Welfare Implications of Fairness and Accountability for Insurance Pricing
Open accessIntegrity signals
Registry
Cites
Statistics
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 practice, the pure premium for policyholders with the same risk profile may deviate significantly, especially when price optimization is allowed.
“However, in practice, the pure premium for policyholders with the same risk profile may deviate significantly, especially when price optimization is allowed; see Shimao and Huang (2022) for further discussion.”
From Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models (2024) · cites this paper as Shimao and Huang (2022)
Cites
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 · see citing works
Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information
Rothschild, M., & Stiglitz, J. (1976) · The Quarterly Journal of Economics · doi:10.2307/1885326 · Cited by 4,987 · see citing works
xgboost: Extreme Gradient Boosting
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y., Cho, H., Chen, K., Mitchell, R., Cano, I., Zhou, T., et al. (2014) · doi:10.32614/cran.package.xgboost · Cited by 2,735 · see citing works
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 · see citing works
A model of insurance markets with incomplete information
Wilson, C. M. (1977) · Journal of Economic Theory · doi:10.1016/0022-0531(77)90004-7 · Cited by 1,061 · see citing works
Estimating Risk Preferences from Deductible Choice
Cohen, A., & Einav, L. (2007) · American Economic Review · doi:10.1257/aer.97.3.745 · Cited by 708 · see citing works
Estimating Welfare in Insurance Markets Using Variation in Prices *
Liran Einav, B., Finkelstein, A. N., & Cullen, M. R. (2010) · The Quarterly Journal of Economics · doi:10.1162/qjec.2010.125.3.877 · Cited by 456 · see citing works
Chapter 10 Price discrimination
Varian, H. R. (1989) · Handbook of Industrial Organization · doi:10.1016/s1573-448x(89)01013-7 · Cited by 343 · see citing works
Quantifying search and switching costs in the US auto insurance industry
Honka, E. (2014) · The RAND Journal of Economics · doi:10.1111/1756-2171.12073 · Cited by 303 · see citing works
Beyond Testing: Empirical Models of Insurance Markets
Liran Einav, B., Finkelstein, A. N., & Levin, J. (2010) · Annual Review of Economics · doi:10.1146/annurev.economics.050708.143254 · Cited by 228 · see citing works
Price Discrimination with Fairness Constraints
Cohen, M. C., Elmachtoub, A. N., & Lei, X. (2022) · Management Science · doi:10.1287/mnsc.2022.4317 · Cited by 160 · see citing works
The cost of fairness in binary classification
Menon, A. K., & Williamson, R. C. (2018) · Cited by 157 · see citing works
Crowds, Lending, Machine, and Bias
Fu, R., Huang, Y. H., & Singh, P. V. (2021) · Information Systems Research · doi:10.1287/isre.2020.0990 · Cited by 126 · see citing works
Product Design in Selection Markets *
Veiga, A., & Weyl, E. G. (2016) · The Quarterly Journal of Economics · doi:10.1093/qje/qjw007 · Cited by 117 · see citing works
Zhang, S., Mehta, N., Singh, P. V., & Srinivasan, K. (2021) · Marketing Science · doi:10.1287/mksc.2021.1295 · Cited by 114 · see citing works
'Un'Fair Machine Learning Algorithms
Fu, R., Aseri, M., Singh, P. V., & Srinivasan, K. (2019) · Management Science · doi:10.1287/mnsc.2021.4065 · Cited by 80 · see citing works
Discrimination-Free Insurance Pricing
Lindholm, M., Richman, R., Tsanakas, A., & Wüthrich, M. V. (2022) · ASTIN Bulletin · doi:10.1017/asb.2021.23 · Cited by 60 · see citing works
The Discriminating (Pricing) Actuary
Frees, E. W. (., & Huang, F. (2023) · North American Actuarial Journal · doi:10.1080/10920277.2021.1951296 · Cited by 56 · see citing works
Self-citationAntidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models
Xin, X., & Huang, F. (2024) · North American Actuarial Journal · doi:10.1080/10920277.2023.2190528 · Cited by 43 · see citing works
Self-citationFairness 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 32 · see citing works
Henckaerts, R., Antonio, K., & Côté, M.-P. (2020) · Expert systems with applications · doi:10.1016/j.eswa.2022.117230 · Cited by 28 · see citing works
A discrimination-free premium under a causal framework
Araiza Iturria, C. A., Hardy, M., & Marriott, P. (2024) · North American Actuarial Journal · doi:10.1080/10920277.2023.2291524 · Cited by 20 · see citing works
A Discrimination-Free Premium Under a Causal Framework
Araiza Iturria, C. A., Hardy, M., & Marriott, P. (2022) · doi:10.2139/ssrn.4079068 · Cited by 20 · see citing works
Two-Player Games for Efficient Non-Convex Constrained OptimizationPreprint
Cotter, A., Jiang, H., & Sridharan, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1804.06500 · Cited by 10 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models
Xin, X., & Huang, F. (2024) · North American Actuarial Journal · doi:10.1080/10920277.2023.2190528 · Cited by 43 · see citing works
“Another interesting area is to understand the ANTIDISCRIMINATION INSURANCE PRICING implications of different fairness criteria and regulations for stakeholders considering the entire pricing process; for example, see Shimao and Huang (2022).”
BackgroundInfluentialSelf-citationFairness: plurality, causality, and insurability
Fahrenwaldt, M., Furrer, C., Hiabu, M., Huang, F., Jørgensen, F. H., Lindholm, M., Loftus, J. R., Steffensen, M., & Tsanakas, A. (2024) · European Actuarial Journal · doi:10.1007/s13385-024-00387-3 · Cited by 7 · see citing works
Self-citationA Fair price to pay: exploiting causal graphs for fairness in insurancePreprint
Côté, O., Côté, M., & Arthur Charpentier, A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4709243 · Cited by 2 · see citing works
Consumer-Centric Machine Learning in Life Insurance: Applications, Challenges, and Future Trends
Clímaco, I. N., & Melo, D. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-16284-7_25
- Authors
- Shimao, H., & Huang, F.
- Affiliations
- Santa Fe Institute (US)
- UNSW Sydney (AU)
- Corresponding author
- Huang, Fei
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2022
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4312878395
- Cited by
- 4
- References
- 23
- Citations by year
- 2026: 1 · 2024: 2 · 2023: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Insurance and Financial Risk Management
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- insurance pricing, price regulation, fairness, accountability, welfare analysis, demographic parity, automobile insurance, compulsory insurance, voluntary insurance, market competition, monopoly, consumer welfare, firm profitability
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