Decoding the Black Box: Learning From AI’s Reasoning

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Author: Denis Avetisyan

A new perspective argues that understanding how machine learning models arrive at conclusions is key to unlocking advancements across scientific discovery, optimization, and trustworthy AI systems.

This study categorizes applications of explainable AI across discovery, optimization, and certification-fields spanning science, engineering, and auditing-and ranks them by application risk and the feasibility of generating explanations, illustrated through examples like fluid mechanics in vehicle aerodynamic design, where methods such as <span class="katex-eq" data-katex-display="false"> \text{SHAP} </span> and <span class="katex-eq" data-katex-display="false"> \text{LIME} </span> are particularly relevant.
This study categorizes applications of explainable AI across discovery, optimization, and certification-fields spanning science, engineering, and auditing-and ranks them by application risk and the feasibility of generating explanations, illustrated through examples like fluid mechanics in vehicle aerodynamic design, where methods such as \text{SHAP} and \text{LIME} are particularly relevant.

This review explores the intersection of explainable AI and causal inference, outlining how extracting mechanistic insights from foundation models can enable more effective human-AI collaboration and model certification.

Despite recent advances where artificial intelligence surpasses human performance in specialized tasks, the internal workings of these systems often remain opaque, hindering true understanding and broader application. In this Perspective, ‘Explainable AI: Learning from the Learners’, we propose that combining explainable AI (XAI) with causal reasoning unlocks the potential to learn from these powerful models themselves. This approach facilitates extracting underlying mechanisms, guiding robust design and optimization, and fostering trust in high-stakes applications across scientific discovery and certification. Can XAI ultimately serve as a unifying framework to enable more effective and collaborative human-AI partnerships in science and engineering?

The Illusion of Intelligence: Peering Inside the Black Box

The increasing prevalence of machine learning, especially Deep Neural Networks, is paradoxically hampered by a fundamental limitation: a lack of transparency. While these models achieve remarkable performance in areas like image recognition and natural language processing, the complex, multi-layered architecture often obscures how those results are generated. This ‘black box’ nature isn’t merely an academic concern; it actively hinders the adoption of these technologies in critical applications such as healthcare, finance, and autonomous vehicles. Without understanding the reasoning behind a model’s decisions, validating its reliability, or identifying potential biases becomes exceptionally difficult, fostering distrust and limiting its potential impact. Consequently, research is increasingly focused not only on improving accuracy, but also on developing methods to illuminate the internal workings of these powerful, yet enigmatic, systems.

The inherent opacity of complex machine learning models presents significant challenges to ensuring their dependable application, especially when faced with novel situations outside of their initial training parameters. Without clear insight into the decision-making process, identifying the root cause of errors becomes exceedingly difficult, hindering effective debugging and refinement. More critically, this ‘black box’ nature obscures potential biases embedded within the algorithms, raising concerns about fairness and equity in consequential applications like loan approvals or criminal justice. Consequently, assessing the reliability of predictions when extrapolating beyond familiar data is severely compromised, demanding robust methods for interpretability and validation before widespread deployment can be confidently realized.

Combining AI performance evaluation with explainability-based auditing-rather than relying solely on performance-provides a more robust certification framework by enabling traceability, understanding of failure modes, and proactive mitigation of potential biases or unsafe behavior.
Combining AI performance evaluation with explainability-based auditing-rather than relying solely on performance-provides a more robust certification framework by enabling traceability, understanding of failure modes, and proactive mitigation of potential biases or unsafe behavior.

Unveiling the Gears: The Rise of Explainable AI

Explainable AI (XAI) encompasses a collection of methodologies designed to address the lack of transparency inherent in many machine learning models. Traditionally, complex models – particularly deep neural networks – function as “black boxes,” providing predictions without revealing the reasoning behind them. XAI techniques aim to open these black boxes by providing human-understandable explanations of model behavior. These methods vary in their approach, ranging from feature importance analysis – identifying which input variables most strongly influence predictions – to the visualization of internal model states and the generation of counterfactual explanations – demonstrating how input changes would alter the outcome. The goal of XAI is not necessarily to simplify the model itself, but to create tools that allow humans to understand, trust, and effectively debug and improve these complex systems.

SHAP (SHapley Additive exPlanations) and Integrated Gradients are feature attribution methods used to determine the impact of each input feature on a model’s prediction. SHAP utilizes concepts from game theory to assign each feature a value representing its contribution to the prediction, considering all possible feature combinations. Integrated Gradients calculates the gradient of the prediction with respect to each feature along a path from a baseline input (e.g., all zeros) to the actual input, effectively accumulating the change in prediction attributable to each feature. Both techniques output a quantitative score for each feature, allowing users to identify which features are most influential in driving the model’s output for a specific instance, and thus understand the model’s decision-making process.

Autoencoders are a type of neural network trained to reconstruct input data from a compressed, lower-dimensional representation. This compressed representation, known as the Latent Space, captures the most salient features of the input data. Standard autoencoders learn a deterministic mapping to this Latent Space. β-Variational Autoencoders (β-VAEs) extend this by enforcing a probabilistic distribution on the Latent Space, and introducing a hyperparameter, β, to control the balance between reconstruction accuracy and the disentanglement of learned features. Higher β values encourage the Latent Space to be more organized and interpretable, effectively isolating individual factors of variation within the data; analysis of the Latent Space dimensions then reveals which features the model considers most important for its internal representations and subsequent predictions.

Autoencoders can learn a low-dimensional latent space parameterized by variables like θ and <span class="katex-eq" data-katex-display="false">\dot{\theta}</span> from dynamic systems-such as a swinging pendulum-enabling the discovery of parsimonious models of their behavior.
Autoencoders can learn a low-dimensional latent space parameterized by variables like θ and \dot{\theta} from dynamic systems-such as a swinging pendulum-enabling the discovery of parsimonious models of their behavior.

Beyond Correlation: Dissecting Mechanisms with XAI

Causal inference builds upon Explainable AI (XAI) by moving beyond the identification of correlations to the determination of underlying causal relationships. Traditional XAI methods often highlight features strongly associated with a prediction, but these associations may not reflect true cause-and-effect. Causal inference techniques allow for the formalization of interventions – simulating “what if” scenarios – to assess how changes in one variable directly impact another. This is achieved through methods like do-calculus and structural causal models, which enable researchers to distinguish between correlation and causation by accounting for confounding variables and identifying the mechanisms through which causal effects occur. By explicitly modeling these causal pathways, these tools provide a more robust and reliable understanding of system behavior than correlation-based explanations alone.

Symbolic Regression and Sparse Identification of Nonlinear Dynamics (SINDy) are techniques used to discover mathematical expressions that model the underlying relationships within a dataset. Unlike traditional regression which aims to predict an outcome, these methods seek to identify the functional form of the equations governing a system’s behavior. Symbolic Regression utilizes evolutionary algorithms to search for equations composed of standard mathematical operators and constants that best fit the data, while SINDy leverages sparsity-promoting regularization to identify a minimal set of terms in a pre-defined library of functions. The output of both methods is a concise, interpretable mathematical model – typically in the form of an equation like \frac{dx}{dt} = f(x, y, z) – that describes the system’s dynamics, allowing for analysis and prediction beyond the training data.

Scientific Machine Learning leverages techniques like Symbolic Regression and SINDy to identify the underlying governing equations of a system directly from observed data. This approach circumvents the traditional requirement for researchers to manually construct or hypothesize mathematical models based on domain expertise. By employing algorithms to search for mathematical expressions that best fit the data, these methods automate model discovery, potentially revealing previously unknown relationships and simplifying complex system representations. The resulting equations, often expressed in a compact and interpretable form, describe the dynamics of the system and can be used for prediction, simulation, and control without relying on pre-defined model structures.

A framework combining foundation models, <span class="katex-eq" data-katex-display="false">eta</span>-VAEs, latent diffusion, causal analysis (using SHAP), and LLM-based agents enables autonomous discovery and optimization of fluid dynamics by mapping flow conditions to a disentangled latent space and connecting physical regions with latent variables.
A framework combining foundation models, eta-VAEs, latent diffusion, causal analysis (using SHAP), and LLM-based agents enables autonomous discovery and optimization of fluid dynamics by mapping flow conditions to a disentangled latent space and connecting physical regions with latent variables.

From Prediction to Understanding: The Real Promise of AI

The pursuit of artificial intelligence is increasingly focused on not just what happens, but why. Combining Explainable AI (XAI) with causal discovery techniques represents a pivotal shift from predictive modeling to mechanistic understanding. Traditional machine learning excels at identifying correlations, yet often fails when faced with interventions or shifts in underlying conditions. By integrating methods that infer causal relationships – determining which variables directly influence others – systems can move beyond simply forecasting outcomes to explaining how those outcomes arise. This deeper comprehension is critical for reliable decision-making, particularly in complex domains where spurious correlations can lead to flawed strategies. Consequently, models built on causal foundations are demonstrably more robust, adaptable, and trustworthy, offering a pathway towards truly intelligent systems capable of navigating uncertainty and driving meaningful progress across diverse scientific and practical applications.

Foundation Models, while powerful, often operate as ‘black boxes,’ limiting trust and hindering application in critical domains. Integrating Explainable AI (XAI) and causal discovery techniques directly addresses this limitation, bolstering both the capabilities and trustworthiness of these models. This synergy is particularly impactful in Rare Event Analysis – fields like predicting financial crises, extreme weather events, or disease outbreaks – where data is scarce and accurate forecasting is paramount. By not simply predicting that a rare event will occur, but revealing why, these integrated models offer actionable insights and enable more robust risk mitigation strategies. The ability to discern causal factors, rather than mere correlations, dramatically improves the reliability of predictions and facilitates informed decision-making, ultimately unlocking the full potential of Foundation Models in high-stakes scenarios.

Agentic artificial intelligence represents a paradigm shift in scientific exploration, moving beyond human-directed inquiry to enable autonomous investigation of complex systems. By integrating explainable AI and causal discovery techniques, these agents aren’t simply predicting outcomes; they are actively formulating hypotheses, designing experiments – whether simulations or real-world data collection – and interpreting results to refine their understanding. This capability promises to dramatically accelerate scientific discovery across diverse fields, from materials science and drug development to climate modeling and fundamental physics. The potential lies in an AI capable of independently navigating the vast landscape of possibilities, identifying crucial relationships, and ultimately unlocking new knowledge at a pace previously unattainable, potentially revealing insights hidden within the noise of complex data and pushing the boundaries of human comprehension.

The pursuit of Explainable AI, as detailed in the paper, feels less like building a fortress of understanding and more like constructing an elaborate sandcastle against the inevitable tide of production realities. The ambition to extract underlying mechanisms from foundation models-to move beyond correlation and towards causal inference-is admirable, yet history suggests every abstraction eventually succumbs to unforeseen edge cases. As Claude Shannon observed, “The most important thing in communication is to convey the meaning, not just the information.” This resonates deeply; simply having a model explain itself isn’t enough. The explanation must be genuinely meaningful, robust enough to survive scrutiny, and, ultimately, useful in the face of the chaos inherent in real-world application. It’s a fleeting victory, perhaps, but a beautiful one nonetheless.

What’s Next?

The ambition to not just use these learning machines, but to actually learn from them, is predictably ambitious. This paper correctly identifies the need to move beyond post-hoc interpretability – essentially, elaborate debugging – and towards genuinely understanding the mechanisms these models have stumbled upon. The coupling with causal inference is logical, if only because correlation, as every statistician knows, is a beautifully consistent liar. The real challenge, of course, will be scaling this. A neat explanation for a toy dataset is charming; a certified explanation for a foundation model controlling critical infrastructure? Less so.

The pursuit of ‘model certification’ feels particularly fraught. It implies a level of formal verification that software engineering abandoned decades ago as impractical. One suspects the goal isn’t truly guaranteed correctness, but rather, ‘good enough’ assurance to shift liability. It’s the same mess, just more expensive. And latent space exploration, while intellectually appealing, risks becoming another black box, only with fancier visualizations.

Ultimately, this work is a reminder that we don’t write code – we leave notes for digital archaeologists. The question isn’t whether these explanations are true, but whether they’re useful to the next generation attempting to salvage what remains. If a system crashes consistently, at least it’s predictable. A beautifully explained, subtly flawed system? That’s the kind of legacy that haunts the server room.

Original article: https://arxiv.org/pdf/2601.05525.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-01-12 08:48