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A financial services company is developing machine learning models to automate credit risk assessments and ensure regulatory compliance. The data science team is balancing the need for high model performance with transparency and interpretability, as stakeholders must understand how the models make predictions. The team is evaluating how these factors — model transparency, interpretability, and performance — interact and affect each other. What do you suggest to the team?