---
type: Research concept
title: Interpretability
description: Concept page for explaining model behavior and data influence.
tags:
  - en
  - research
  - concept
  - research-concept
  - llm
timestamp: '2026-05-05T20:55:21+08:00'
modified: '2026-07-02T20:03:20+08:00'
content_hash: 'sha256:218df98003e2d7abaf687efc0c1417ef55412b77887fc85e5bdb222a572e63cb'
reviewed_at: '2026-07-02T20:03:20+08:00'
review_due: '2026-12-29'
name: Interpretability
summary: Concept page for explaining model behavior and data influence.
occupation: Research concept
---
**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 wiki

Interpretability is a supporting topic for [[Trustworthy_AI|Trustworthy AI]] and [[Data_Centric_Machine_Learning|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 work

Qiao's wiki connects interpretability most directly through [[Influence_Functions|influence functions]] and unlearning. [[Hessian_Free_Online_Certified_Unlearning|Hessian-Free Online Certified Unlearning]] and [[Soft_Weighted_Machine_Unlearning|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.

## See also

- [[Influence_Functions]]
- [[Trustworthy_AI]]
- [[Machine_Unlearning]]
- [[Data_Centric_Machine_Learning|Data Centric ML]]
