Intro to Model Explainability

As businesses across sectors implement ML and AI, the need for transparent decision-making grows increasingly important. The problem with black-box models (neural networks, large language models, etc.) is that their decision process is entirely opaque and unauditable. Model explainability has evolved as a subfield to combat this problem.

Explainability vs. interpretability

Simply put, explainability attempts to provide some clarity into how an ML algorithm makes its decision. Interpretability is the ability to have clarity into why an ML algorithm made a decision. The difference is subtle but has ...