arXiv:2606.14898v1 Announce Type: new Abstract: Fairness in insurance pricing remains a long-standing and deeply debated puzzle. On one hand, insurers, driven by profitability considerations, set premiums that differentiate across individual risks to achieve actuarial fairness. On the other hand, insurance serves a critical societal function by pooling risks across a population, motivating cross-subsidization among groups to promote solidarity fairness. The tension between these two competing notions of fairness makes insurance pricing inherently complex, particularly in modern settings where
The increasing sophistication of AI models for risk assessment and the growing societal debate around algorithmic fairness are converging to demand new frameworks for ethical pricing, especially in socially critical areas like insurance.
This research directly addresses the tension between actuarial profitability and social equity in insurance, presenting a novel 'fairness continuum' that could reshape how AI is applied to risk and pricing models, influencing both industry practices and regulatory frameworks.
The prior binary view of fairness in insurance is evolving towards a more nuanced continuum, allowing for a structured approach to balancing economic efficiency with social solidarity through AI-driven pricing mechanisms.
- · Insurance tech (insurtech) companies
- · Regulators focused on consumer protection
- · Actuarial science researchers
- · Underinsured populations
- · Traditional insurance models resistant to AI integration
- · Insurance companies unwilling to adapt pricing for fairness
- · Data brokers selling biased datasets
Insurance companies will begin exploring and implementing 'alpha-fair' models to optimize both profitability and social responsibility.
New regulatory guidelines may emerge, mandating or incentivizing the use of such fairness-aware AI in insurance pricing.
The concept of an 'alpha-fairness continuum' could be adopted by other sectors dealing with algorithmic discrimination, such as lending and hiring.
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Read at arXiv cs.LG