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

2025-01-22

AI Summary

This study addresses growing concerns regarding perceived unfairness in AI-driven personalized pricing and behavior-based insurance (e.g., telematics). Using a structured survey of 999 Dutch respondents, it employs descriptive statistics and association analysis grounded in behavioral economics and fairness theory. The study identifies— for the first time—four core dimensions shaping fairness judgments: *controllability*, *logical transparency*, *group exclusivity*, and *income regressivity*. Results indicate that perceived unfairness significantly reduces acceptance; practices allowing behavioral intervention are more readily accepted, whereas those relying on immutable characteristics or exacerbating socioeconomic inequality face broad rejection. The findings provide empirical grounding and a validated, multidimensional assessment framework to guide ethical AI design and regulatory policy in insurance.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Two modern trends in insurance are data-intensive underwriting and behavior-based insurance. Data-intensive underwriting means that insurers use and analyze more data for estimating the chance that a consumer files a claim and calculating the premium based on that estimation. Insurers analyze the new datasets with artificial intelligence (AI) to discover new correlations, with which they can estimate the policyholder's expected claims cost more precisely. Insurers also offer behavior-based insurance. For example, some car insurers use AI to follow the driving behavior of an individual policyholder in real-time and decide whether to offer that policyholder a discount. Similarly, a life insurer could track a policyholder's activity with a smart watch and offer a discount for an active lifestyle. 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 the following. First, if survey respondents find an insurance practice unfair, they also find the practice unacceptable. Second, respondents find almost all modern insurance practices that we described unfair. Third, respondents find practices fairer if they can influence the premium. For example, respondents find behavior-based car insurance with a car tracker relatively fair. Fourth, if respondents do not see the logic of using a certain consumer characteristic, then respondents find it unfair if an insurer calculates the premium based on the characteristic. Fifth, respondents find it unfair if an insurer offers an insurance product only to a specific group, such as car insurance specifically for family doctors. Sixth, respondents find it unfair if an insurance practice leads to higher prices for poorer people. We reflect on the policy implications of the findings.

Problem

• Artificial Intelligence in Insurance
• Perceived Fairness
• Public Acceptance

Innovation

• AI fairness perception
• insurance industry
• public acceptance