Fairness and Risk: An Ethical Argument for a Group Fairness Definition Insurers Can Use.

Area of Science:

• Data Science
• Insurance Analytics
• Machine Learning Ethics

Background:

• Algorithmic predictions offer personalized risk models for insurance premiums.
• Potential biases in these algorithms can lead to discrimination and social injustice against specific groups.
• Ensuring fairness in algorithmic insurance pricing is crucial to prevent systematic disadvantages.

Purpose of the Study:

• To analyze algorithmic fairness in insurance premium systems.
• To define and measure fairness in the context of insurance.
• To identify appropriate fairness criteria for assessing algorithmic bias in premiums.

Main Methods:

• Application of established fair machine learning fairness frameworks to insurance premiums.
• Evaluation of group fairness criteria such as independence (statistical/demographic parity) and separation (equalized odds).
• Proposal and justification of the sufficiency criterion (well-calibration) as a suitable alternative.

Main Results:

• Independence and separation criteria are deemed inappropriate for insurance premiums.
• The sufficiency criterion (well-calibration) is proposed as a normatively defensible method for assessing group fairness.
• The relationship between group fairness and the degree of personalization in premiums is clarified.

Conclusions:

• Insurers can use the sufficiency criterion to assess and mitigate biases in their risk models.
• Adopting fair algorithmic practices helps avoid reputational damage from discriminatory premium systems.
• This research provides a framework for developing equitable and trustworthy algorithmic insurance.