Thermodynamics-inspired explanations of artificial intelligence

Shams Mehdi and Pratyush Tiwary ()
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Shams Mehdi: University of Maryland
Pratyush Tiwary: University of Maryland

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract In recent years, predictive machine learning models have gained prominence across various scientific domains. However, their black-box nature necessitates establishing trust in them before accepting their predictions as accurate. One promising strategy involves employing explanation techniques that elucidate the rationale behind a model’s predictions in a way that humans can understand. However, assessing the degree of human interpretability of these explanations is a nontrivial challenge. In this work, we introduce interpretation entropy as a universal solution for evaluating the human interpretability of any linear model. Using this concept and drawing inspiration from classical thermodynamics, we present Thermodynamics-inspired Explainable Representations of AI and other black-box Paradigms, a method for generating optimally human-interpretable explanations in a model-agnostic manner. We demonstrate the wide-ranging applicability of this method by explaining predictions from various black-box model architectures across diverse domains, including molecular simulations, text, and image classification.

Date: 2024
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DOI: 10.1038/s41467-024-51970-x

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