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  1. Melting contestation: insurance fairness and machine learning.Laurence Barry & Arthur Charpentier - 2023 - Ethics and Information Technology 25 (4):1-13.
    With their intensive use of data to classify and price risk, insurers have often been confronted with data-related issues of fairness and discrimination. This paper provides a comparative review of discrimination issues raised by traditional statistics versus machine learning in the context of insurance. We first examine historical contestations of insurance classification, showing that it was organized along three types of bias: pure stereotypes, non-causal correlations, or causal effects that a society chooses to protect against, are thus the main sources (...)
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  • Editorial: The personalisation of insurance: Data, behaviour and innovation.Ine Van Hoyweghen, Gert Meyers & Liz McFall - 2020 - Big Data and Society 7 (2).
    The adoption of Big Data analytics in insurance has proved controversial but there has been little analysis specifying how insurance practices are changing. Is insurance passively subject to the forces of disruptive innovation, moving away from the pooling of risk towards its personalisation or individualisation, and what might that mean in practice? This special theme situates disruptive innovations, particularly the experimental practices of behaviour-based personalisation, in the context of the practice and regulation of contemporary insurance. Our contributors argue that behaviour-based (...)
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  • Performative innovation: Data governance in China's fintech industries.Jing Wang - 2022 - Big Data and Society 9 (2).
    The financial applications of data technology have enabled the rise of Chinese fintech industries. As part of people's everyday lives, fintech apps have helped companies collect vast amounts of user data for business profit and social good. This paper takes an open-systems approach to study the constructs of this emerging idea of data governance, particularly its operational logic, involved stakeholders, and socio-cultural consequences in the context of fintech industries in China. It asserts that data governance at the company level has (...)
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  • AI, Radical Ignorance, and the Institutional Approach to Consent.Etye Steinberg - 2024 - Philosophy and Technology 37 (3):1-26.
    More and more, we face AI-based products and services. Using these services often requires our explicit consent, e.g., by agreeing to the services’ Terms and Conditions clause. Current advances introduce the ability of AI to evolve and change its own modus operandi over time in such a way that we cannot know, at the moment of consent, what it is in the future to which we are now agreeing. Therefore, informed consent is impossible regarding certain kinds of AI. Call this (...)
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  • The value of sharing: Branding and behaviour in a life and health insurance company.Liz McFall & Hugo Jeanningros - 2020 - Big Data and Society 7 (2).
    As Big Data, the Internet of Things and insurance collide, so too, do the best and the worst of our futures. Insurance is summoned as an example of the interference in our private lives that is already underway everywhere. In this paper, we pause to reflect on this argument. Can changes in the way insurance measures the value of behaviour really serve as an example of the individual and social harms of datafication? How do we know? Insurance is a mathematical (...)
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  • Why Personal Dreams Matter: How professionals affectively engage with the promises surrounding data-driven healthcare in Europe.Antoinette de Bont, Anne Marie Weggelaar-Jansen, Johanna Kostenzer, Rik Wehrens & Marthe Stevens - 2022 - Big Data and Society 9 (1).
    Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from studying how professionals in the healthcare (...)
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