Problem

Modern artificial intelligence (AI) models, particularly deep neural networks, have achieved or surpassed human performance in numerous scientific and engineering tasks. However, their internal decision-making processes and learned representations often remain opaque, presenting a significant barrier to scientific understanding, trust, and accountability. This opacity conflicts with the scientific method, which relies on transparent explanations and causal mechanisms to justify conclusions, verify results, and formulate new hypotheses. The core challenge is to effectively learn new scientific principles and causal insights from these high-performing yet "black-box" AI systems.

Method

The authors propose Explainable AI (XAI) in conjunction with causal reasoning as a unifying framework to extract meaningful insights from AI models. This framework is applied across three domains: scientific discovery, engineering optimization, and system certification. For discovery, the approach emphasizes identifying parsimonious (simple and generalizable) causal mechanisms. This involves techniques like sparse symbolic regression (e.g., Genetic Programming and Sparse Identification of Nonlinear Dynamics - SINDy) to uncover explicit governing equations. Autoencoders are utilized to identify effective low-dimensional latent coordinate systems that simplify complex dynamics. Furthermore, models are constrained to represent specific physical functions or embed symmetries (e.g., Lagrangian/Hamiltonian neural networks) to ensure physical meaningfulness. For optimization, XAI methods like SHapley Additive exPlanations (SHAP) and integrated gradients are employed to identify which input features, variables, or regions critically influence an optimization objective. Gradient-based SHAP is particularly highlighted for its ability to translate machine decisions from abstract latent spaces back into interpretable physical structures. Causal inference frameworks, such as SURD, are applied in latent spaces to reveal minimal causal structures, guiding principled intervention strategies. For certification, XAI provides mechanisms to assess if an AI model's reasoning aligns with known physics, engineering constraints, and established causal mechanisms. This involves using attribution tools to ensure models rely on physically meaningful features rather than spurious correlations. A three-pillar framework is suggested: physics-grounded explainability, stress-testing via interpretable diagnostics for out-of-domain behavior and extreme events, and explanation-driven uncertainty quantification.

Results

Through the proposed XAI framework, the paper argues for several key outcomes. In the domain of discovery, XAI enables the extraction of causal mechanisms and scientific laws from data-driven systems, potentially revealing new principles. This approach enhances the generalizability of AI models by ensuring they learn underlying mechanisms rather than mere correlations, and can identify subtle precursors to extreme events. For optimization, XAI clarifies the physical mechanisms learned by AI models, guiding more robust design and control strategies by pinpointing influential parameters and physical regions. It allows for the "inversion" of latent-space abstractions, mapping complex machine reasoning back into interpretable physical structures, which is crucial for engineering applications where optimization outcomes must translate into physical actions. In the context of certification, XAI offers transparency into the internal workings of AI systems, aiding in the identification of unphysical behaviors and guiding model redesign towards more robust and stable architectures. This allows for a transition from merely trusting a model based on its performance to trusting it through a deeper understanding of its reasoning, which is vital for safety-critical applications and liability assessment.

Implications

The paper posits that Explainable AI can transform the relationship between humans and machines, moving AI from a mere computational tool to a scientific collaborator. This new paradigm fosters a feedback loop where humans learn from machines, and both co-evolve towards more understandable and reliable models. The integration of XAI is crucial for the safe and ethical deployment of AI in high-stakes scientific and engineering applications, ensuring decisions are transparent and scientifically grounded. Future research should prioritize developing robust validation frameworks for explanations, scalable methods for causal discovery in learned latent spaces, and improving human-AI interaction to facilitate an iterative dialogue. Ultimately, this approach aims to unlock accelerated and autonomous scientific discovery and design, aligning AI systems with human values and understanding.