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Insights From Insurance for Fair Machine Learning

Published: 05 June 2024 Publication History

Abstract

We argue that insurance can act as an analogon for the social situatedness of machine learning systems, hence allowing machine learning scholars to take insights from the rich and interdisciplinary insurance literature. Tracing the interaction of uncertainty, fairness and responsibility in insurance provides a fresh perspective on fairness in machine learning. We link insurance fairness conceptions to their machine learning relatives, and use this bridge to problematize fairness as calibration. In this process, we bring to the forefront two themes that have been largely overlooked in the machine learning literature: responsibility and aggregate-individual tensions.

References

[1]
Kenneth S. Abraham. 1985. Efficiency and fairness in insurance risk classification. Virginia Law Review 71, 3 (1985), 403–451.
[2]
Martin Marchman Andersen and Morten Ebbe Juul Nielsen. 2015. Luck egalitarianism, universal health care, and non-responsibility-based reasons for Responsibilization. Res Publica 21 (2015), 201–216.
[3]
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016. Machine Bias: There’s Software Used Across the Country to Predict Future Criminals, and It’s Biased Against Blacks. ProPublica (2016). https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing Accessed on June 2, 2023.
[4]
Chris Armstrong. 2005. Equality, risk and responsibility: Dworkin on the insurance market. Economy and Society 34, 3 (2005), 451–473.
[5]
Kenneth J. Arrow. 1963. Uncertainty and the welfare economics of medical care. The American Economic Review 53, 5 (1963), 941–973.
[6]
John L. Austin. 1962. How to Do Things With Words. Harvard University Press, Cambridge.
[7]
Ronen Avraham. 2018. Discrimination and insurance. In The Routledge Handbook of the Ethics of Discrimination. Routledge.
[8]
Ronen Avraham, Kyle D. Logue, and Daniel Schwarcz. 2013. Understanding insurance antidiscrimination law. Southern California Law Review 87 (2013), 195–274. https://scholarship.law.umn.edu/faculty_articles/576 Accessed on June 2, 2023.
[9]
Tom Baker. 1996. On the genealogy of moral hazard. Texas Law Review 75 (1996), 237.
[10]
Tom Baker. 2000. Insuring morality. Economy and Society 29, 4 (2000), 559–577.
[11]
Tom Baker. 2002. Risk, insurance, and the social construction of responsibility. University of Chicago Press.
[12]
Tom Baker and Jonathan Simon. 2002. Embracing risk: The changing culture of insurance and responsibility. University of Chicago Press.
[13]
Solon Barocas, Moritz Hardt, and Arvind Narayanan. 2019. Fairness and Machine Learning: Limitations and Opportunities. http://www.fairmlbook.org Accessed on January 5, 2024.
[14]
Laurence Barry. 2019. The rationality of the digital governmentality. Journal for Cultural Research 23, 4 (2019), 365–380.
[15]
Laurence Barry. 2020. Insurance, big data and changing conceptions of fairness. European Journal of Sociology/Archives Européennes de Sociologie 61, 2 (2020), 159–184.
[16]
Laurence Barry and Arthur Charpentier. 2022. The Fairness of Machine Learning in Insurance: New Rags for an Old Man?arXiv preprint arXiv:2205.08112 (2022).
[17]
Reuben Binns. 2020. On the apparent conflict between individual and group fairness. In Proceedings of the 2020 conference on fairness, accountability, and transparency. 514–524.
[18]
Joar Björk, Gert Helgesson, and Niklas Juth. 2020. Better in theory than in practise? Challenges when applying the luck egalitarian ethos in health care policy. Medicine, Health Care and Philosophy 23 (2020), 735–742.
[19]
Ivan Boldyrev and Ekaterina Svetlova. 2016. Enacting dismal science: New perspectives on the performativity of economics. Springer.
[20]
Geoffrey C. Bowker and Susan Leigh Star. 2000. Sorting Things Out: Classification and Its Consequences. The MIT Press.
[21]
Nicolas Brisset. 2016. Economics is not always performative: some limits for performativity. Journal of Economic Methodology 23, 2 (2016), 160–184.
[22]
Laure Cabantous. 2007. Ambiguity aversion in the field of insurance: Insurers’ attitude to imprecise and conflicting probability estimates. Theory and Decision 62, 3 (2007), 219–240.
[23]
Michel Callon (Ed.). 1998. The laws of the markets. Oxford: Blackwell.
[24]
Alessandro Castelnovo, Riccardo Crupi, Greta Greco, Daniele Regoli, Ilaria Giuseppina Penco, and Andrea Claudio Cosentini. 2022. A clarification of the nuances in the fairness metrics landscape. Scientific Reports 12 (2022). Article number: 4209.
[25]
Alberto Cevolini and Elena Esposito. 2020. From pool to profile: Social consequences of algorithmic prediction in insurance. Big Data & Society 7, 2 (2020), 1–11.
[26]
Alberto Cevolini and Elena Esposito. 2022. From Actuarial to Behavioural Valuation. The impact of telematics on motor insurance. Valuation Studies 9, 1 (2022), 109–139.
[27]
Arthur Charpentier. 2022. Insurance: Discrimination, Biases & Fairness. Opinions & Debates (2022). https://www.institutlouisbachelier.org/en/insurance-discrimination-biases-fairness/ Accessed on June 2, 2023.
[28]
Karen A. Clifford and Russel P. Iuculano. 1987. AIDS and insurance: the rationale for AIDS-related testing. Harvard Law Review 100, 7 (1987), 1806–1825.
[29]
Dan Cooper and Brian Grinder. 2009. Probability, Gambling and the Origins of Risk Management. Financial History Magazine 93 (2009), 10–11.
[30]
Alexander D’Amour, Hansa Srinivasan, James Atwood, Pallavi Baljekar, David Sculley, and Yoni Halpern. 2020. Fairness is not static: deeper understanding of long term fairness via simulation studies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 525–534.
[31]
Norman Daniels. 1990. Insurability and the HIV epidemic: ethical issues in underwriting. The Milbank Quarterly 68, 4 (1990), 497–525.
[32]
Lorraine Daston. 2023. Classical Probability in the Enlightenment. Princeton University Press.
[33]
Philip Dawid. 2017. On individual risk. Synthese 194, 9 (2017), 3445–3474.
[34]
Bruno de Finetti. 1974/2017. Theory of probability: A critical introductory treatment. John Wiley & Sons.
[35]
Mitchell Dean. 1998. Risk, calculable and incalculable. Soziale Welt (1998), 25–42.
[36]
Michel Denuit, Arthur Charpentier, and Julien Trufin. 2021. Autocalibration and Tweedie-dominance for insurance pricing with machine learning. Insurance: Mathematics and Economics 101 (2021), 485–497.
[37]
Alain Desrosières. 1998. The politics of large numbers: A history of statistical reasoning. Harvard University Press.
[38]
Rainer Diaz-Bone and Emmanuel Didier. 2016. Introduction: The sociology of quantification — perspectives on an emerging field in the social sciences. Historical Social Research 41, 2 (2016), 7–26.
[39]
Simon Dietz and Falk Niehörster. 2021. Pricing ambiguity in catastrophe risk insurance. The Geneva Risk and Insurance Review 46, 2 (2021), 112–132.
[40]
Kate Donahue and Solon Barocas. 2021. Better Together? How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness. In Proceedings of the 2021 Conference on Fairness, Accountability, and Transparency. 185–195.
[41]
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel. 2012. Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference. 214–226.
[42]
Benjamin Eidelson. 2021. Patterned inequality, compounding injustice, and algorithmic prediction. American Journal of Law and Equality 1 (2021), 252–276.
[43]
Richard Ericson, Dean Barry, and Aaron Doyle. 2000. The moral hazards of neo-liberalism: lessons from the private insurance industry. Economy and Society 29, 4 (2000), 532–558.
[44]
Wendy Nelson Espeland and Michael Sauder. 2007. Rankings and reactivity: How public measures recreate social worlds. Amer. J. Sociology 113, 1 (2007), 1–40.
[45]
Wendy Nelson Espeland and Mitchell L. Stevens. 2008. A sociology of quantification. European Journal of Sociology/Archives Européennes de Sociologie 49, 3 (2008), 401–436.
[46]
François Ewald. 1986. L’État providence. Grasset.
[47]
François Ewald. 1989. Die Versicherungs-Gesellschaft. Kritische Justiz 22, 4 (1989), 385–393.
[48]
François Ewald. 1990. Norms, discipline, and the law. Representations 30 (1990), 138–161.
[49]
Francois Ewald. 1991. Insurance and risk. In The Foucault effect: Studies in governmentality. The University of Chicago Press, 197–210.
[50]
Edward W. Frees and Fei Huang. 2023. The discriminating (pricing) actuary. North American Actuarial Journal 27, 1 (2023), 2–24.
[51]
Sylvestre Frezal. 2016. Alea and Heterogeneity: the Tyrannous Conflation. https://www.chaire-pari.fr/wp-content/uploads/2016/09/Alea-and-Heterogeneity_the-Tyrannous-Conflation_eng_MEP.pdf Accessed on June 14, 2023.
[52]
Sylvestre Frezal and Laurence Barry. 2020. Fairness in uncertainty: Some limits and misinterpretations of actuarial fairness. Journal of Business Ethics 167 (2020), 127–136.
[53]
Rachel Z. Friedman. 2020. Probable Justice: Risk, Insurance, and the Welfare State. University of Chicago Press.
[54]
Jill Gaulding. 1994. Race, sex and genetic discrimination in insurance: What’s fair?Cornell Law Review 80 (1994), 1646.
[55]
Ackerlof George A.1970. The market for lemons: Quality uncertainty and the market mechanism. The Quarterly Journal of Economics 84, 3 (1970), 488–500.
[56]
Gerd Gigerenzer, Zeno Swijtink, Theodore Porter, Lorraine Daston, and Lorenz Kruger. 1989. The empire of chance: How probability changed science and everyday life. Cambridge University Press.
[57]
Brian J. Glenn. 2003. Postmodernism: the basis of insurance. Risk Management and Insurance Review 6, 2 (2003), 131–143.
[58]
Brian J. Glenn. 2003. Risk, Insurance, and the Changing Nature of Mutual Obligation. Law & Social Inquiry 28, 1 (2003), 295–314.
[59]
Ben Green. 2022. Escaping the impossibility of fairness: From formal to substantive algorithmic fairness. Philosophy & Technology 35 (2022). Article number: 90.
[60]
Francisca Grommé and Stephan Scheel. 2020. Doing statistics, enacting the nation: The performative powers of categories. Nations and nationalism 26, 3 (2020), 576–593.
[61]
Ella Hafermalz, Kai Riemer, and Sebastian Boell. 2016. Enactment or performance? A non-dualist reading of Goffman. In Beyond Interpretivism? New Encounters with Technology and Organization: IFIP WG 8.2 Working Conference on Information Systems and Organizations, IS&O 2016. Springer, Cham, 167–181.
[62]
Alan Hájek. 2007. The reference class problem is your problem too. Synthese 156 (2007), 563–585.
[63]
Moritz Hardt, Meena Jagadeesan, and Celestine Mendler-Dünner. 2022. Performative power. In Advances in Neural Information Processing Systems, Vol. 35. 22969–22981.
[64]
Moritz Hardt, Eric Mazumdar, Celestine Mendler-Dünner, and Tijana Zrnic. 2023. Algorithmic Collective Action in Machine Learning. In Proceedings of the 40th International Conference on Machine Learning.
[65]
Uri Hasson, Samuel A. Nastase, and Ariel Goldstein. 2020. Direct fit to nature: An evolutionary perspective on biological and artificial neural networks. Neuron 105, 3 (2020), 416–434.
[66]
Hoda Heidari, Michele Loi, Krishna P. Gummadi, and Andreas Krause. 2019. A moral framework for understanding fair ML through economic models of equality of opportunity. In Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency. 181–190.
[67]
Carol Anne Heimer. 1985. Reactive risk and rational action: Managing moral hazard in insurance contracts. University of California Press.
[68]
Antonio J. Heras, Pierre-Charles Pradier, and David Teira. 2020. What was fair in actuarial fairness?History of the Human Sciences 33, 2 (2020), 91–114.
[69]
Benedikt Höltgen and Robert C. Williamson. 2023. On the Richness of Calibration. In Proceedings of the 2023 Conference on Fairness, Accountability, and Transparency. 1124–1138.
[70]
Lily Hu and Yiling Chen. 2018. A short-term intervention for long-term fairness in the labor market. In Proceedings of the 2018 World Wide Web Conference. 1389–1398.
[71]
Lily Hu and Issa Kohler-Hausmann. 2020. What’s sex got to do with machine learning?. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency.
[72]
Robert Huseby. 2016. Can Luck Egalitarianism Justify the Fact that Some are Worse Off than Others?Journal of Applied Philosophy 33, 3 (2016), 259–269.
[73]
Ben Hutchinson and Margaret Mitchell. 2019. 50 Years of Test (Un)fairness: Lessons for Machine Learning. In Proceedings of the Conference on Fairness, Accountability, and Transparency. 49–58.
[74]
Renée Jorgensen. 2022. Algorithms and the Individual in Criminal Law. Canadian Journal of Philosophy 52, 1 (2022), 61–77.
[75]
Matthew Joseph, Michael Kearns, Jamie H. Morgenstern, and Aaron Roth. 2016. Fairness in learning: Classic and contextual bandits. In Advances in Neural Information Processing Systems. Vol. 29.
[76]
Atoosa Kasirzadeh. 2022. Algorithmic Fairness and Structural Injustice: Insights from Feminist Political Philosophy. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (Oxford, United Kingdom) (AIES ’22). Association for Computing Machinery, New York, NY, USA, 349–356.
[77]
Niki Kilbertus, Mateo Rojas Carulla, Giambattista Parascandolo, Moritz Hardt, Dominik Janzing, and Bernhard Schölkopf. 2017. Avoiding discrimination through causal reasoning. In Advances in neural information processing systems, Vol. 30.
[78]
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan. 2017. Inherent Trade-Offs in the Fair Determination of Risk Scores. In 8th Innovations in Theoretical Computer Science Conference (ITCS 2017), Vol. 67. 43:1–43:23.
[79]
Carl Knight. 2013. Luck egalitarianism. Philosophy Compass 8, 10 (2013), 924–934.
[80]
Greta R. Krippner. 2023. Unmasked: A History of the Individualization of Risk. Sociological Theory (2023), 83–104.
[81]
Greta R. Krippner and Daniel Hirschman. 2022. The person of the category: the pricing of risk and the politics of classification in insurance and credit. Theory and Society 51, 5 (2022), 685–727.
[82]
Matthias Kuppler, Christoph Kern, Ruben L Bach, and Frauke Kreuter. 2021. Distributive justice and fairness metrics in automated decision-making: How much overlap is there?arXiv preprint arXiv:2105.01441 (2021).
[83]
Xavier Landes. 2013. The normative foundations of (social) insurance: Technology, social practices and political philosophy. (2013). https://www.centroeinaudi.it/images/abook_file/WP-LPF_6_2013_Landes.pdf Accessed on June 14, 2023.
[84]
Xavier Landes. 2015. How fair is actuarial fairness?Journal of Business Ethics 128 (2015), 519–533.
[85]
Xavier Landes and Nils Holtug. 2015. Insurance, Equality and the Welfare State: Political Philosophy and (of) Public Insurance. Res Publica 21 (2015), 111–118.
[86]
Sharon M. Lee. 1993. Racial classifications in the US Census: 1890–1990. Ethnic and Racial Studies 16, 1 (1993), 75–94.
[87]
Turo-Kimmo Lehtonen and Jyri Liukko. 2011. The forms and limits of insurance solidarity. Journal of Business Ethics 103 (2011), 33–44.
[88]
Turo-Kimmo Lehtonen and Jyri Liukko. 2015. Producing solidarity, inequality and exclusion through insurance. Res publica 21, 2 (2015), 155–169.
[89]
Turo-Kimmo Lehtonen and Ine Van Hoyweghen. 2014. Editorial: Insurance and the Economization of Uncertainty Journal. Journal of Cultural Economy 7, 4 (2014), 532–540.
[90]
Kasper Lippert-Rasmussen. 2015. Genetic discrimination and health insurance. Res Publica 21, 2 (2015), 185–199.
[91]
Lydia T. Liu, Sarah Dean, Esther Rolf, Max Simchowitz, and Moritz Hardt. 2018. Delayed impact of fair machine learning. In International Conference on Machine Learning. 3150–3158.
[92]
Jyri Liukko. 2010. Genetic discrimination, insurance, and solidarity: an analysis of the argumentation for fair risk classification. New Genetics and Society 29, 4 (2010), 457–475.
[93]
Michele Loi and Markus Christen. 2021. Choosing how to discriminate: Navigating ethical trade-offs in fair algorithmic design for the insurance sector. Philosophy & Technology 34 (2021), 967–992.
[94]
Deborah Lupton. 2016. The diverse domains of quantified selves: self-tracking modes and dataveillance. Economy and Society 45, 1 (2016), 101–122.
[95]
Donald MacKenzie. 2008. An engine, not a camera: How financial models shape markets. MIT Press.
[96]
Donald MacKenzie, Fabian Muniesa, and Leung-Sea Siu (Eds.). 2008. Do economists make markets?: on the performativity of economics. Princeton University Press.
[97]
Uskali Mäki. 2013. Performativity: Saving Austin from MacKenzie. In EPSA11 perspectives and foundational problems in philosophy of science. 443–453.
[98]
Liz McFall. 2011. A ‘good, average man’: Calculation and the limits of statistics in enrolling insurance customers. The Sociological Review 59, 4 (2011), 661–684.
[99]
Liz McFall. 2019. Personalizing solidarity? The role of self-tracking in health insurance pricing. Economy and Society 48, 1 (2019), 52–76.
[100]
Liz McFall, Gert Meyers, and Ine Van Hoyweghen. 2020. Editorial: The personalisation of insurance: Data, behaviour and innovation. Big Data & Society 7, 2 (2020), 1–11.
[101]
Liz McFall and Liz Moor. 2018. Who, or what, is insurtech personalizing?: persons, prices and the historical classifications of risk. Distinktion: journal of social theory 19, 2 (2018), 193–213.
[102]
Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan. 2021. A survey on bias and fairness in machine learning. ACM Computing Surveys (CSUR) 54, 6 (2021), 1–35.
[103]
Andrea Mennicken and Wendy Nelson Espeland. 2019. What’s new with numbers? Sociological approaches to the study of quantification. Annual Review of Sociology 45 (2019), 223–245.
[104]
Gert Meyers and Ine Van Hoyweghen. 2018. Enacting actuarial fairness in insurance: From fair discrimination to behaviour-based fairness. Science as Culture 27, 4 (2018), 413–438.
[105]
M. A. Milevsky, S. D. Promislow, and V. R. Young. 2006. Killing the law of large numbers: Mortality risk premiums and the sharpe ratio. Journal of Risk and Insurance 73, 4 (2006), 673–686.
[106]
Michael J. Miller. 2009. Disparate impact and unfairly discriminatory insurance rates. In Casualty Actuarial Society E-Forum, Winter 2009.
[107]
Shira Mitchell, Eric Potash, Solon Barocas, Alexander D’Amour, and Kristian Lum. 2021. Algorithmic fairness: Choices, assumptions, and definitions. Annual Review of Statistics and Its Application 8 (2021), 141–163.
[108]
Annemarie Mol. 2002. The body multiple: Ontology in medical practice. Duke University Press.
[109]
Liz Moor and Celia Lury. 2018. Price and the person: Markets, discrimination, and personhood. Journal of Cultural Economy 11, 6 (2018), 501–513.
[110]
G. Cristina Mora. 2014. Making Hispanics: How Activists, Bureaucrats, and Media Constructed a New American. University of Chicago Press.
[111]
Juan Perdomo, Tijana Zrnic, Celestine Mendler-Dünner, and Moritz Hardt. 2020. Performative prediction. In International Conference on Machine Learning. 7599–7609.
[112]
Alois Pichler. 2014. Insurance pricing under ambiguity. European Actuarial Journal 4, 2 (2014), 335–364.
[113]
Barbara Prainsack and Ine Van Hoyweghen. 2020. Shifting solidarities: Personalisation in insurance and medicine. Shifting solidarities: Trends and developments in European societies (2020), 127–151.
[114]
Jarkko Pyysiäinen, Darren Halpin, and Andrew Guilfoyle. 2017. Neoliberal governance and ‘responsibilization’ of agents: reassessing the mechanisms of responsibility-shift in neoliberal discursive environments. Distinktion: Journal of Social Theory 18, 2 (2017), 215–235.
[115]
Tim Räz. 2021. Group fairness: Independence revisited. In Proceedings of the 2021 Conference on Fairness, Accountability, and Transparency. 129–137.
[116]
Lisa Rebert and Ine Van Hoyweghen. 2015. The right to underwrite gender: The goods & services directive and the politics of insurance pricing. Tijdschrift Voor Genderstudies 18, 4 (2015), 413–431.
[117]
Florian Rechfeld. 2016. Personalised Genetic Testing and Its Impact to Insurance. Swiss Re (2016). https://www.swissre.com/dam/jcr:24995a5d-5b66-42ea-a2b9-660458bc6e26/Personalised_genetic_testing_and_its_impact_to_insurance.pdf Accessed on June 14, 2023.
[118]
Hans Reichenbach. 1949. The Theory of Probability: An Inquiry Into the Logical and Mathematical Foundations of the Calculus of Probability. University of California Press.
[119]
Pierre Rosanvallon. 2000. The New Social Question: Rethinking the Welfare State. Princeton University Press.
[120]
Pola Schwöbel and Peter Remmers. 2022. The Long Arc of Fairness: Formalisations and Ethical Discourse. In Proceedings of the 2022 Conference on Fairness, Accountability, and Transparency. 2179–2188.
[121]
Andrew D. Selbst, Danah Boyd, Sorelle A. Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. Fairness and Abstraction in Sociotechnical Systems. In Proceedings of the 2019 Conference on Fairness, Accountability, and Transparency. Association for Computing Machinery, 59–68.
[122]
Ronen Shamir. 2008. The age of responsibilization: On market-embedded morality. Economy and Society 37, 1 (2008), 1–19.
[123]
Tamar Sharon. 2017. Self-tracking for health and the quantified self: Re-articulating autonomy, solidarity, and authenticity in an age of personalized healthcare. Philosophy & Technology 30, 1 (2017), 93–121.
[124]
Deborah A. Stone. 2001. Ad Missions. The American Prospect. https://prospect.org/health/ad-missions/ Accessed on May 17, 2023.
[125]
Rick Swedloff. 2014. Risk classification’s big data (r) evolution. Connecticut Insurance Law Journal 143 (2014), 339–373.
[126]
Maiju Tanninen. 2020. Contested technology: Social scientific perspectives of behaviour-based insurance. Big Data & Society 7, 2 (2020), 1–14.
[127]
Yves Thiery and Caroline Van Schoubroeck. 2006. Fairness and equality in insurance classification. The Geneva Papers on Risk and Insurance-Issues and Practice 31, 2 (2006), 190–211.
[128]
Ine Van Hoyweghen. 2014. On the politics of calculative devices: performing life insurance markets. Journal of Cultural Economy 7, 3 (2014), 334–352.
[129]
Ine Van Hoyweghen. 2018. Genomics and insurance: The lock-in effects of a politics of genetic solidarity. In Handbook of Genomics, Health and Society. Routledge, 203–211.
[130]
Ine Van Hoyweghen, Klasien Horstman, and Rita Schepers. 2006. Making the normal deviant: The introduction of predictive medicine in life insurance. Social Science & Medicine 63, 5 (2006), 1225–1235.
[131]
Ine Van Hoyweghen, Klasien Horstman, and Rita Schepers. 2007. Genetic ‘risk carriers’ and lifestyle ‘risk takers’. Which risks deserve our legal protection in insurance?Health Care Analysis 15 (2007), 179–193.
[132]
John Venn. 1876. The Logic of Chance. MacMillan.
[133]
Roel Verbelen, Katrien Antonio, and Gerda Claeskens. 2018. Unravelling the predictive power of telematics data in car insurance pricing. Journal of the Royal Statistical Society: Series C (Applied Statistics) 67, 5 (2018), 1275–1304.
[134]
Ed Vosselman. 2014. The ‘performativity thesis’ and its critics: Towards a relational ontology of management accounting. Accounting and Business Research 44, 2 (2014), 181–203.
[135]
Kate Vredenburgh. 2022. Fairness. In The Oxford Handbook of AI Governance. Oxford University Press.
[136]
Michael A. Walters. 1981. Risk classification standards. In Proceedings of the Casualty Actuarial Society, Vol. 68. 1–18.
[137]
David Wilkie. 1997. Mutuality and solidarity: assessing risks and sharing losses. Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352, 1357 (1997), 1039–1044.
[138]
Jon Williamson. 2004. A dynamic interaction between machine learning and the philosophy of science. Minds and Machines 14, 4 (2004), 539–549.
[139]
Xi Xin and Fei Huang. 2023. Antidiscrimination Insurance Pricing: Regulations, Fairness Criteria, and Models. North American Actuarial Journal (2023), 1–35.

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  • (2024)Fairness: plurality, causality, and insurabilityEuropean Actuarial Journal10.1007/s13385-024-00387-314:2(317-328)Online publication date: 19-Jun-2024

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FAccT '24: Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency
June 2024
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ISBN:9798400704505
DOI:10.1145/3630106
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  2. fair machine learning
  3. insurance
  4. responsibility
  5. statistics

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  • (2024)Fairness: plurality, causality, and insurabilityEuropean Actuarial Journal10.1007/s13385-024-00387-314:2(317-328)Online publication date: 19-Jun-2024

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