Explainable AI (XAI) focuses on making machine learning models transparent, understandable and trustworthy for humans. As AI systems grow more complex, especially deep neural networks, their internal reasoning becomes opaque. XAI research aims to open this “black box” so that humans can see why a model made a particular prediction or decision, and how confident it is.

A core distinction is between global and local explanations. Global explanations describe how a model works overall, such as which features are most important across many decisions. Local explanations clarify individual predictions, for example why a medical AI labeled a specific image as showing a tumor. Techniques include feature importance scores, saliency maps that highlight influential regions in images, and example based explanations that compare new inputs to similar past cases.

Another line of work studies how to present explanations so that people can actually understand and use them. Researchers examine interpretability, which concerns how easily humans can grasp model logic, and findability, which asks whether users can even locate the right information in complex AI tools. User studies measure how explanations affect trust, error detection and collaboration between humans and AI.

XAI is becoming vital in high stakes domains like healthcare, finance and criminal justice, where decisions must be accountable and fair. It also matters for regulation, as laws increasingly demand that automated decisions be explainable. Future research combines technical methods with insights from psychology, design and ethics, with the goal of AI systems whose reasoning is both accurate and meaningfully transparent to the people who rely on them.