AI literacy

The practical understanding that lets non-specialists use AI systems well, judge their outputs critically and decide when to rely on them, as distinct from the expertise needed to build them. Frameworks typically include knowing what AI is and how machine learningmachine learning, n.: A way of building software that learns from examples instead of following rules written by a programmer. Shown thousands of labeled photos, for instance, a program can learn to recognize cats. learns from data; recognizing AI in everyday products; understanding typical strengths and failures such as hallucinationhallucination, n.: When an AI states something false or made up — a fake fact, quotation or source — fluently and confidently, as if it were true. and biasalgorithmic bias, n.: Unfairness in software that judges or serves people: it consistently produces worse results for some groups, such as women, older people or ethnic minorities, often because it learned from skewed or incomplete records.; using AI tools effectively and checking their output; and weighing ethical, privacy and social implications. In the EU AI ActEU AI Act, prop. n.: The European Union's law on artificial intelligence, the first broad AI law anywhere: it bans some uses, sets strict checks for risky ones and puts duties on the makers of the biggest AI models., AI literacy is a defined term. From 2 February 2025, Article 4 required providers and deployers to take measures to ensure, to their best extent, a sufficient level of AI literacy among staff and others operating or using AI systems on their behalf; since an amendment took effect on 27 July 2026, they must instead take measures to support its development, taking account of those people's knowledge, experience, training and context of use, without having to guarantee any individual's level.

Before the rollout, every caseworker took an AI literacy course on when to double-check the tool's summaries.