AI literacy is not a technical specialism but a foundational skill for anyone who works with AI systems in a professional context. The EU AI Act gives the term a legal basis: Article 3(56) defines AI literacy as "skills, knowledge and understanding that allow providers, deployers and affected persons, taking into account their respective rights and obligations in the context of this Regulation, to make an informed deployment of AI systems, as well as to gain awareness about the opportunities and risks of AI and possible harm it can cause." This article explains what that definition entails, which knowledge components it covers, and how AI literacy differs from purely technical AI training.
A legal definition with broad scope
Article 4 of the EU AI Act requires providers and deployers to take measures to ensure, to their best extent, a sufficient level of AI literacy among their staff and others who operate AI systems on their behalf. The obligation applies to all AI systems regardless of risk category and entered into application on 2 February 2025. Supervision and enforcement by national market surveillance authorities begins on 2 August 2026.
The definition is deliberately broad. AI literacy is not uniform: the required level depends on a person's role, the technical nature of the system, and the context in which it is used. A staff member reviewing AI outputs has different knowledge needs from a developer training a model or a manager taking procurement decisions.
Four core competences
From the Regulation, Recital 20, and the European Commission's guidance, four interrelated knowledge components can be identified:
1. Technical foundational knowledge Understanding what an AI system is, on what data it was trained, how it operates and where its limitations lie, and how its outputs are produced. This does not require the ability to build models, but does require a sufficient grasp of how deployed systems function and whether their outputs can be relied upon.
2. Critical thinking The ability to question AI outputs rather than accept them as factual: asking whether results are reliable, whether there are signs of bias or error, and whether human oversight is needed. Recital 20 explicitly includes "the suitable ways in which to interpret the AI system's output" as part of AI literacy.
3. Ethical awareness An understanding of the societal and ethical implications of AI: risks of discrimination, impact on fundamental rights, transparency obligations, and the effects on persons targeted by the system. The European Commission explicitly encourages connections to ethical principles and AI governance as part of the literacy obligation.
4. Practical application skills The capacity to deploy AI systems purposefully and responsibly within one's own role, including familiarity with applicable laws and regulations. This also means recognising when a system is being used outside its intended scope and knowing when escalation or human intervention is required.
AI literacy versus technical AI training
AI literacy and technical AI expertise are not the same. Technical training focuses on building, training, and optimising AI models, and requires deep knowledge of mathematics, statistics, and programming. AI literacy makes different demands: it is about understanding, judgement, and awareness, not coding ability.
Academic research formulates the distinction as follows: "AI literacy focuses on knowledge, understanding and application of AI. AI competency is about skilful application and optimisation." Literacy is the precondition; competence builds on it. A staff member without a technical background can be fully AI-literate if they understand how the system functions, what risks it carries, and how they exercise their rights and obligations.
Context-specific application
The European Commission emphasises that there is no one-size-fits-all approach. A risk-based method asks organisations to follow three steps: identify which AI systems are in use and their risk category, determine which staff work with them and what they already know, and build literacy measures on that baseline. The content, depth, and form of those measures may vary by target group and sector.
The Dutch government's Digitale Overheid portal describes this in similar terms: the required level of AI literacy is strongly dependent on the context, the nature of the work, and the sector, whether in public administration or in private organisations acting as providers or deployers.
Scope: who does it cover?
The literacy obligation targets three groups. First, the staff of providers and deployers. Second, "other persons" who operate or use AI systems on the organisation's behalf, including self-employed individuals, agency workers, service providers and, in some cases, partners. Third, affected persons: people upon whom AI systems are applied, who need sufficient understanding of how automated decisions affect them.
Relationship to other obligations
AI literacy does not stand alone. Article 4 reinforces two other core obligations in the Regulation: the transparency requirement of Article 13, which requires that users understand what a system does, and the human oversight requirement of Article 14, which requires that people be able to monitor, correct, and if necessary override AI outputs. Without a sufficient level of AI literacy, these obligations are difficult to fulfil in practice.
AI literacy therefore forms the foundation of the broader compliance architecture of the EU AI Act. Stichting ter Bevordering van AI-Geletterdheid (SAIG) certifies AI literacy in accordance with Article 4, in line with international practice for the certification of persons, as demonstrable evidence that the literacy obligation has been met.