SHAP

Local surrogate explanation method approximating model behavior near a specific input.

Framework for reasoning about cause-effect relationships beyond correlation, often using structural assumptions and e...

A hidden variable influences both cause and effect, biasing naive estimates of causal impact.

Controlled experiment comparing variants by random assignment to estimate causal effects of changes.

Automated detection/prevention of disallowed outputs (toxicity, self-harm, illegal instruction, etc.).

Systematic differences in model outcomes across groups; arises from data, labels, and deployment context.

Techniques to understand model decisions (global or local), important in high-stakes and regulated settings.

Studying internal mechanisms or input influence on outputs (e.g., saliency maps, SHAP, attention analysis).