ai explainability

The ai explainability tag brings together reporting on how AI systems can make their outputs more understandable, testable, and useful to people who must evaluate them. Current coverage includes Microsoft Research’s generative causal testing work, which aims to turn language-model predictions about brain activity into short explanations that can be tested experimentally rather than treated as proof of understanding. It also follows planned Microsoft Purview Data Security Triage Agent updates that add reasoning traces and confidence scores for Data Loss Prevention alerts. Together, these discussions focus on transparency, scientific scrutiny, analyst trust, and the practical limits of relying on opaque AI predictions or automated security judgments.