Interpretabilityedit
Concept page for explaining model behavior and data influence.
Interpretability refers to methods that help people understand why a model behaves the way it does. In this wiki the emphasis is narrower than the whole interpretability field: it focuses on data influence, error diagnosis, and explanations that support trustworthiness decisions.
Role in this wikiedit
Interpretability is a supporting topic for Trustworthy AI and Data Centric ML. A model can be accurate but still difficult to audit. If a researcher can explain which examples, groups, or synthetic-data processes caused a behavior, then the next action can be data selection, unlearning, correction, or collaborative evaluation. Interpretability therefore links explanation to intervention.
Connection to Qiao's workedit
Qiao's wiki connects interpretability most directly through influence functions and unlearning. Hessian-Free Online Certified Unlearning and Beyond Binary Erasure both rely on understanding how data changes affect model parameters or predictions. The synthetic-data line also needs interpretability in a broader sense: when model collapse occurs, the research asks what process caused the degeneration and how distributed parties can detect it.