ML interpretability

Decoding black-box models to ensure transparency, safety, and regulatory compliance in high-stakes AI deployments.

ML interpretability transforms opaque neural networks into audited systems by quantifying how specific inputs drive predictions. Engineers use post-hoc tools like SHAP (Shapley Additive Explanations) and LIME to debug bias or explain credit denials to regulators. In medical imaging, techniques like Grad-CAM highlight the exact pixels triggering a diagnosis. These methods move AI from 'trust me' to 'show me' (reducing risk in sectors governed by GDPR Article 22 or similar transparency mandates).

https://christophm.github.io/interpretable-ml-book/