Actuarial Algorithm Audits for Fairness in Disability Insurance

 

A four-panel educational comic titled 'Actuarial Algorithm Audits for Fairness in Disability Insurance'. Panel 1: A woman asks, 'What are algorithm audits?' and a man holding a folder labeled 'Audit' begins to explain. Panel 2: He continues, 'They check for bias!' next to a board listing 'Fairness Testing, Input Analysis, Bias Metrics'. Panel 3: Another man says, 'Applied to disability insurance models…' beside a board that reads 'Risk Assessment, Claims Decisions'. Panel 4: The first woman concludes, 'That prevents discrimination!'"

Actuarial Algorithm Audits for Fairness in Disability Insurance

Disability insurance is designed to provide financial protection when individuals are unable to work due to injury or illness.

As AI and machine learning become common in underwriting and claims decisions, questions of fairness and bias are becoming central to actuarial practices.

Actuarial algorithm audits are emerging as a critical tool for ensuring these models remain transparent, compliant, and just.

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Why Algorithmic Fairness Matters in Disability Insurance

AI is used in disability insurance to automate underwriting, detect fraud, and optimize claims handling.

However, if not carefully audited, these systems can unintentionally discriminate based on gender, income level, race, or disability type.

This undermines public trust and can lead to regulatory violations and legal action.

What Is an Actuarial Algorithm Audit?

An actuarial audit is a structured evaluation of models used in insurance decision-making.

When applied to algorithms, it includes:

- Input variable analysis (to identify proxy bias)

- Model explainability testing

- Performance benchmarking across demographic groups

- Scenario simulations to observe unintended outcomes

Audit Methodologies and Metrics

Effective audits use both quantitative and qualitative tools:

- Disparate Impact Ratio: Measures statistical parity between protected groups

- SHAP/ICE Analysis: Explains individual predictions and model behavior

- Bias-Variance Decomposition: Assesses model stability and generalization

- Transparency Logs: Document model updates and rationale

Regulatory & Ethical Frameworks

Key frameworks guiding fairness in actuarial AI include:

- NAIC AI Guiding Principles (U.S.)

- EU AI Act (2025)

- Actuarial Standards of Practice (ASOPs) by the American Academy of Actuaries

- OECD AI Principles for trustworthy and inclusive use of AI

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Important keywords: actuarial audit, disability insurance fairness, algorithmic bias, AI ethics, insurance regulation