Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information

[Submitted on 11 Aug 2026 (v1), last revised 13 Aug 2026 (this version, v2)]

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Abstract: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.
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Submission history

From: Kaivalya Rawal [view email]
[v1] Tue, 11 Aug 2026 10:23:33 UTC (6,338 KB)
[v2] Thu, 13 Aug 2026 10:45:12 UTC (6,338 KB)