Local Interpretable Model-agnostic Explanations: explains one prediction by fitting a simple interpretable model to the black box's behaviour on perturbed samples around the input 1; vulnerable to off-manifold manipulation.
- Developed in
- ch. 16, Feature attribution: SHAP, LIME and integrated gradients
- Chapters
- ch. 16, Fairness & XAI
- Contrast with
- SHAP
- Source
- 1 numbered reference, listed below
Where it is used
One chapter of the Body of Knowledge uses the term. Each link opens the first section that does.
Mentions as a table
| Chapter | Mentions | Cross-referenced |
|---|---|---|
| 00 Preface | 0 | No |
| 01 Definition | 0 | No |
| 02 Why Now | 0 | No |
| 03 Values & Principles | 0 | No |
| 04 The Stack | 0 | No |
| 05 Patterns | 0 | No |
| 06 The Role | 0 | No |
| 07 Maturity Model | 0 | No |
| 08 Regulatory Map | 0 | No |
| 10 Reading List | 0 | No |
| 11 AI Defined | 0 | No |
| 12 Governance Program | 0 | No |
| 13 Risk Management | 0 | No |
| 14 Development | 0 | No |
| 15 Deployment | 0 | No |
| 16 Fairness & XAI | 3 | Yes |
| 17 Incidents | 0 | No |
| 18 EU AI Act | 0 | No |
| 19 Privacy & AI | 0 | No |
| 20 Existing Law | 0 | No |
| 21 AI Laws Worldwide | 0 | No |
| 22 Principles & Standards | 0 | No |
| 23 AI Agents | 0 | No |
- 16 · Fairness & XAI Explanation techniques 3 mentions
Sources
- [1] "'Why Should I Trust You?': Explaining the Predictions of Any Classifier" (Ribeiro, Singh and Guestrin; LIME; arXiv 1602.04938). arXiv. 2016-02-16. https://arxiv.org/abs/1602.04938 (verified: primary)
Definitions of legal terms paraphrase the cited text, which governs. Dated statements are as of .