Moral Authority Over Risk Classifications: How Data Professionals Shape the Uneven Algorithmization of Life Insurance

How do data professionals claim moral authority over market classifications? This article examines the adoption of machine learning (ML) algorithms in life insurance through a comparative ethnographic study of how actuaries and data scientists morally justify disability risk classifications in pricing and claims prevention. The classification work of both professionals relies on utilitarian fairness notions shaped by market contexts and professional cultures. In the publicly sensitive context of pricing, actuarial fairness guides the classification of risk into groups through transparent models, creating barriers to ML adoption. In the insulated context of claims prevention, data scientists’ moral frameworks and technical fairness metrics justify individual risk classifications that deliver equal opportunities for customers in need. By demonstrating how professional notions of fairness shape broader morals of markets, this article contributes to debates on multiple market moralities and theorizes moral authority as a key factor driving the uneven algorithmization of markets.