The use of Artificial Intelligence (AI) and Machine Learning (ML) in underwriting and pricing is a major trend, but it presents ethical dilemmas.
Bias: AI models are trained on historical data, which may contain past discriminatory patterns. If left unchecked, the AI can use proxy factors (variables highly correlated with protected characteristics like race or gender) to create models that are indirectly biased and lead to unfair outcomes or pricing.
Transparency and Explainability: Insurers face the challenge of providing clear, understandable reasons for an underwriting decision (the “black box” problem), especially when a policy is denied or priced highly.