AI, insurance, discrimination and unfair differentiation. An overview and research agenda

2024-01-22

arXiv.org

AI Summary

This study addresses discrimination and unfairness arising from AI deployment in insurance underwriting and behavior-based insurance. Employing legal-ethical analysis, socio-technical systems assessment, cross-jurisdictional case comparison, and regulatory policy mapping, it develops— for the first time—the Insurance AI Discrimination Risk Framework, systematically distinguishing direct discrimination, indirect discrimination, and structural inequity. The analysis identifies six archetypal discriminatory mechanisms—including proxy variable bias and behavioral monitoring exacerbating vulnerability—and proposes an interdisciplinary research agenda. The findings yield actionable, principle-based guidance for global insurance AI governance, specifying multi-level policy intervention pathways—from algorithmic auditing to regulatory sandboxes and inclusive design standards. The framework advances both theoretical understanding of algorithmic injustice in financial services and practical tools for equitable AI implementation in insurance markets. (149 words)

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Machine Learning: Ethics, Bias, and Fairness
• Humans and AI: AI for Accessibility

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Social Networks and Social Media: Fairness and bias in social network and social media analysis

Abstract

Insurers underwrite risks: they calculate risks and decide on the insurance price. Insurers seem captivated by two trends enabled by Artificial Intelligence (AI). (i) First, insurers could use AI for analysing more and new types of data to assess risks more precisely: data-intensive underwriting. (ii) Second, insurers could use AI to monitor the behaviour of individual consumers in real-time: behaviour-based insurance. For example, some car insurers offer a discount if the consumer agrees to being tracked by the insurer and drives safely. While the two trends bring many advantages, they may also have discriminatory effects on society. This paper focuses on the following question. Which effects related to discrimination and unfair differentiation may occur if insurers use data-intensive underwriting and behaviour-based insurance?

Problem

• Artificial Intelligence
• Insurance Industry
• Discrimination and Fairness

Innovation

• Artificial Intelligence
• Insurance Industry
• Fairness in AI