Ethics of Algorithmic Pricing in Insurance

Algorithmic Pricing and Fairness

Machine learning algorithms can identify complex patterns in data that improve pricing accuracy, but they also raise ethical concerns. Algorithms may inadvertently use proxies for protected characteristics like race or religion, producing outcomes that are actuarially sound but socially inequitable. The concept of unfair discrimination in insurance distinguishes between risk-based differentiation (generally acceptable) and differentiation based on protected classes (prohibited). The challenge lies in the gray area between these boundaries.

Principles for Ethical Use

The actuarial profession has developed guidance on ethical use of algorithms in insurance. Key principles include transparency (being able to explain how models produce their outputs), accountability (clear ownership of model decisions), fairness testing (evaluating disparate impact across demographic groups), and human oversight (maintaining meaningful human review of automated decisions). Actuaries have a professional obligation under the Code of Professional Conduct to consider the broader societal impact of their work, not just statistical accuracy.