Thermodynamics-inspired Explanations of Artificial Intelligence (2206.13475v3)

Abstract: In recent years, predictive machine learning methods have gained prominence in various scientific domains. However, due to their black-box nature, it is essential to establish trust in these models before accepting them as accurate. One promising strategy for assigning trust involves employing explanation techniques that elucidate the rationale behind a black-box model's predictions in a manner that humans can understand. However, assessing the degree of human interpretability of the rationale generated by such methods is a nontrivial challenge. In this work, we introduce interpretation entropy as a universal solution for assessing the degree of human interpretability associated with 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 (TERP), a method for generating accurate, and human-interpretable explanations for black-box predictions in a model-agnostic manner. To demonstrate the wide-ranging applicability of TERP, we successfully employ it to explain various black-box model architectures, including deep learning Autoencoders, Recurrent Neural Networks, and Convolutional Neural Networks, across diverse domains such as molecular simulations, text, and image classification.

Insights from "Thermodynamics-inspired Explanations of Artificial Intelligence"

The paper "Thermodynamics-inspired Explanations of Artificial Intelligence" by Shams Mehdi and Pratyush Tiwary proposes a novel method for interpreting the predictions made by black-box AI models. Current AI models often operate as opaque systems, which complicates trust-building and limits understanding of their prediction mechanisms. This work introduces the concept of "interpretation entropy," inspired by classical thermodynamics, to assess the human interpretability of AI models, advancing towards more reliable AI explanations.

Summary and Methodology

The paper tackles the problem of AI interpretability by presenting a concept called Thermodynamics-inspired Explainable Representations of AI and other black-box Paradigms (TERP). The core idea of TERP is to assess AI model explanations by balancing their interpretation entropy and unfaithfulness through a thermodynamics-inspired framework. In this context, interpretation entropy quantifies the human interpretability of a model, while unfaithfulness measures the deviation from ground truth. By introducing a tunable parameter analogous to thermodynamic temperature, TERP identifies optimal explanations as those that minimize a trade-off between interpretation entropy and unfaithfulness.

Key features of this approach involve:

1. Interpretation Entropy: Defined using Shannon entropy, it gauges the interpretability of model weights considered as a probability distribution. Low entropy indicates high human interpretability.
2. Unfaithfulness: Measured by the correlation coefficient between the linear approximation of a black-box model and its predictions, ensuring the explanation remains close to the ground truth.
3. Free Energy Analogy: The unfaithfulness-entropy trade-off is analogous to entropy-energy trade-offs in thermodynamics, enabling the tuning of parameter $\theta$ akin to temperature to identify the 'equilibrium' explanation.

Results and Applications

To establish the methodology's efficacy, TERP was applied across various domains:

• Molecular Dynamics (MD) Simulations: Through VAMPnets, a widely used AI model for understanding molecular trajectories. TERP's application illustrated its power in identifying significant molecular features that drive model predictions, offering insights consistent with established scientific findings on protein dynamics.
• Image Classification: TERP explained predictions made by Vision Transformers (ViTs) on the CelebA dataset. Compared with baseline methods like saliency maps, TERP provided interpretable elucidations of the model's decision-making process, validated through sanity checks involving model and data randomization.
• Text Classification: It was utilized to interpret an Att-BLSTM model's categorization of news articles, highlighting critical keyword contributions to predictions.

Implications and Future Directions

The introduction of interpretation entropy offers a promising dimension to evaluate AI interpretability, grounded in the robust principles of thermodynamics. Practically, this method allows researchers to select explanations that offer the best trade-off between accuracy and interpretability, making AI systems more transparent and trustworthy.

Furthermore, the approach could significantly impact fields relying heavily on black-box models by providing system-specific interpretability. In molecular simulations, for example, understanding conformational dynamics could become more intuitive, enhancing insights about protein folding or drug interactions. As AI models become more pervasive in high stake environments, incorporating interpretability techniques like TERP could become integral to model deployment strategies.

In future research, it would be beneficial to extend the scope of TERP to non-linear models and investigate its adaptability across other forms of data, such as auditory signals or hybrid data types. This work sets a paradigm where interpretability frameworks grounded in physical sciences are leveraged to demystify complex AI systems, paving the way for reliable and explainable artificial intelligence.