REFERENCES
- Agarwal, A., M. Dudik, and Z. S. Wu. 2019. Fair regression: Quantitative definitions and reduction-based algorithms. Proceedings of the International Conference on Machine Learning 97:120–29.
- Alexander v. sandoval (No. 99-1908; Vol. 532, p. 275). (2001). (Vol. 532). Supreme Court. https://www.supremecourt.gov/opinions/boundvolumes/532bv.pdf
- AMERICAN INS. V. US DEPT. OF HOUSING AND URBAN (Civil Case No. 13-00966 (RJL); Vol. 74, p. 30). (2014). (Vol. 74). Dist. Court, Dist. of Columbia. https://ecf.dcd.uscourts.gov/cgi-bin/show_public_doc?2013cv0966-47
- Araiza Iturria, C. A., M. Hardy, and P. Marriott. 2022. A discrimination-free premium under a causal framework. 10.2139/ssrn.4079068
- Australian Human Rights Commission. 2020. Using artificial intelligence to make decisions: Addressing the problem of algorithmic bias. https://humanrights.gov.au/sites/default/files/document/publication/ahrc_technical_paper_algorithmic_bias_2020.pdf.
- Australian Human Rights Commission. n.d.a. Direct discrimination. https://humanrights.gov.au/quick-guide/12026.
- Australian Human Rights Commission. n.d.b. Indirect discrimination. https://humanrights.gov.au/quick-guide/12049.
- Australian Law Reform Commission. 2003. Essentially yours—The protection of human genetic information in Australia. Vols. 1 and 2. Report 96. https://www.alrc.gov.au/publication/essentially-yours-the-protectionof-human-genetic-information-in-australia-alrc-report-96/%7D.
- Avraham, R., K. D. Logue, and D. Schwarcz. 2014a. Towards a universal framework for insurance anti-discrimination laws. Connecticut Insurance Law Journal 21:1.
- Avraham, R., K. D. Logue, and D. Schwarcz. 2014b. Understanding insurance antidiscrimination laws. Southern California Law Review 87 (2):195–274.
- Barocas, S., M. Hardt, and A. Narayanan. 2019. Fairness and machine learning. fairmlbook.org.
- Barocas, S., and A. D. Selbst. 2016. Big data’s disparate impact. California Law Review 104:671.
- Berk, R. 2009. The role of race in forecasts of violent crime. Race and Social Problems 1 (4):231–42. 10.1007/s12552-009-9017-z
- Berk, R., H. Heidari, S. Jabbari, M. Joseph, M. Kearns, J. Morgenstern, S. Neel, and A. Roth. 2017. A convex framework for fair regression. arXiv Preprint arXiv:1706.02409.
- Berk, R., H. Heidari, S. Jabbari, M. Kearns, and A. Roth. 2018. Fairness in criminal justice risk assessments: The state of the art. Sociological Methods & Research 50 (1):3–44.
- Binns, R. 2018. Fairness in machine learning: Lessons from political philosophy. Proceedings of Machine Learning Research 81:149–59.
- Binns, R. 2020. On the apparent conflict between individual and group fairness. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 514–24. New York, NY: Association for Computing Machinery.
- Birnbaum, B. 2020a. Addressing systemic racism in insurance: Presentation to the NAIC committee on race and insurance workstream 4: Life insurance and annuities. https://content.naic.org/sites/default/files/call_materials/cej_presentation_naic_race_life_201210.pdf.
- Birnbaum, B. 2020b. Insurance consumer protection issues resulting from, or heightened by COVID-19: Presentation to NAIC Consumer Liaison Committee. https://content.naic.org/sites/default/files/call_materials/Slideshow_Consumer%5B1%5D.pdf.
- California Department of Insurance. 2020. Private passenger auto class plan filing instructions. http://www.insurance.ca.gov/0250-insurers/0800-rate-filings/upload/Class-Plan-Instructions-02_10_2020.pdf.
- California Office of Administrative Law. (2023). California code of regulations. https://govt.westlaw.com/calregs/Document/IDE21D4235C2F11EC9C68000D3A7C4BC3?viewType=FullText&originationContext=documenttoc&transitionType=CategoryPageItem&contextData=(sc.Default)
- Casualty Actuarial Society. (1988). Statement of principles regarding property and casualty insurance ratemaking. https://www.casact.org/sites/default/files/2021-05/Statement-Of-Principles-Ratemaking.pdf
- Casualty Actuarial and Statistical (C) Task Force. 2015. Casualty Actuarial and Statistical (C) Task Force price optimization white paper. https://content.naic.org/sites/default/files/inline-files/committees_c_catf_related_price_optimization_white_paper.pdf.
- Caton, S., and C. Haas. 2020. Fairness in machine learning: A survey. arXiv Preprint arXiv:2010.04053.
- Centers for Medicare & Medicaid Services. 2022. Market rating reforms—State specific geographic rating areas. https://www.cms.gov/CCIIO/Programs-and-Initiatives/Health-Insurance-Market-Reforms/state-gra.
- Chen, T., and C. Guestrin. 2016. XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–94. New York, NY: Association for Computing Machinery.
- Chen, T., T. He, M. Benesty, V. Khotilovich, Y. Tang, H. Cho, K. Chen, R. Mitchell, I. Cano, T. Zhou, et al. 2015. XGBoost: Extreme gradient boosting. R Package v0.4-2. https://cran.microsoft.com/snapshot/2022-01-01/web/packages/xgboost/xgboost.pdf.
- Chiappa, S. 2019. Path-specific counterfactual fairness. Proceedings of the AAAI Conference on Artificial Intelligence 33:7801–8. 10.1609/aaai.v33i01.33017801
- Chibanda, K. F. 2022. SB21-169—Protecting consumers from unfair discrimination in insurance practices. https://doi.colorado.gov/for-consumers/sb21-169-protecting-consumers-from-unfair-discrimination-in-insurance-practices.
- Civil rights act of 1991. (1991). Public Law 102-166. https://www.eeoc.gov/civil-rightsact-1991-original-text#:∼:text=To%20amend%20the%20Civil%20Rights,actions%2C%20and%20for%20other%20purposes.
- Consumer Reports. 2021. Effects of varying education level and job status on online auto insurance price quotes. https://advocacy.consumerreports.org/wp-content/uploads/2021/01/Auto-Insurance-White-Paper-Report-FINAL1.26C.pdf.
- Corbett-Davies, S., E. Pierson, A. Feller, S. Goel, and A. Huq. 2017. Algorithmic decision making and the cost of fairness. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 797–806. New York, NY: Association for Computing Machinery.
- Council of the European Union. (2000). Council directive 2000/43/EC of 29 june 2000 implementing the principle of equal treatment between persons irrespective of racial or ethnic origin. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32000L0043
- Council of the European Union. 2004. Council Directive 2004/113/EC of 13 December 2004 implementing the principle of equal treatment between men and women in the access to and supply of goods and services. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32004L0113.
- Court of Justice. 2011. Judgment of the Court (Grand Chamber) of 1 March 2011. https://eurlex.europa.eu/legal-content/EN/ALL/?uri=CELEX%3A62009CJ0236, Judgement of the Court, Case C-236/09.
- De Jong, P., and G. Z. Heller. 2008. Generalized linear models for insurance data. New York, NY: Association for Computing Machinery. Cambridge Books.
- Department of Housing and Urban Development. 2013. Implementation of the Fair Housing Act’s discriminatory effects standard. https://www.hud.gov/sites/documents/DISCRIMINATORYEFFECTRULE.PDF.
- Department of Housing and Urban Development. 2020. HUD’s implementation of the Fair Housing Act’s disparate impact standard. https://www.govinfo.gov/content/pkg/FR-2020-09-24/pdf/2020-19887.pdf.
- Department of Housing and Urban Development. 2021. Reinstatement of HUD’s discriminatory effects standard. https://www.federalregister.gov/documents/2021/06/25/2021-13240/reinstatement-of-huds-discriminatory-effects-standard.
- Department of Justice. (2021). Title VI legal manual. https://www.justice.gov/crt/fcs/T6Manual7#:%20:text=To/%20establish/%20an/%20adverse/%20disparate,and/%%20Q201%2020(4)/%20establish/%20causation
- Department of Transportation. (n.d.a). Title VI - intentional discrimination and disparate impact. https://www.fhwa.dot.gov/civilrights/programs/docs/Title%20VI%20-%20Intentional%20Discrimination%20and%20Disparate%20Impact.pdf
- Department of Transportation. (n.d.b). What types of discrimination are prohibited by title VI? https://www.fhwa.dot.gov/civilrights/programs/docs/Title%20VI%20-%20Types%20of%20Discrimination.pdf
- Directorate of Legal and Administrative Information. (2020). Bonus-malus in car insurance. https://www.service-public.fr/particuliers/vosdroits/F2655.
- Di Stefano, P. G., J. M. Hickey, and V. Vasileiou. 2020. Counterfactual fairness: Removing direct effects through regularization. arXiv Preprint arXiv:2002.10774.
- Directorate of Legal and Administrative Information. 2022. Assurance auto obligatoire ou “au tiers” [Compulsory or "third party" car insurance]. https://www.service-public.fr/particuliers/vosdroits/F2628.
- Dolman, C., and D. Semenovich. 2019. Algorithmic fairness: Some practical considerations for actuaries. Paper presented at Actuaries Summit 2019, Sydney, New South Wales, Australia, June 3–4.
- Dunn, R. A. 2009. Measuring racial disparities in traffic ticketing within large urban jurisdictions. Public Performance & Management Review 32 (4):537–61. 10.2753/PMR1530-9576320403
- Dutang, C., A. Charpentier, and M. C. Dutang. 2015. Package “CASdatasets.” http://dutangc.perso.math.cnrs.fr/RRepository/pub/web/CASdatasets-manual.pdf.
- Dwork, C., M. Hardt, T. Pitassi, O. Reingold, and R. Zemel. 2012. Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, 214–26. New York, NY: Association for Computing Machinery.
- Elliott, M. N., P. A. Morrison, A. Fremont, D. F. McCaffrey, P. Pantoja, and N. Lurie. 2009. Using the Census Bureau’s surname list to improve estimates of race/ethnicity and associated disparities. Health Services and Outcomes Research Methodology 9 (2):69–83. 10.1007/s10742-009-0047-1
- European Commission. 2014. Commission Staff working document Annexes to the joint report on the application of the racial equality directive (2000/43/EC) and the employment equality directive (2000/78/EC). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex:52014SC0005.
- European Insurance and Occupational Pensions Authority. 2019. Big data analytics in motor and health insurance: A thematic review. https://register.eiopa.europa.eu/Publications/EIOPA_BigDataAnalytics_ThematicReview_April2019.pdf.
- Fang, H., and A. Ko. 2018. Partial rating area offering in the ACA marketplaces: Facts, theory and evidence. Cambridge, MA: National Bureau of Economic Research.
- Fauzan, M. A., and H. Murfi. 2018. The accuracy of XGBoost for insurance claim prediction. International Journal of Advances in Soft Computing and Its Applications 10 (2):159–171.
- Federal Reserve. 2017. Fair lending regulations and statutes: Overview. https://www.federalreserve.gov/boarddocs/supmanual/cch/fair_lend_over.pdf.
- Feldman, M., S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian. 2015. Certifying and removing disparate impact. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 259–68. New York, NY: Association for Computing Machinery.
- Fick, B. (1997). American bar association guide to workplace law. Times Books, New York, United States.
- Frees, E. W., R. A. Derrig, and G. Meyers. 2014. Predictive modeling applications in actuarial science. Vol. 1. New York, NY: Cambridge University Press.
- Frees, E. W., and F. Huang. 2021. The discriminating (pricing) actuary. North American Actuarial Journal. 27 (1):2–24
- Friedman, C. (2020). HUD issues final rule on the fair housing act’s disparate impact standard. https://www.jdsupra.com/legalnews/hud-issues-final-rule-on-the-fair-63161/.
- Gaulding, J. 1994. Race sex and genetic discrimination in insurance: What’s fair. Cornell Law Review 80:1646.
- Goldburd, M., A. Khare, D. Tevet, and D. Guller. 2016. Generalized linear models for insurance rating. Arlington, VA: Casualty Actuarial Society, CAS Monographs Series, 5.
- Grari, V., C. Arthur, L. Sylvain, and D. Marcin. 2022. A fair pricing model via adversarial learning. arXiv Preprint arXiv:2202.12008.
- Griggs v. Duke Power Co. No. 124; Vol. 401, p. 424 (1971). Supreme Court. https://tile.loc.gov/storage-services/service/ll/usrep/usrep401/usrep401424/usrep401424.pdf.
- Hardt, M. 2013. Fairness through awareness. https://course.ece.cmu.edu/∼ece734/fall2013/lectures/cmu13-fairness.pdf.
- Hardt, M., E. Price, and N. Srebro. 2016. Equality of opportunity in supervised learning. arXiv Preprint arXiv:1610.02413.
- Hedden, B. 2021. On statistical criteria of algorithmic fairness. Philosophy and Public Affairs 49 (2):209–231. doi:10.1111/papa.12189.
- Henckaerts, R., M.-P. Côté, K. Antonio, and R. Verbelen. 2021. Boosting insights in insurance tariff plans with tree-based machine learning methods. North American Actuarial Journal 25 (2):255–85. 10.1080/10920277.2020.1745656
- Hunstad, L. 1996. Sequential analysis guidelines. https://www.insurance.ca.gov/0250-insurers/0800-rate-filings/upload/Sequential-Analysis.pdf.
- Hutchinson, B., and M. Mitchell. 2019. 50 Years of test (un) fairness: Lessons for machine learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency, 49–58. New York, NY: Association for Computing Machinery.
- Insurance Information Institute. 2019. Background on: Credit scoring. https://www.iii.org/article/background-on-credit-scoring.
- Johndrow, J. E., and K. Lum. 2019. An algorithm for removing sensitive information: Application to race-independent recidivism prediction. The Annals of Applied Statistics 13 (1):189–220. 10.1214/18-AOAS1201
- Kallus, N., X. Mao, and A. Zhou. 2021. Assessing algorithmic fairness with unobserved protected class using data combination. Management Science. 68 (3):1959–1981
- Kamiran, F., and T. Calders. 2012. Data preprocessing techniques for classification without discrimination. Knowledge and Information Systems 33 (1):1–33. 10.1007/s10115-011-0463-8
- Kasirzadeh, A., and A. Smart. 2021. The use and misuse of counterfactuals in ethical machine learning. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 228–36. New York, NY: Association for Computing Machinery.
- Kilbertus, N., M. Rojas-Carulla, G. Parascandolo, M. Hardt, D. Janzing, and B. Schölkopf. 2017. Avoiding discrimination through causal reasoning. In Proceedings of the 31st International Conference on Neural Information Processing Systems, eds. Ulrike von Luxburg, Isabelle Guyon, Samy Bengio, Hanna Wallach, and Rob Fergus, 656–66. New York, NY: Curran Associates Inc.
- Kim, M. P., O. Reingold, and G. N. Rothblum. 2018. Fairness through computationally bounded awareness. arXiv Preprint arXiv:1803.03239.
- Kleinberg, J., S. Mullainathan, and M. Raghavan. 2016. Inherent trade-offs in the fair determination of risk scores. arXiv Preprint arXiv:1609.05807.
- Kusner, M. J., J. Loftus, C. Russell, and R. Silva. 2017. Counterfactual fairness. In Advances in Neural Information Processing Systems, eds. I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 4066–76. New York, NY: Curran Associates Inc.
- Landes, X. 2015. How fair is actuarial fairness? Journal of Business Ethics 128 (3):519–33. 10.1007/s10551-014-2120-0
- Lehtonen, T.-K., and J. Liukko. 2011. The forms and limits of insurance solidarity. Journal of Business Ethics 103 (1):33–44. 10.1007/s10551-012-1221-x
- Lindholm, M., R. Richman, A. Tsanakas, and M. V. Wüthrich. 2022a. Discrimination-free insurance pricing. ASTIN Bulletin 52 (1):55–89. 10.1017/asb.2021.23
- Lindholm, M., R. Richman, A. Tsanakas, and M. V. Wüthrich. 2022b. A discussion of discrimination and fairness in insurance pricing. arXiv Preprint arXiv:2209.00858.
- Lindholm, M., R. Richman, A. Tsanakas, and M. V. Wüthrich. 2022c. A multi-task network approach for calculating discrimination-free insurance prices. arXiv Preprint arXiv:2207.02799.
- Lohia, P. K., K. N. Ramamurthy, M. Bhide, D. Saha, K. R. Varshney, and R. Puri. 2019. Bias mitigation post-processing for individual and group fairness. In ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2847–51. New York, NY: IEEE.
- Loi, M., and M. Christen. 2021. Choosing how to discriminate: Navigating ethical trade-offs in fair algorithmic design for the insurance sector. Philosophy & Technology 34:967–92. 10.1007/s13347-021-00444-9
- Merriam-Webster. 2022. Discrimination. https://www.merriam-webster.com/dictionary/discrimination.
- Meyers, G., and I. van Hoyweghen. 2018. Enacting actuarial fairness in insurance: From fair discrimination to behaviour-based fairness. Science as Culture 27 (4):413–38. 10.1080/09505431.2017.1398223
- Miller, M. J. 2009. Disparate impact and unfairly discriminatory insurance rates. Casualty Actuarial Society e-Forum 276.
- National Association of Insurance Commissioners. 2010. Property and casualty model rating law (GL-1775). https://content.naic.org/sites/default/files/GL1775.pdf.
- National Association of Insurance Commissioners. 2020a. NAIC unanimously adopts artificial intelligence guiding principles. https://content.naic.org/article/news_release_naic_unanimously_adopts_artificial_intelligence_%0Aguiding_principles.htm#:∼:text=Washington%20(August%2020%2C%202020),safe%2C%20secure%%0A20and%20robust%20outputs.
- National Association of Insurance Commissioners. 2020b. National Association of Insurance Commissioners (NAIC) principles on artificial intelligence (AI). https://content.naic.org/sites/default/files/inlinefiles/AI%20principles%20as%20Adopted%20by%20the%20TF_0807.pdf, as a response to the OECD Principles on Artificial Intelligence.
- National Association of Insurance Commissioners. 2021. NAIC members vote to form new letter committee. https://content.naic.org/article/naic-members-vote-form-new-letter-committee.
- National Association of Insurance Commissioners. 2022. McCarran-Ferguson Act. https://content.naic.org/cipr-topics/mccarran-ferguson-act; see the legislative background of the Act as per the NAIC.
- National Association of Mutual Insurance Companies. 2017. Our positions—Disparate impact rule. https://www.namic.org/issues/disparate-impact-rule.
- National Association of Mutual Insurance Companies. 2020. Re: NAMIC comments on the draft NAIC principles on artificial intelligence. https://content.naic.org/sites/default/files/call_materials/NAMIC%20-%20NAIC%20AIWG%20-%20Comments%20-%206-29-20.pdf.
- Ojo v. Farmers group, inc. (No. 10-0245; Vol. 356, p. 421). (2011). (Vol. 356). Tex: Supreme Court. https://www.txcourts.gov/media/819896/OpinionsFY2011.pdf
- Pearl, J. 2000. Models, reasoning and inference. Cambridge, UK: Cambridge University Press.
- Pope, D. G., and J. R. Sydnor. 2011. Implementing anti-discrimination policies in statistical profiling models. American Economic Journal: Economic Policy 3 (3):206–31. 10.1257/pol.3.3.206
- Prince, A. E., and D. Schwarcz. 2019. Proxy discrimination in the age of artificial intelligence and big data. Iowa Law Review 105:1257.
- Quintanar, S. M. 2017. Man vs. Machine: An investigation of speeding ticket disparities based on gender and race. Journal of Applied Economics 20 (1):1–28. 10.1016/S1514-0326(17)30001-6
- Rawls, J. 2001. Justice as fairness: A restatement. Cambridge, MA: Harvard University Press.
- Ricci v. DeStefano (No. 07-1428; Vol. 557, p. 557). (2009). (Vol. 557). Supreme Court. https://www.supremecourt.gov/opinions/boundvolumes/557bv.pdf
- Rogelberg, S. G. 2007. Encyclopedia of industrial and organizational psychology. Vol. 1. Los Angeles, CA: Sage.
- Roth, P. L., P. Bobko, and F. S. Switzer III. 2006. Modeling the behavior of the 4/5ths rule for determining adverse impact: Reasons for caution. Journal of Applied Psychology 91 (3):507. 10.1037/0021-9010.91.3.507
- Sawyer, R. L., N. S. Cole, and J. W. Cole. 1976. Utilities and the issue of fairness in a decision theoretic model for selection. Journal of Educational Measurement 13 (1):59–76.
- Schelldorfer, J., and M. V. Wuthrich. 2019. Nesting classical actuarial models into neural networks. 10.2139/ssrn.3320525
- Shimao, H., and F. Huang. 2022. Welfare cost of fair prediction and pricing in insurance market. http://dx.doi.org/10.2139/ssrn.4225159.
- Smith v. City of Jackson. No. 03-1160; Vol. 544, p. 228 (2005). Supreme Court. https://www.supremecourt.gov/opinions/boundvolumes/544bv.pdf.
- Texas Department of Housing and Community Affairs v. Inclusive Communities Project, Inc. No. 13-1371; Vol. 576, p. 519 (2015). Supreme Court. https://www.supremecourt.gov/opinions/boundvolumes/576BV.pdf.
- The Lawyers’ Committee for Civil Rights Under Law. (2015). American insurance association. https://www.lawyerscommittee.org/project/aianamic/
- Thomas, R. G. 2012. Non-risk price discrimination in insurance: Market outcomes and public policy. The Geneva Papers on Risk and Insurance-Issues and Practice 37 (1):27–46.
- Thorndike, R. L. 1971. Concepts of culture-fairness. Journal of Educational Measurement 8 (2):63–70. 10.1111/j.1745-3984.1971.tb00907.x
- University of California. 2008. Race, sex and disparate impact: Legal and policy considerations regarding University of California admissions and scholarships. https://regents.universityofcalifornia.edu/regmeet/may08/e2attach.pdf.
- U.S. Equal Employment Opportunity Commission. 1979. Questions and answers to clarify and provide a common interpretation of the uniform guidelines on employee selection procedures. https://www.eeoc.gov/laws/guidance/questions-and-answers-clarify-and-provide-commoninterpretation-uniform-guidelines.
- U.S. Government Publishing Office. 2017. Part 1607—Uniform guidelines on employee selection procedures (1978). https://www.govinfo.gov/content/pkg/CFR-2017-title29-vol4/xml/CFR-2017-title29-vol4-part1607.xml.
- Verma, S., and J. Rubin. 2018. Fairness definitions explained. Paper presented at the 2018 IEEE/ACM International Workshop on Software Fairness (Fairware), Gothenburg, Sweden, May 29.
- Wang, S., W. Guo, H. Narasimhan, A. Cotter, M. Gupta, and M. I. Jordan. 2020. Robust optimization for fairness with noisy protected groups. arXiv Preprint arXiv:2002.09343.
- Wards cove packing co. V. atonio (No. 87-1387; Vol. 490, p. 642). (1989). (Vol. 490). Supreme Court. https://tile.loc.gov/storage-services/service/ll/usrep/usrep490/usrep490642/usrep490642.pdf
- White House. 2021. Memorandum on redressing our nation’s and the federal government’s history of discriminatory housing practices and policies. https://www.whitehouse.gov/briefing-room/presidential-actions/2021/01/26/memorandumon-redressing-our-nations-and-the-federal-governments-history-of-discriminatoryhousing-practices-and-policies/.
- Willis, C. J., R. J. Andreano, and L. Sommerfield. 2021. President Biden issues executive order directing HUD to review fair housing act disparate impact rule. https://www.consumerfinancemonitor.com/2021/02/03/president-biden-issues-executiveorder-directing-hud-to-review-fair-housing-act-disparate-impact-rule/.
- Wiśniewski, J., and P. Biecek. 2021. Fairmodels: A flexible tool for bias detection, visualization, and mitigation. arXiv Preprint arXiv:2104.00507.
- Wortham, L. 1986a. The economics of insurance classification: The sound of one invisible hand clapping. Ohio State Law Journal 47:835.
- Wortham, L. 1986b. Insurance classification: Too important to be left to the actuaries. University of Michigan Journal of Law Reform 19 (2):349–424.
- Wu, Y., L. Zhang, and X. Wu. 2019. Counterfactual fairness: Unidentification, bound and algorithm. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence., ed. Sarit Kraus, 1438–1444. Macao, China: International Joint Conferences on Artificial Intelligence.
- Xenidis, R., and L. Senden. 2019. EU non-discrimination law in the era of artificial intelligence: Mapping the challenges of algorithmic discrimination. In General principles of EU law and the EU digital order, eds. Ulf Bernitz, Xavier Groussot, Jaan Paju, Sybe Alexander de Vries, 151–82. Alphen aan den Rijn, NL: Kluwer Law International.
- Ye, C., L. Zhang, M. Han, Y. Yu, B. Zhao, and Y. Yang. 2018. Combining predictions of auto insurance claims. arXiv Preprint arXiv:1808.08982.
- Zemel, R., Y. Wu, K. Swersky, T. Pitassi, and C. Dwork. 2013. Learning fair representations. Proceedings of the 30th International Conference on Machine Learning 28:325–33.