Model Explainability, Revisited: SHAP and Beyond

The rapid rise of large language models has dominated much of the conversation around AI in recent months—which is understandable, given LLMs’ novelty and the speed of their integration into the daily workflows of data science and ML professionals.

Longstanding concerns around the performance of models and the risks they pose remain crucial, however, and explainability is at the core of these questions: how and why do models produce the predictions they offer us? What’s inside the black box?

This …

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