A financial firm uses a generative AI model to set credit limits and wants to make the model’s decisions more transparent to customers. Which solution satisfies this requirement?

Explainable AI (XAI) methods directly address the need for transparency by providing insights into how a model arrived at a particular decision. For credit limit decisions, XAI can highlight the specific financial factors (e.g., credit score, income, debt-to-income ratio) that most influenced the generative AI model's output, making the process understandable to customers. Replacing the ML model with a rule-based system would achieve transparency but would likely sacrifice the advanced pattern recognition capabilities and performance benefits of generative AI. Creating an interactive UI with technical descriptions would overwhelm customers without explaining the why behind the decision. Improving accuracy is always desirable but does not inherently increase transparency; a highly accurate model can still be a "black box.