To maintain fairness in a deployed system, it is most important to?

What is the technique to remove the effects of improperly used data from an ML system?

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 tA. 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.
The best approach to enable a customer who wants information on the Al model's parameters for underwriting purposes is to provide?

You are part of your organization's ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating.
What is the best first step address this?

An Al system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as?