Explainable AI (XAI) refers to methods that make an AI model’s decisions understandable to humans — showing why a prediction was made — which is essential for trust, compliance, and debugging.
How it works
XAI techniques reveal what drove a model’s output: which features mattered most, how changing an input would change the result, or which examples influenced a decision. Methods range from inherently interpretable models to post-hoc tools such as feature-importance and local explanations for complex models.
Why it matters for enterprise AI
In regulated and high-stakes settings, a correct answer is not enough — organisations must be able to justify it. Explainability supports compliance, lets teams catch bias and errors, and builds the human trust needed to act on AI decisions, making it a pillar of responsible AI governance.
