Data Explainability: The Counterpart To Model Explainability

AI models have become increasingly complex, with black box models being the most popular. However, black box models are difficult to interpret and consume, leading to the black box problem. To address this, model explainability and data explainability have become important, as they improve model transparency, accuracy, and fairness. However, model explainability does not fully address the black box problem, as practitioners are unable to interpret the inner layers of the model.