Beyond Black Boxes: A New Standard for AI Explainability

Author: Denis Avetisyan

Researchers have developed a rigorous, testable framework to move beyond intuitive notions of explainability and formally verify why AI models make the decisions they do.

The PREDICT cardiovascular risk model employs a hierarchical explanation structure-comprising leaf annotations, composition annotations, and global composition-to elucidate complex risk factors and their interrelationships.

The study introduces a method using annotated computational graphs and compositional coverage to define and assess inherent explainability in artificial intelligence.

Despite the central goal of Explainable AI (XAI), a rigorous, testable definition of inherent explainability remains elusive, often relying on subjective intuition. This paper, ‘Explanation Beyond Intuition: A Testable Criterion for Inherent Explainability’, addresses this gap by introducing a formal framework grounded in graph theory and compositional coverage, enabling verifiable decomposition and recomposition of AI models for explanation. We demonstrate that this criterion aligns with existing intuitions while providing a basis to differentiate explainable and explained models, and apply it to fully explain a clinically-used cardiovascular disease risk model. Could this approach offer a flexible yet rigorous standard for evaluating and regulating the explainability of AI systems across diverse domains?

The Imperative of Transparency: Illuminating AI Decision-Making

The increasing integration of artificial intelligence into critical sectors, most notably healthcare, necessitates a level of decision-making transparency previously uncommon in automated systems. As AI algorithms assume roles impacting patient diagnoses, treatment plans, and resource allocation, the rationale behind their outputs can no longer remain obscured. A lack of scrutiny invites potential errors to propagate unnoticed, eroding trust amongst medical professionals and, crucially, patients. Justification for AI-driven conclusions isn’t merely about understanding how a system arrived at a particular outcome, but establishing accountability and enabling effective oversight, ensuring that these powerful tools augment, rather than compromise, established standards of care and ethical practice.

Many artificial intelligence systems currently operate as “black boxes,” meaning their internal workings remain opaque even to their creators. This lack of transparency poses significant challenges, particularly as these systems are increasingly deployed in critical applications. Without understanding how an AI arrives at a decision, establishing trust becomes difficult, and accountability is severely compromised. The inability to trace the reasoning process also hinders error detection and correction; a flawed outcome cannot be readily analyzed to pinpoint the source of the problem, potentially perpetuating inaccuracies and biases. Consequently, the opaqueness of these models represents not merely a technical limitation, but a fundamental obstacle to the responsible and reliable implementation of AI in fields demanding precision and justification.

The burgeoning field of Explainable AI, or ExplainableAI, directly confronts the opacity of many modern artificial intelligence systems. Rather than simply delivering predictions, ExplainableAI techniques strive to illuminate the reasoning behind those predictions, making the decision-making process accessible to human understanding. This isn’t merely about providing a post-hoc justification; it encompasses the development of inherently interpretable models and methods for dissecting complex ‘black box’ algorithms. By revealing the factors that contribute to a specific outcome, ExplainableAI fosters trust, enables effective debugging, and facilitates responsible deployment in critical applications, ranging from medical diagnoses to financial risk assessment. The ultimate goal is to move beyond systems that simply act intelligently to those that can explain their intelligence, paving the way for true human-AI collaboration.

Formalizing Justification: A Structured Approach to Explanation

A FormalExplanation, as a foundational element of rigorous model analysis, necessitates a structured approach to justification. This involves not only articulating a claim but also defining its scope and limitations to ensure clarity. Completeness within a FormalExplanation requires the explicit statement of all necessary assumptions and preconditions that underpin the claim. Critically, the framework demands objective evaluability; that is, the explanation must be presented in a manner that allows for independent verification of its validity, typically through quantifiable metrics or demonstrable logical consistency. Without these attributes, an explanation remains subjective and unsuitable for reliable model understanding or debugging.

A formal explanation framework utilizes a HypothesisEvidenceStructure to establish logical support for claims. This structure directly parallels scientific reasoning by requiring each assertion, or hypothesis, to be substantiated with concrete evidence. Evidence can take various forms, including data points, observations, or the results of computations, and must directly relate to and support the proposed hypothesis. The strength of an explanation is therefore determined by the quality and relevance of the supporting evidence, allowing for objective evaluation and verification of the presented claims. The framework ensures traceability from hypothesis to evidence, facilitating analysis and identification of any gaps in reasoning.

Functional Graph Decomposition (FGD) facilitates the creation of formal explanations by systematically dissecting a complex model into its constituent functional graphs. Each graph represents a specific sub-function or component within the overall model, defined by nodes representing variables and edges representing functional dependencies-$f(x) = y$. This decomposition allows for the tracing of information flow and the identification of causal pathways. By analyzing the individual graphs and their interconnections, the contribution of each component to the model’s output can be determined. FGD enables the isolation of specific features or inputs and their subsequent impact assessment, thereby creating a granular and interpretable explanation of the model’s behavior. The resulting structure supports the construction of a HypothesisEvidenceStructure by linking specific functional components to observable evidence.

Achieving Complete Coverage: Deconstructing Complexity for Clarity

Complete model explanation necessitates both StructuralCoverage and CompositionalCoverage. StructuralCoverage refers to the extent to which all individual components of a model are addressed within the explanation; a fully structurally covered explanation accounts for every node, layer, or parameter. CompositionalCoverage, conversely, details the interactions between these components, explaining how they combine to produce the model’s output. Without both, explanations remain incomplete; addressing components in isolation, without outlining their relationships, fails to provide a holistic understanding of the model’s functionality, and conversely, detailing interactions without identifying the involved components lacks clarity. Achieving both coverage types is critical for building trust and facilitating debugging or refinement of the model.

SubgraphComposition facilitates complete model explanation by decomposing the system into functionally distinct subgraphs, each representing a specific unit of operation. Explanations are then generated for each subgraph individually, detailing its inputs, processes, and outputs. These individual explanations are subsequently combined to provide a holistic understanding of the entire model’s behavior. This approach allows for modularity and scalability in explanation generation, as complexity is managed by addressing smaller, more manageable components before synthesizing a comprehensive overview. The resulting explanation details not only what the model does, but how individual parts contribute to the overall outcome.

The developed framework demonstrably achieves complete explanation coverage, quantified as 1.0 for both StructuralCoverage and CompositionalCoverage. This validation is based on a three-level annotation hierarchy, meticulously designed to reflect clinically meaningful groupings of model components and their associated interactions. This hierarchical structure ensures that every element of the model, and the manner in which these elements contribute to overall function, is accounted for within the explanation process, providing a comprehensive and exhaustive account of the model’s behavior.

Real-World Impact: Enhancing Trust in Cardiovascular Risk Prediction

The PREDICT CVD risk model, currently utilized within New Zealand’s healthcare system to assess an individual’s likelihood of developing cardiovascular disease, sees a marked improvement in trust and usability when paired with formal explanation techniques. While highly accurate in its predictions, the model’s internal complexity previously presented a barrier to clinical acceptance; healthcare professionals desired greater insight into why a particular risk score was assigned. By employing methods that deconstruct the model’s decision-making process, clinicians can now understand the contribution of each factor – such as age, cholesterol levels, and blood pressure – to an individual’s overall risk. This transparency not only fosters confidence in the model’s output but also allows for more informed, collaborative patient care, enabling physicians to tailor interventions based on a clear understanding of the driving factors behind each prediction.

The PREDICT CVD Risk Model, utilized in New Zealand for cardiovascular disease prediction, fundamentally operates on the principle of Additivity, inherent to the Cox Proportional Hazards Model it employs. This means the model calculates risk by summing the individual contributions of each factor – age, cholesterol levels, blood pressure, and so on – rather than through complex interactions. This additive structure isn’t merely a mathematical convenience; it’s crucial for interpretability. Because the model assesses risk via a straightforward summation of effects, it allows for a clear and verifiable explanation of why an individual receives a particular risk score. Each factor’s contribution can be directly traced and quantified, fostering trust and enabling clinicians to effectively communicate risk assessments to patients and tailor preventative strategies with confidence. The additive nature of the model therefore moves beyond simple prediction to offer genuine understanding of the underlying factors driving cardiovascular risk.

The successful annotation of a cardiovascular disease risk model, built on the Cox Proportional Hazards Model, signifies a crucial step beyond theoretical demonstrations. This practical application, deployed in a New Zealand clinical setting, validates the framework’s ability to function effectively with complex, real-world data-not merely simplified examples. By meticulously annotating the model’s contributing factors, researchers demonstrated the framework’s scalability and utility in a high-stakes domain, proving its potential to enhance transparency and trust in predictive healthcare tools. The annotation process not only illuminated the model’s internal logic but also paved the way for improved interpretability, potentially aiding clinicians in understanding and validating risk assessments for individual patients.

Guaranteeing Reliability: The Path Forward for Verifiable XAI

Robust model verification stands as a critical component in the development of trustworthy explainable AI (XAI) systems. It moves beyond simply having an explanation to rigorously confirming that the explanation faithfully represents the model’s actual decision-making process. This process involves mathematically proving that the explanation’s logic aligns with the model’s internal computations, ensuring it isn’t a superficial rationalization. Without such verification, explanations risk being misleading or even demonstrably false, potentially leading to flawed interpretations and incorrect reliance on the AI’s output. Techniques such as formal methods and property-based testing are employed to identify discrepancies between the explanation and the model’s behavior, guaranteeing that the stated reasons for a prediction are genuinely reflective of the underlying computation and providing a solid foundation for accountability and trust.

The pursuit of inherently interpretable models offers a significant advantage when it comes to verifying explanations. Unlike post-hoc methods that attempt to rationalize a ‘black box’ decision, models built with inherent explainability – such as decision trees or certain rule-based systems – possess transparency by design. This foundational clarity dramatically simplifies the verification process; instead of proving that an explanation matches the model’s behavior, one can directly assess whether the model’s structure itself adheres to desired logical or domain-specific constraints. Consequently, formal verification techniques – which mathematically prove properties about a system – become far more tractable and less computationally expensive. This streamlined verification not only reduces the risk of misleading or erroneous explanations, but also fosters greater confidence in the model’s reliability and trustworthiness, ultimately accelerating the deployment of responsible AI.

The pursuit of truly reliable artificial intelligence hinges on a commitment to both formal explanations and rigorous verification processes. Establishing clear, mathematically defined explanations allows for precise evaluation of a model’s reasoning, moving beyond simply observing what a system decides to understanding why. This level of scrutiny is paramount for building accountable AI, as it enables independent auditing and the identification of potential biases or errors. Consequently, prioritizing these features isn’t merely a technical refinement; it’s a fundamental step towards fostering public trust and enabling the widespread adoption of AI technologies across critical sectors, ultimately unlocking their potential for positive societal impact and responsible innovation.

The pursuit of inherent explainability, as detailed in the paper, demands a shift from post-hoc justifications to building transparency into the system’s core. This echoes John McCarthy’s sentiment: “It is better to solve a problem correctly than to provide a clever solution.” A ‘clever’ model, complex and opaque, might yield results, but lacks the robustness of a rigorously verified one. The paper’s focus on compositional coverage – ensuring explanations trace back to fundamental components – isn’t merely about understanding how a model arrives at a decision, but validating its underlying logic. If the explanation falters at any level, the entire structure is suspect, demonstrating that architecture, indeed, dictates behavior.

The Horizon of Understanding

The pursuit of explainable AI has, to this point, largely focused on post hoc rationalization – dressing up decisions after they are made. This work suggests a necessary shift: evaluation of inherent explainability, a property baked into the model’s structure. However, formalizing explanation does not eliminate the fundamental tension. Each optimization, each refinement of compositional coverage, inevitably creates new points of potential opacity. The system, in becoming more adept at a specific task, simultaneously becomes more complex, and thus, presents new challenges to complete understanding.

Future work must address the limits of annotation. The framework presented here relies on human-provided labels, which introduces both bias and scalability issues. The ideal system wouldn’t require external justification; its structure would be self-evident. This suggests a need to explore alternative formalisms, perhaps drawing from category theory or information theory, to represent knowledge and reasoning in a more intrinsically transparent manner.

Ultimately, the architecture is the system’s behavior over time, not a diagram on paper. A truly explainable model isn’t merely interpretable; it’s elegant. It reflects a fundamental principle: simplicity, not complexity, is the hallmark of genuine understanding. The question isn’t whether a model can be explained, but whether its very design demands explanation, or whether clarity is its default state.

Original article: https://arxiv.org/pdf/2512.17316.pdf

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