AI interpretability

AI interpretability opens the 'black box': it provides the specific methods and tools (e.g., SHAP, LIME) necessary to trace, audit, and explain a complex model's decision-making process.

AI interpretability is the technical mandate for transparent AI systems. It moves beyond performance metrics, focusing on the internal logic of complex models like deep neural networks. We use specific techniques—like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations)—to map model outputs back to their input features, quantifying influence. This capability is non-negotiable for high-stakes domains: in finance, it ensures regulatory compliance (e.g., GDPR's 'right to explanation'); in healthcare, it builds the trust required for clinical adoption. The goal is clear: ensure the system's logic is inspectable, auditable, and free of embedded biases before deployment.

https://www.nist.gov/artificial-intelligence/ai-research/explainability