What is the best strategy to mitigate the bias uncovered in the loan applications?

CASE STUDY

Please use the following answer the next question:

ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.

ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model (“LLM”). In particular, ABC intends to use its historical customer data―including applications, policies, and claims―and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed a human underwriter for final review.

ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women’s loan applications due primarily to women historically receiving lower salaries than men.

What is the best strategy to mitigate the bias uncovered in the loan applications?
A . Retrain the model with data that reflects demographic parity.
B . Procure a third-party statistical bias assessment tool.
C . Document all instances of bias in the data set.
D . Delete all gender-based data in the data set.

View Answer

Answer: A

Explanation:

Retraining the model with data that reflects demographic parity is the best strategy to mitigate the bias uncovered in the loan applications. This approach addresses the root cause of the bias by ensuring that the training data is representative and balanced, leading to more equitable decision-making by the AI model.

Reference: The AIGP Body of Knowledge stresses the importance of using high-quality, unbiased training data to develop fair and reliable AI systems. Retraining the model with balanced data helps correct biases that arise from historical inequalities, ensuring that the AI system makes decisions based on equitable criteria.