AI glossary · Ethics, safety and privacy
Explainable AI (XAI)
Explainable AI (XAI) is the effort to make AI decisions understandable to people, so you can see why a system gave a particular answer.
Also called interpretability, black box AI, AI transparency
In more detail
Many modern models are “black boxes”: even their creators can’t fully trace why they produce a given output. Explainability methods highlight which inputs mattered most, or produce a reason people can check.
It matters most where AI affects people’s lives, such as loans, insurance or medical decisions, and some laws give people a right to an explanation.
Related terms
AI biasAI bias is when an AI system produces results that are unfair to certain groups of people, usually because of skewed training data or design choices.AI ethicsAI ethics is the study of how AI should be designed and used so it is fair, safe, transparent and respects people’s rights.Responsible AIResponsible AI is the practice of building and using AI in ways that are safe, fair, transparent and accountable, with people kept in control.AI regulationAI regulation is the set of laws and rules governments create to manage the risks of AI, such as the EU AI Act.
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