Intro to Model Explainability
Explore how model explainability helps clarify machine learning decisions, distinguishing it from interpretability. Understand the challenges with black-box models like random forests and neural networks, and discover interpretable models such as logistic regression and decision trees. This lesson prepares you to evaluate and implement explainability approaches critical for high-risk domains needing transparent AI systems.
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 ...