Discrimination and AI in insurance: what do people find fair? Results from a survey

TL;DR

This survey reveals that the Dutch public generally perceives modern AI-driven insurance practices as unfair, especially when they are uncontrollable or lead to discrimination, highlighting concerns about fairness and policy implications.

Contribution

This study provides empirical insights into public perceptions of AI-based insurance practices, highlighting fairness concerns and informing policy debates.

Findings

Respondents associate unfairness with practices they find unacceptable.

Most modern insurance practices are perceived as unfair.

Fairness is linked to consumer influence and avoidance of discrimination.

Abstract

Two modern trends in insurance are data-intensive underwriting and behavior-based insurance. Data-intensive underwriting means that insurers analyze more data for estimating the claim cost of a consumer and for determining the premium based on that estimation. Insurers also offer behavior-based insurance. For example, some car insurers use artificial intelligence (AI) to follow the driving behavior of an individual consumer in real-time and decide whether to offer that consumer a discount. In this paper, we report on a survey of the Dutch population (N=999) in which we asked people's opinions about examples of data-intensive underwriting and behavior-based insurance. The main results include: (i) If survey respondents find an insurance practice unfair, they also find the practice unacceptable. (ii) Respondents find almost all modern insurance practices that we described unfair. (iii) Respondents find practices for which they can influence the premium fairer. (iv) If respondents find a certain consumer characteristic illogical for basing the premium on, then respondents find using the characteristic unfair. (v) Respondents find it unfair if an insurer offers an insurance product only to a specific group. (vi) Respondents find it unfair if an insurance practice leads to the poor paying more. We also reflect on the policy implications of the findings.