Understand the trade-off between interpretability and performance

Deep learning methods are notably difficult to interpret. If data is provided without explicitly engineered features, how are you going to address finding any biases in predictions?

What sort of problem are you trying to solve with DL?
Is high performance from a DL approach worth the difficulty in explaining how the model assigns a value, or is the value in the model in understanding the biological problem at hand?