Скачать или смотреть CPDP 2019: Insurance, algorithmic decision-making, and discrimination.

INSURANCE, ALGORITHMIC DECISION-MAKING, AND DISCRIMINATION.

Organised by CPDP

Chair: Jedrzej Niklas, University of Leeds (UK)
Moderator: Frederik Zuiderveen Borgesius, IViR Institute for Information Law (NL)
Speakers: Toon Calders, University of Antwerp (BE); Ursula Pachl, BEUC (BE); Freyja Van Den Boom, Bournemouth University (UK); Jos Schaffers, Dutch Association of Insurers (NL)

Insurance companies could use algorithmic systems to set premiums for individual consumers, or deny them insurance. More and more data become available for insurers for risk differentia- tion. For instance, some insurers monitor people’s driving behaviour to estimate risks. To some extent, risk differentiation is necessary for insurance. And it can be considered fair when high- risk drivers pay higher premiums. But there are drawbacks. Algorithmic decision-making could lead, unintentionally, to discrimination on the basis of, for instance, skin colour. And too much risk differentiation could make insurance unaffordable for some consumers, and could threaten the risk-pooling function of insurance. Furthermore, risk differentiation might result in the poor paying more. A consumer who lives in a poor neighbourhood with many burglaries might pay more for house insurance, because the risk of a burglary is higher. Hence, poor people might pay, on average, more.

• How should discrimination on the basis of, for instance, skin colour be avoided?
• Can non-discrimination norms be built in the computer systems of insurance companies?
• Are current laws sufficient to protect fairness and the right to non-discrimination in the insurance area?
• Should the law protect poor people against paying extra?
• Is it always reasonable when high-risk insurance consumers pay extra?