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Research Article

What is fair? Proxy discrimination vs. demographic disparities in insurance pricing

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References

  • Agarwal, A., Dudik, M., & Wu, Z. S. (2019). Fair regression: Quantitative definitions and reduction-based algorithms. arXiv: 1905.12843.
  • Araiza Iturria, C. A., Hardy, M., & Marriott, P. (2022). A discrimination-free premium under a causal framework (SSRN Manuscript ID 4079068).
  • Avraham, R., Logue, K. D., & Schwarcz, D. B. (2014). Understanding insurance anti-discrimination laws. Southern California Law Review, 87(2), 195–274.
  • Awasthi, P., Cortes, C., Mansour, Y., & Mohri, M. (2020). Beyond individual and group fairness. arXiv: 2008.09490.
  • Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and machine learning: Limitations and opportunities. https://fairmlbook.org/
  • Binns, R. (2020). On the apparent conflict between individual and group fairness. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 514–524). Association for Computing Machinery.
  • Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Conference on Fairness, Accountability and Transparency. Proceedings of Machine Learning Research (Vol. 81, pp. 77–91). PMLR.
  • Charpentier, A. (2022). Insurance: Discrimination, biases & fairness. In Institut Louis Bachelier, Opinions & Débates, No25, July 2022.
  • Charpentier, A., Hu, F., & Ratz, P. (2023). Mitigating discrimination in insurance with Wasserstein barycenters. arXiv: 2306.12912.
  • Chiappa, S., Jiang, R., Stepleton, T., Pacchiano, A., Jiang, H., & Aslanides, J. (2020). A general approach to fairness with optimal transport. In Proceedings of the 34th AAAI Conference on Artificial Intelligence (Vol. 34, No. 04). AAAI-20 Technical Tracks 4 .
  • Chibanda, K. F. (2021). Defining discrimination in insurance. In CAS research papers: A special series on race and insurance pricing. https://www.casact.org/publications-research/research/research-paper-series-race-and-insurance-pricing
  • Chzhen, E., Denis, C., Hebiri, M., Oneto, L., & Pontil, M. (2020). Fair regression with Wasserstein barycenters. Advances in Neural Information Processing Systems, 33, 7321–7331.
  • Cook, T., Greenall, A., & Sheehy, E. (2022). Discriminatory pricing: Exploring the ‘ethnicity penalty’ in the insurance market. Citizens Advice. https://www.citizensadvice.org.uk/Global/CitizensAdvice/Consumer%20publications/Report%20cover/Citizens%20Advice%20-%20Discriminatory%20Pricing%20report%20(4).pdf
  • Cuturi, M., & Doucet, A. (2014). Fast computation of Wasserstein barycenters. In Proceedings of the 31st International Conference on Machine Learning. PMLR.
  • Delbaen, F., & Majumdar, C. (2023). Approximation with independent random variables. Frontiers of Mathematical Finance, 2/2, 141–149. https://doi.org/10.3934/fmf.2023011
  • del Barrio, E., Gamboa, F., Grodaliza, P., & Loubes, J.-P. (2019). Obtaining fairness using optimal transport theory. In Proceedings of the 36st International Conference on Machine Learning, Long Beach, California. Proceedings of Machine Learning Research (Vol. 97, pp. 2357–2365). PMLR.
  • Djehiche, B., & Löfdahl, B. (2016). Nonlinear reserving in life insurance: Aggregation and mean-field approximation. Insurance: Mathematics & Economics, 69, 1–13.
  • Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (pp. 214–226). Association for Computing Machinery.
  • EIOPA (2021). Artificial intelligence governance principles: Towards ethical and trustworthy artificial intelligence in the European insurance sector (A report from EIOPA's Consultative Expert Group on Digital Ethics in Insurance).
  • EIOPA (2023). EIOPA supervisory statement takes aim at unfair ‘price walking’ practices. March 16, 2023. https://www.eiopa.europa.eu/eiopa-supervisory-statement-takes-aim-unfair-price-walking-practices-2023-03-16_en
  • European Commission (2012). Guidelines on the application of COUNCIL DIRECTIVE 2004/113/EC to insurance, in the light of the judgment of the Court of Justice of the European Union in Case C-236/09 (Test-Achats). Official Journal of the European Union (Vol. C11, pp. 1–11).
  • European Council (2004). COUNCIL DIRECTIVE 2004/113/EC – implementing the principle of equal treatment between men and women in the access to and supply of goods and services. Official Journal of the European Union (Vol. L 373, pp. 37–43).
  • Financial Conduct Authority (2021). General insurance pricing practices market study: Feedback to CP20/19 and final rules (Policy Statement PS21/5).
  • Frees, E. W. J., & Huang, F. (2022). The discriminating (pricing) actuary. North American Actuarial Journal, 27(1), 2–24. https://doi.org/10.1080/10920277.2021.1951296
  • Friedler, S., Scheidegger, C., & Venkatasubramanian, S. (2016). On the (im)possibility of fairness. arXiv: 1609.07236.
  • Grari, V., Charpentier, A., Lamprier, S., & Detyniecki, M. (2022). A fair pricing model via adversarial learning. arXiv: 2202.12008v2.
  • Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In Advances in neural information processing systems (pp. 3315–3323). Curran Associates.
  • Hedden, B. (2021). On statistical criteria of algorithmic fairness. Philosophy & Public Affairs, 49(2), 209–231. https://doi.org/10.1111/papa.v49.2
  • Kilbertus, N., Rojas Carulla, M., Parascandolo, G., Hardt, M., Janzing, D., & Schölkopf, B. (2017). Avoiding discrimination through causal reasoning. In Proceedings of the International Conference on Advances in Neural Information Processing Systems (pp. 656–666). Curran Associates.
  • Kleinberg, J., Mullainathan, S., & Raghavan, M. (2016). Inherent trade-offs in the fair determination of risk scores. arXiv: 1609.05807.
  • Kusner, M. J., Loftus, J., Russell, C., & Silva, R. (2017). Counterfactual fairness. In Advances in neural information processing systems (pp. 4066–4076). Curran Associates.
  • Lahoti, P., Gummadi, K. P., & Weikum, G. (2019). iFair: Learning individually fair data representations for algorithmic decision making. In IEEE 35th International Conference on Data Engineering (pp. 1334–1345).
  • Lindholm, M., Richman, R., Tsanakas, A., & Wüthrich, M. V. (2022). Discrimination-free insurance pricing. ASTIN Bulletin, 52(2), 55–89. https://doi.org/10.1017/asb.2021.23
  • Lindholm, M., Richman, R., Tsanakas, A., & Wüthrich, M. V. (2023). A multi-task network approach for calculating discrimination-free insurance prices. European Actuarial Journal. https://doi.org/10.1007/s13385-023-00367-z
  • Loader, C., Sun, J., & Liaw, A., & Lucent Technologies (2022). locfit: Local regression, likelihood and density estimation. https://cran.r-project.org/web/packages/locfit/index.html
  • Maliszewska-Nienartowicz, J. (2014). Direct and indirect discrimination in European union law – how to draw a dividing line?. International Journal of Social Sciences, III(1), 41–55.
  • Mehrabi, N., Morstatter, F., Sexana, N., Lerman, K., & Galstyan, A. (2019). A survey on bias and fairness in machine learning. arXiv: 1908.09635v3.
  • Pearl, J. (2009). Causality: Models, reasoning, and inference (2nd ed.).Cambridge University Press.
  • Pessach, D., & Erez Shmueli, E. (2022). A review on fairness in machine learning. ACM Computing Survey, 55(3), Article 51. https://doi.org/10.1145/3494672
  • Prince, A. E. R., & Schwarcz, D. (2020). Proxy discrimination in the age of artificial intelligence and big data. Iowa Law Review, 105(3), 1257–1318.
  • Qureshi, B., Kamiran, F., Karim, A., & Ruggieri, S. (2016). Causal discrimination discovery through propensity score analysis. arXiv: 1608.03735.
  • Ravfogel, S., Elazar, Y., Gonen, H., Twiton, M., & Goldberg, Y. (2020). Null it out: Guarding protected attributes by iterative nullspace projection. arXiv: 2004.07667.
  • Ravfogel, S., Twinton, M., Goldberg, Y., & Cotterell, R. (2022). Linear adversarial concept erasure. arXiv: 2201.12091.
  • Shimao, H., & Huang, F. (2022). Welfare cost of fair prediction and pricing in insurance market (SSRN Manuscript ID 4225159).
  • Thomas, R. G. (2012). Non-risk price discrimination in insurance: Market outcomes and public policy. Geneva Papers on Risk and Insurance – Issues and Practice, 37, 27–46. https://doi.org/10.1057/gpp.2011.32
  • Thomas, R. G. (2022). Discussion on ‘The discriminating (pricing) actuary’, by E. W. J. Frees and F. Huang. North American Actuarial Journal, in press.
  • Tschantz, M. C. (2022). What is proxy discrimination? In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (pp. 1993–2003). Association for Computing Machinery.
  • Vallance, C. (2021). Legal action over alleged Uber facial verification bias. BBC News. Retrieved April 28, 2023, from https://www.bbc.co.uk/news/technology-58831373.
  • Wüthrich, M. V., & Merz, M. (2015). Stochastic claims reserving manual: Advances in dynamic modeling (SSRN Manuscript ID 264905).
  • Wüthrich, M. V., & Merz, M. (2023). Statistical foundations of actuarial learning and its applications. Springer. https://doi.org/10.1007/978-3-031-12409-9
  • Wüthrich, M. V., & Ziegel, J. (2024). Isotonic recalibration under a low signal-to-noise ratio. Scandinavian Actuarial Journal, in press.
  • Xin, X., & Huang, F. (2021). Anti-discrimination insurance pricing: Regulations, fairness criteria, and models (SSRN Manuscript ID 3850420).
  • Zemel, R., Wu, Y., Swersky, K., Pitassi, T., & Dwork, C. (2013). Learning fair representations. In Proceedings of the 30th International Conference on Machine Learning, PMLR (Vol. 28, No. 3, pp. 325–333). PMLR.