.
“To counteract bias in credit-scoring models and ensure equitable access to loans and credit opportunities for all individuals, AI fairness is applied with bias migration strategies [287,288].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [287]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,190 · see citing works
(2012) · Cited by 8,009 · see citing works
Explanation in artificial intelligence: Insights from the social sciences
Miller, T. (2019) · Artificial Intelligence · doi:10.1016/j.artint.2018.07.007 · Cited by 5,302 · see citing works
The Statistical Theory of Racism and Sexism.
Phelps, E. S. (1972) · American Economic Review · Cited by 3,305 · see citing works
The Economics of Discrimination
Leonard, R. C., & Becker, G. S. (1957) · The American Catholic Sociological Review · doi:10.2307/3708738 · Cited by 2,971 · see citing works
Equality of Opportunity in Supervised Learning
Hardt, M., Price, E., & Srebro, N. (2016) · arXiv · doi:10.48550/arxiv.1610.02413 · Cited by 2,536 · see citing works
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 · see citing works
Machine Learning: An Applied Econometric Approach
Mullainathan, S., & Spiess, J. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.87 · Cited by 1,989 · see citing works
Arrow, K. J. (2015) · Princeton University Press eBooks · doi:10.1515/9781400867066-003 · Cited by 1,869 · see citing works
Recursive partitioning for heterogeneous causal effects
Athey, S., & Imbens, G. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510489113 · Cited by 1,586 · see citing works
Data preprocessing techniques for classification without discrimination
Kamiran, F., & Calders, T. (2012) · Knowledge and Information Systems · doi:10.1007/s10115-011-0463-8 · Cited by 1,305 · see citing works
Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research
Lessmann, S., Baesens, B., Seow, H.-V., & Thomas, L. C. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2015.05.030 · Cited by 1,284 · see citing works
The Role of Technology in Mortgage Lending
Fuster, A., Plosser, M. C., Schnabl, P., & Vickery, J. I. (2019) · Review of Financial Studies · doi:10.1093/rfs/hhz018 · Cited by 1,180 · see citing works
Fairness definitions explained
Verma, S., & Rubin, J. (2018) · Proceedings of the International Workshop on Software Fairness · doi:10.1145/3194770.3194776 · Cited by 1,124 · see citing works
Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2018) · The Quarterly Journal of Economics · doi:10.1093/qje/qjx032 · Cited by 1,040 · see citing works
Mortgage Lending in Boston: Interpreting HMDA Data
Munnell, A. H., Tootell, G. M. B., Browne, L. E., & McEneaney, J. P. (1992) · American Economic Review · Cited by 996 · see citing works
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 · see citing works
Inherent 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 1× · see citing works
“…technique (1) is state-of-the-art and could be put in production by a lender, (2) is in 18 This result may be surprising at (cid:133)rst glance given the fact that the impossibility theorem of Kleinberg et al. (2017) states that no more than one fairness de(cid:133)nition can hold at the same time.”
BackgroundInherent Trade-Offs in the Fair Determination of Risk Scores
Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016) · arXiv · doi:10.48550/arxiv.1609.05807 · Cited by 941 · see citing works
On the Rise of FinTechs: Credit Scoring Using Digital Footprints
Berg, T., Burg, V., Gombović, A., & Puri, M. (2020) · The Review of Financial Studies · doi:10.1093/rfs/hhz099 · Cited by 919 · see citing works
On the Rise of FinTechs – Credit Scoring using Digital FootprintsPreprint
Berg, T., Burg, V., Gombović, A., & Puri, M. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3163781 · Cited by 896 · see citing works
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilović, A., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942287 · Cited by 884 · see citing works
Predictably Unequal? The Effects of Machine Learning on Credit Markets
Fuster, A., Goldsmith-Pinkham, P. S., Ramadorai, T., & Walther, A. (2017) · doi:10.2139/ssrn.3072038 · Cited by 769 · see citing works
Three naive Bayes approaches for discrimination-free classification.
Calders, T., & Verwer, S. (2010) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-010-0190-x · Cited by 764 · see citing works
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 · see citing works
Consumer-lending discrimination in the FinTech Era
Bartlett, R., Morse, A., Stanton, R., & Wallace, N. (2022) · Journal of Financial Economics · doi:10.1016/j.jfineco.2021.05.047 · Cited by 659 · see citing works
Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · see citing works
Predictably Unequal? The Effects of Machine Learning on Credit Markets
FUSTER, A., GOLDSMITH‐PINKHAM, P., RAMADORAI, T., & WALTHER, A. (2022) · The Journal of Finance · doi:10.1111/jofi.13090 · Cited by 548 · see citing works
Human Decisions and Machine Predictions
Kleinberg, J., Lakkaraju, H., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017) · doi:10.3386/w23180 · Cited by 545 · see citing works
On Fairness and CalibrationPreprint
Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J. M., & Weinberger, K. Q. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02012 · Cited by 509 · see citing works
Building Classifiers with Independency Constraints
Calders, T., Kamiran, F., & Pechenizkiy, M. (2009) · 2009 IEEE International Conference on Data Mining Workshops · doi:10.1109/icdmw.2009.83 · Cited by 468 · see citing works
Optimized Pre-Processing for Discrimination Prevention
Calmon, F. P., Wei, D., Vinzamuri, B., Ramamurthy, K. N., & Varshney, K. R. (2017) · neural information processing systems · Cited by 459 · see citing works
Learning Adversarially Fair and Transferable RepresentationsPreprint
Madras, D., Creager, E., Pitassi, T., & Zemel, R. S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.06309 · Cited by 418 · see citing works
Fairness testing: testing software for discrimination
Galhotra, S., Brun, Y., & Meliou, A. (2017) · Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering · doi:10.1145/3106237.3106277 · Cited by 405 · see citing works
FairGAN: Fairness-aware Generative Adversarial Networks
Xu, D., Yuan, S., Zhang, L., & Wu, X. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8622525 · Cited by 379 · see citing works
Avoiding Discrimination through Causal Reasoning
Kilbertus, N., Rojas-Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., & Schölkopf, B. (2017) · arXiv · doi:10.48550/arxiv.1706.02744 · Cited by 358 · see citing works
DO WOMEN PAY MORE FOR CREDIT? EVIDENCE FROM ITALY
Alesina, A., Lotti, F., & Mistrulli, P. E. (2013) · Journal of the European Economic Association · doi:10.1111/j.1542-4774.2012.01100.x · Cited by 334 · see citing works
Discrimination Aware Decision Tree Learning
Kamiran, F., Calders, T., & Pechenizkiy, M. (2010) · 2010 IEEE International Conference on Data Mining · doi:10.1109/icdm.2010.50 · Cited by 303 · see citing works
Decision Theory for Discrimination-Aware Classification
Kamiran, F., Karim, A., & Zhang, X. (2012) · 2012 IEEE 12th International Conference on Data Mining · doi:10.1109/icdm.2012.45 · Cited by 300 · see citing works
Information and Incentives Inside the Firm: Evidence from Loan Officer Rotation
Hertzberg, A., Liberti, J. M., & Paravisini, D. (2010) · The Journal of Finance · doi:10.1111/j.1540-6261.2010.01553.x · Cited by 288 · see citing works
Kleinberg, J., Ludwig, J., Mullainathan, S., & Rambachan, A. (2018) · AEA Papers and Proceedings · doi:10.1257/pandp.20181018 · Cited by 278 · see citing works
Qian, J. “., Strahan, P. E., & Yang, Z. (2015) · The Journal of Finance · doi:10.1111/jofi.12251 · Cited by 210 · see citing works
Fairness in credit scoring: Assessment, implementation and profit implications
Kozodoi, N., Jacob, J., & Lessmann, S. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2021.06.023 · Cited by 198 · see citing works
What Drives Racial and Ethnic Differences in High-Cost Mortgages? The Role of High-Risk Lenders
Bayer, P. J., Ferreira, F. V., & Ross, S. L. (2017) · Review of Financial Studies · doi:10.1093/rfs/hhx035 · Cited by 188 · see citing works
The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
Shah, R. D., & Peters, J. (2019) · Apollo (University of Cambridge) · doi:10.17863/cam.45267 · Cited by 176 · see citing works
The cost of fairness in binary classification
Menon, A. K., & Williamson, R. C. (2018) · Cited by 157 · see citing works
Identifying and Correcting Label Bias in Machine Learning
Jiang, H., & Nachum, O. (2019) · arXiv · doi:10.48550/arxiv.1901.04966 · Cited by 152 · see citing works
(2020) · Iowa law review · Cited by 151 · see citing works
Yang, J., Soltan, A. A. S., Eyre, D. W., & Clifton, D. A. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00697-3 · Cited by 147 · see citing works
Do Minorities Pay More for Mortgages?
Bhutta, N., & Hizmo, A. (2020) · Review of Financial Studies · doi:10.1093/rfs/hhaa047 · Cited by 144 · see citing works
The Role of Technology in Mortgage Lending
Fuster, A., Plosser, M. C., Schnabl, P., & Vickery, J. I. (2018) · National Bureau of Economic Research · doi:10.3386/w24500 · Cited by 142 · see citing works
Lakkaraju, H., Kleinberg, J., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098066 · Cited by 110 · see citing works
Gillis, T. B., & Spiess, J. (2019) · bepress Legal Repository · Cited by 108 · see citing works
On the Rise of FinTechs – Credit Scoring using Digital Footprints
Berg, T., Burg, V., Gombović, A., & Puri, M. (2018) · National Bureau of Economic Research · doi:10.3386/w24551 · Cited by 108 · see citing works
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 · see citing works
Measuring Bias in Consumer Lending
Dobbie, W., Liberman, A., Paravisini, D., & Pathania, V. S. (2021) · The Review of Economic Studies · doi:10.1093/restud/rdaa078 · Cited by 106 · see citing works
One-Network Adversarial Fairness
Adel, T., Valera, I., Ghahramani, Z., & Weller, A. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33012412 · Cited by 95 · see citing works
Racial Disparities in the Auto Loan Market
Butler, A. W., Mayer, E. J., & Weston, J. P. (2022) · Review of Financial Studies · doi:10.1093/rfs/hhac029 · Cited by 89 · see citing works
Implementing Anti-discrimination Policies in Statistical Profiling Models
Pope, D. G., & Sydnor, J. R. (2011) · American Economic Journal: Economic Policy · doi:10.1257/pol.3.3.206 · Cited by 87 · see citing works
Achieving Causal Fairness through Generative Adversarial Networks
Xu, D., Wu, Y., Yuan, S., Zhang, L., & Wu, X. (2019) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/201 · Cited by 76 · see citing works
Active Fairness in Algorithmic Decision Making
Noriega-Campero, A., Bakker, M. A., Bueno, B. G. B., & Pentland, A. (2019) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3306618.3314277 · Cited by 74 · see citing works
Lender Automation and Racial Disparities in Credit Access
Howell, S. T., Kuchler, T., Snitkof, D., Stroebel, J., & Wong, J. K. (2023) · The Journal of Finance · doi:10.1111/jofi.13303 · Cited by 74 · see citing works
Loan Officer Incentives, Internal Rating Models, and Default Rates
Berg, T., Puri, M., & Rocholl, J. (2019) · European Finance Review · doi:10.1093/rof/rfz018 · Cited by 55 · see citing works
What Drives Racial and Ethnic Differences in High Cost Mortgages? The Role of High Risk LendersPreprint
Bayer, P. J., Ferreira, F. V., & Ross, S. L. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2730894 · Cited by 54 · see citing works
FAE: A Fairness-Aware Ensemble Framework
Iosifidis, V., Fetahu, B., & Ntoutsi, E. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006487 · Cited by 54 · see citing works
Lender Automation and Racial Disparities in Credit Access
Howell, S. T., Kuchler, T., Snitkof, D., Stroebel, J., & Wong, J. K. (2021) · National Bureau of Economic Research · doi:10.3386/w29364 · Cited by 50 · see citing works
Cost-sensitive learning for profit-driven credit scoring
Petrides, G., Moldovan, D., Coenen, L., Guns, T., & Verbeke, W. (2020) · Journal of the Operational Research Society · doi:10.1080/01605682.2020.1843975 · Cited by 46 · see citing works
Learning Optimal Fair Policies
Nabi, R., Malinsky, D., & Shpitser, I. (2018) · PubMed · doi:10.48550/arxiv.1809.02244 · Cited by 43 · see citing works
Algorithmic fairness in credit scoring
Bono, T., Croxson, K., & Giles, A. (2021) · Oxford Review of Economic Policy · doi:10.1093/oxrep/grab020 · Cited by 43 · see citing works
FairGAN: Fairness-aware Generative Adversarial NetworksPreprint
Xu, D., Yuan, S., Zhang, L., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.11202 · Cited by 42 · see citing works
FAIR: Fair adversarial instance re-weighting
Petrović, A., Nikolić, M., Radovanović, S., Delibašić, B., & Jovanović, M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2021.12.082 · Cited by 40 · see citing works
Measuring Bias in Consumer Lending
Dobbie, W., Liberman, A., Paravisini, D., & Pathania, V. S. (2018) · National Bureau of Economic Research · doi:10.3386/w24953 · Cited by 35 · see citing works
The price of fairness - A framework to explore trade-offs in algorithmic fairness
Haas, C. S. (2019) · Journal of the Association for Information Systems · Cited by 33 · see citing works
The implicit fairness criterion of unconstrained learningPreprint
Liu, L. T., Simchowitz, M., & Hardt, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.10013 · Cited by 32 · see citing works
Martins, P., Oliveira, H. G., Goncalves, J. C., Cruz, A., Cardoso, A., Žnidaršič, M., Nada Lavrač, A., Linkola, S., Toivonen, H. T. T., Hervás, R., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2898417 · Cited by 30 · see citing works
Ensuring Fairness Beyond the Training DataPreprint
Mandal, D., Deng, S., Jana, S., Wing, J. M., & Hsu, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.06029 · Cited by 27 · see citing works
2009 2nd international conference on computer, control and communication
(2009) · International Conference on Computer, Control and Communication · doi:10.1109/ic4.2009.4909206 · Cited by 27 · see citing works
Characterizing Fairness Over the Set of Good Models Under Selective LabelsPreprint
Coston, A., Rambachan, A., & Chouldechova, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.00352 · Cited by 24 · see citing works
Generating Fair Universal Representations Using Adversarial Models
Kairouz, P., Liao, J., Huang, C. X., Vyas, M. R., Welfert, M., & Sankar, L. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3170265 · Cited by 24 · see citing works
Practical valid inferences for the two-sample binomial problem
Fay, M. P., & Hunsberger, S. (2021) · Statistics Surveys · doi:10.1214/21-ss131 · Cited by 21 · see citing works
Anatomy of a Fair Lending Exam: The Uses and Limitations of Statistics*
Calem, P. S., & Longhofer, S. D. (2002) · The Journal of Real Estate Finance and Economics · doi:10.1023/a:1015264813969 · Cited by 21 · see citing works
Enhancing the Accuracy and Fairness of Human Decision MakingPreprint
Valera, I., Singla, A. K., & Gomez-Rodriguez, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10318 · Cited by 20 · see citing works
Nabi, R., & Shpitser, I. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11553 · Cited by 20 · see citing works
Racial Disparities in the Paycheck Protection ProgramPreprint
Chernenko, S. V., & Scharfstein, D. S. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3907575 · Cited by 17 · see citing works
Racial disparities in the Paycheck Protection Program
Chernenko, S. V., & Scharfstein, D. S. (2024) · Journal of Financial Economics · doi:10.1016/j.jfineco.2024.103911 · Cited by 17 · see citing works
Path-Specific Counterfactual Fairness
Chiappa, S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33017801 · Cited by 16 · see citing works
Statistical Models for Proportions and Probabilities
Seber, G. A. F. (2013) · SpringerBriefs in statistics · doi:10.1007/978-3-642-39041-8 · Cited by 15 · see citing works
Do Minorities Pay More for Mortgages?
Bhutta, N., & Hizmo, A. (2020) · Finance and Economics Discussion Series · doi:10.17016/feds.2020.007 · Cited by 15 · see citing works
Technological Innovation and Discrimination in Household Finance
Morse, A., & Pence, K. M. (2021) · Springer eBooks · doi:10.1007/978-3-030-65117-6_28 · Cited by 15 · see citing works
How Costly are Cultural Biases?Preprint
D’Acunto, F., Ghosh, P., Jain, R., & Rossi, A. G. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3736117 · Cited by 14 · see citing works
Algorithmic Fairness in Mortgage Lending: From Absolute Conditions to Relational Trade-OffsPreprint
Lee, M. S. A., & Floridi, L. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3559407 · Cited by 13 · see citing works
Weapons of maths destruction: How big data increases inequality and threatens democracy
Hollis, L. (2017) · IPPR Progressive Review · doi:10.1111/newe.12047 · Cited by 12 · see citing works
Technological Innovation and Discrimination in Household Finance
Morse, A., & Pence, K. M. (2020) · Finance and Economics Discussion Series · doi:10.17016/feds.2020.018 · Cited by 9 · see citing works
Fair models in credit: intersectional discrimination and the amplification of inequity
Kim, S., Lessmann, S., Andreeva, G., & Rovatsos, M. (2026) · Annals of Operations Research · doi:10.1007/s10479-026-07086-0 · Cited by 9 · see citing works
Anatomy of a Fair Lending Exam: The Uses and Limitations of Statistics
Calem, P. S., & Longhofer, S. D. (2000) · Finance and Economics Discussion Series · doi:10.17016/feds.2000.15 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- Bartlett, R. P., Morse, A., Wallace, N., and Stanton, R. (2021). Algorithmic Accountability: A Legal and Economic Framework. Berkeley Technology Law Journal , 36(3):675-736.
- Berg, T., Fuster, A., and Puri, M. (2023). FinTech Lending. Annual Review of Financial Economics , 14(1):187-207.
- Cowgill, B. and Tucker, C. E. (2020). Algorithmic Fairness and Economics. SSRN Electronic Journal .
- Mayson, S. G. (2019). Bias In, Bias Out. The Yale Law Journal , 128(8):2218-2300.
Cited by
Sorted by citations
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 · see citing works
Robust integration of blockchain and explainable federated learning for automated credit scoring
Jovanovic, Z., Hóu, Z., Biswas, K., & Muthukkumarasamy, V. (2024) · Computer Networks · doi:10.1016/j.comnet.2024.110303 · Cited by 53 · see citing works
Li, C., Wang, H., Jiang, S., & Gu, B. (2024) · MIS Quarterly · doi:10.25300/misq/2024/18340 · Cited by 36 · see citing works
Operationalizing the Search for Less Discriminatory Alternatives in Fair Lending
Gillis, T. B., Meursault, V., & Ustun, B. (2024) · Conference on Fairness, Accountability and Transparency · doi:10.1145/3630106.3658912 · Cited by 30 · see citing works
Algorithmic decision making methods for fair credit scoring
Moldovan, D. (2023) · IEEE Access · doi:10.1109/access.2023.3286018 · Cited by 28 · see citing works
Consumer Credit in The Age of AI – Beyond Anti-Discrimination LawPreprint
Langenbucher, K. C. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4275723 · Cited by 26 · see citing works
Adegoke, T. I., Ofodile, O. C., Ochuba, N. A., & Akinrinola, O. (2024) · GSC Advanced Research and Reviews · doi:10.30574/gscarr.2024.18.3.0104 · Cited by 16 · see citing works
Learning fair models without sensitive attributes: A generative approach
Zhu, H., Dai, E., Liu, H., & Wang, S. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126841 · Cited by 12 · see citing works
Application of fairness to healthcare, organizational justice, and finance: A survey
Birzhandi, P., & Cho, Y.-S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119465 · Cited by 13 · see citing works
Adegoke, T. I., Ofodile, O. C., Ochuba, N. A., & Akinrinola, O. (2024) · International Journal of Science and Research Archive · doi:10.30574/ijsra.2024.11.2.0416 · Cited by 11 · see citing works
Credit Scoring with Drift Adaptation Using Local Regions of Competence
Nikolaidis, D., & Doumpos, M. (2022) · Operations Research Forum · doi:10.1007/s43069-022-00177-1 · Cited by 7 · see citing works
Measuring Fairness in Credit ScoringPreprint
Chen, Y., Giudici, P., Liu, K., & Raffinetti, E. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4123413 · Cited by 5 · see citing works
Saxena, A. K. (2024) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas61960.2024.10732696 · Cited by 4 · see citing works
Explainable Fairness in Mortgage Lending
Babaei, G., Giudici, P., & Wu, L. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-08330-2_18 · Cited by 3 · see citing works
Explainability, fairness and the Simpson’s paradox in credit lending
Babaei, G., Giudici, P., & Neelakantan, P. (2025) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2025.131030 · Cited by 2 · see citing works
Explainable Fairness and Propensity Score Matching
Giudici, P., & Babaei, G. (2025) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.70047 · Cited by 2 · see citing works
Falelakis, T., Dimara, A., & Anagnostopoulos, C. (2026) · Information · doi:10.3390/info17040326 · Cited by 2 · see citing works
Assuring AI methods for economic policymaking
Monken, A., Ampeh, W., Haberkorn, F., Krishnaswamy, U., & Batarseh, F. A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-391919-7.00025-1 · Cited by 2 · see citing works
Conformité européenne des systèmes d’IA : outils statistiques élémentaires
Besse, P. C. (2022) · Statistique et société · doi:10.4000/statsoc.565 · Cited by 1 · see citing works
Meursault, V., Moulton, D., Santucci, L., & Schor, N. (2024) · Journal of Policy Analysis and Management · doi:10.1002/pam.22662 · Cited by 1 · see citing works
Machine Learning and IRB Capital Requirements: Advantages, Risks, and RecommendationsPreprint
Hurlin, C., & Pérignon, C. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4483793 · Cited by 1 · see citing works
Self-citationConditional Fairness, with Application to Credit LendingPreprint
Babaei, G., & Neelakantan, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4814600 · Cited by 1 · see citing works
Explainable Fairness, with Application to Credit LendingPreprint
Stefano, P. G., Babaei, G., & Neelakantan, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5082737 · Cited by 1 · see citing works
Fairness Testing for Algorithmic Pricing
Huang, F., & Hooker, G. (2026) · arXiv · doi:10.48550/arxiv.2605.11614
“…in both the literature and regulatory proposals treats statistical testing, sample design, and remediation as separate exercises rather than an integrated pre-committed pipeline (Colorado Division of Insurance, 2023; Hurlin et al., 2026; New York State Department of Financial Services, 2024).”
InfluentialAdvancing fairness in natural language processing : from traditional methods to explainability
Jourdan, F. (2024) · theses.fr (ABES) · doi:10.70675/b8535a05z82f1z46f7z9688z3cd35cea8381 · Cited by 3 · see citing works
Are bank decisions fair? a statistical analysis of fairness in credit lending
Giudici, P., Neelakantan, P., Pavarana, S., & Schmidt, T. (2026) · Statistics · doi:10.1080/02331888.2026.2700452
- Authors
- Hurlin, C., Perignon, C., & Saurin, S.
- Affiliations
- Université d'Orléans (FR)
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2021
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W3134370295
- Cited by
- 26
- References
- 93
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 9 · 2023: 5 · 2022: 6
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- HAL (submittedVersion)
- Funders
- Agence Nationale de la Recherche (ANR-21-CE26-0007)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Accounting › Financial Distress and Bankruptcy Prediction
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- algorithmic fairness, credit scoring, credit risk assessment, protected characteristics, discrimination, fairness-performance trade-off, fairness testing, model interpretability, credit regulation
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