More and more decisions are made or quietly nudged by AI, which is fine right up until someone asks why, and the honest answer is “the machine felt strongly about it.”

In a world increasingly run by these systems, being able to explain them has stopped being optional. We demystify Explainable AI (XAI): what it is, why it matters, and how to build models that are not merely clever but accountable and willing to show their working. We weave theory together with real-world scenarios, including the instructive carnage of AI going wrong for want of explainability, and give serious attention to the ethics.

Because the more powerful these systems get, the less comfortable “it felt strongly about it” becomes as a legal defence. With, as ever, some fun along the way.