Your question is Fairness and Interpretability in Black-Box Models. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You're choosing a high-performing black-box model for a supervised learning task, but you also need to explain its behavior and check whether it treats groups differently.
How do you ensure fairness and interpretability in complex black-box models?