Gaining clarity on algorithmic explainability is crucial for understanding AI processes. By examining the data cleansing, labeling, and algorithm choices, one can uncover potential issues that may arise. While technology provides insights into model behavior, the human element is essential for root cause explanations, especially in sensitive scenarios like loan denials, where transparency is key.
