Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information
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Abstract (arXiv)
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
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 96 references that are in the map. First posted on arXiv 11 Aug 2026.
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8 of 96 references.
References · 8
- 2013Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Mapscited by 2,371
- 2017SmoothGrad: removing noise by adding noisecited by 1,109
- 2024Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inferencecited by 554
- 2020Problems with Shapley-value-based explanations as feature importance measurescited by 200
- 2019Feature relevance quantification in explainable AI: A causal problemcited by 167
- 2019Ablation Studies in Artificial Neural Networkscited by 60
- 2024Amazing Things Come From Having Many Good Modelscited by 55
- 2023Manifold Restricted Interventional Shapley Valuescited by 9
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