Source note2 min read
Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information
Source
arXiv
arxiv.org/abs/2608.10766v1
Source
- Source name: arXiv
- Source kind: paper
- Authors / organization: Kaivalya Rawal, Daria Onitiu, Brent Mittelstadt, Sandra Wachter, Chris Russell
- Venue: arXiv: cs.AI
- Published: 2026-08-11
- Collected: 2026-08-12
- URL: http://arxiv.org/abs/2608.10766v1
- PDF: https://arxiv.org/pdf/2608.10766v1
- DOI: UNKNOWN
- Registry group: academic_apis_and_preprints
- Evidence role: canonical_academic_index
- Language: en
- Country / region: UNKNOWN
Plain-Language Brief
The paper presents a new method called 'Rule of Thumb' (RoT) for explaining how Artificial Intelligence systems make decisions. RoT identifies the most relevant features of an AI system to predict its behavior for a specific data point. The method is suitable for various applications, including auditing opaque AI systems and using AI in scientific discovery. It meets regulatory requirements, offers a familiar interface for XAI practitioners, is model-agnostic, and faster than alternative methods. Code for the method is available.
What the Document Says
- The paper introduces 'Rule of Thumb' (RoT) explanations, a new approach to Explainable Artificial Intelligence (XAI).
- RoT identifies the most relevant features for predicting the behavior of an AI system for a specific datapoint.
- RoT is suitable for zero-shot classification using large language models (LLMs), auditing opaque AI systems, and the use of AI in scientific discovery.
- RoT meets specific requirements from leading AI regulations.
- RoT provides a familiar interface and visualisations for XAI practitioners.
- RoT is model-agnostic.
- RoT is faster than alternative methods.
- Code for RoT is available at https://github.com/KaiRawal/Rule-of-Thumb-Explaining-Artificial-Intelligence-Systems-using-Partial-Information
Key Concepts Mentioned
- Artificial Intelligence
- Explainable AI (XAI)
- Rule of Thumb (RoT)
- zero-shot classification
- large language models (LLMs)
- auditing
- scientific discovery
Claims Made by the Document
- RoT is a new approach to XAI.
- RoT identifies the most relevant features for predicting AI behavior.
- RoT is suitable for various applications.
- RoT meets regulatory requirements.
- RoT offers a familiar interface for XAI practitioners.
- RoT is model-agnostic.
- RoT is faster than alternative methods.
Evidence Used
- The paper explains the methodology and benefits of RoT
Methods / Approach
UNKNOWN
Limitations Stated by the Authors
UNKNOWN
Limitations Noted by the Agent
Agent interpretation — not from the source.
UNKNOWN
Open Questions
UNKNOWN
Notes for Future Q&A
May include interpretation. Labeled where it does.
UNKNOWN
Raw Abstract / Excerpt
Explainable Artificial Intelligence (XAI) seeks to explain how an Artificial Intelligence (AI) system arrived at a particular decision. We propose ''Rule of Thumb'' (RoT) explanations, a new approach to XAI based upon a novel formulation that identifies the most relevant features for predicting the behaviour of an AI system, for a particular datapoint. We show how RoT is well-suited to enable XAI in: (a) zero-shot classification using large language models (LLMs), (b) auditing of opaque AI systems without model access, and (c) the use of AI in scientific discovery. Additionally, RoT meets specific requirements from leading AI regulations, provides a familiar interface and visualisations for XAI practitioners, is model-agnostic, and is substantially faster than alternatives. Code available at: https://github.com/KaiRawal/Rule-of-Thumb-Explaining-Artificial-Intelligence-Systems-using-Partial-Information
