MUPAX: Multidimensional Problem Agnostic eXplainable AI

MUPAX offers a universally applicable explanation method: Unlike existing explainability techniques that work best for specific types of AI models or data, MUPAX works across different model types and data formats (audio, images, 3D medical scans), making it potentially useful for explaining AI decisions across diverse real-world applications.

The method provides mathematical guarantees: MUPAX comes with formal convergence guarantees regardless of the loss function or data dimensions used, meaning developers and regulators can have confidence that the explanations will be reliable and consistent—an important requirement for deploying AI in high-stakes domains like healthcare.

Explanations improve rather than degrade model performance: While other explanation methods typically reduce accuracy when they mask or remove features to test importance, MUPAX actually maintains or improves model accuracy by identifying only the most meaningful patterns, suggesting the explanations reflect genuine decision-making logic rather than artifacts.

The approach eliminates spurious correlations: By using structured perturbation analysis, MUPAX distinguishes between features that genuinely drive AI decisions versus coincidental patterns in the data, helping prevent explanations that might mislead users about why an AI system made a particular decision.

Deterministic and model-agnostic design supports trustworthiness: Because MUPAX produces consistent results and works with any AI model type, it removes variability that could undermine trust and makes it easier to audit and validate AI systems across different organizations and applications.