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Avoiding Unfair Bias in Insurance Applications of AI Models

Authors

Logan T. Smith, ASA
Emma Pirchalski
Ilana Golbin

Description

Artificial intelligence (“AI”) adoption in the insurance industry is increasing. One known risk as adoption of AI increases is the potential for unfair bias. Central to understanding where and how unfair bias may occur in AI systems is defining what unfair bias means and what constitutes fairness.

This research identifies methods to avoid or mitigate unfair bias unintentionally caused or exacerbated by the use of AI models and proposes a potential framework for insurance carriers to consider when looking to identify and reduce unfair bias in their AI models. The proposed approach includes five foundational principles as well as a four-part model development framework with five stage gates.

Report

Avoiding Unfair Bias in Insurance Applications of AI Models

Suggested citation: Smith, L.T., E. Pirchalski, and I. Golbin. Avoiding Unfair Bias in Insurance Applications of AI Models. Society of Actuaries, August 2022.

Podcast

Research Insights - Avoiding Unfair Bias in Insurance Applications of AI Models.

Acknowledgments

The Society of Actuaries Research Institute thanks the Project Oversight Group for their input and guidance during the project and on the report.

Dorothy Andrews, ASA, CSPA, MAAA
Kelly Edmiston, PhD
Rebecca Johnson, MBA
Mengting Kim, FSA, CERA, MAAA
Min Mercer, FSA
Norman Niami, FCAS, MAAA, Affiliate IFoA
Shisheng (Rose) Qian, ASA
John Robinson, FSA, FCA, MAAA
Mark A. Sayre, FSA, CERA
Ann Weber, JD

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