AWS Certified AI Practitioner Practice Exam

The most appropriate action for a loan company to take in order to minimize bias in a generative AI model is to detect imbalances or disparities in the data. This approach involves thoroughly analyzing the dataset to identify any unequal representation of different groups or characteristics that could lead to biased outcomes. Imbalances in the data can manifest as over-representation or under-representation of certain demographics, which can significantly influence the model's predictions and decisions. By ensuring the training data is balanced and representative, the loan company can build a more equitable model that provides fair treatment across all applicant profiles.

To effectively address bias, it is crucial to understand the underlying data from which the model learns. Identifying and mitigating these disparities helps prevent the perpetuation of existing biases that could result in discriminatory lending practices. This step not only enhances the integrity of the AI model but also aligns with ethical guidelines and regulatory requirements in the financial industry.

In comparison, the other options may contribute to overall model performance or transparency but do not specifically address the root causes of bias associated with the input data. Ensuring that the model runs frequently can improve its responsiveness but does not reduce bias. Evaluating model behavior for transparency is beneficial for understanding outputs, but transparency on its own does not eliminate bias if the