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Trustworthy AI
The umbrella term for AI that people can justifiably rely on. The most widely cited definition lists its properties: valid and reliable, safe, secure and resilient, accountable and transparent, explainable and interpretable, privacy-enhanced, and fair.
- trustworthy AI
- NIST AI RMF
- OECD AI Principles
- responsible AI
- ethics guidelines
The main definitions
The NIST AI Risk Management Framework lists the seven characteristics above and stresses that they trade off against each other. The OECD AI Principles, adopted in 2019 and updated in 2024, set out values-based principles that most national AI strategies cite. The EU High-Level Expert Group's Ethics Guidelines for Trustworthy AI (2019) set seven requirements that shaped the EU AI Act. The wording differs; the ground covered is largely the same.
Why the term needs care
Trustworthiness is a claim about a system, not a feature it has. Calling a system trustworthy says nothing unless each property is backed by evidence for that system in its use. The useful move is to break the term into its characteristics and ask what the evidence is for each; see AI assurance.
Where it sits in the register
The register is organised so that each characteristic has risks and controls behind it, from training data bias for fairness to prompt injection for security. A system that met them at approval and was never rechecked is the AI system lifecycle controls gap.
Sources
Related Terms
AI Assurance
The work of measuring, evaluating and communicating whether an AI system, or the organisation running it, meets the claims made for it, so that others can rely on those claims. It spans internal testing through to independent audit and certification, and the question it answers is not whether a system is good but whether a statement about it can be trusted.
Explainability
The ability to give a person an account of why a system produced a particular output, in terms they can act on. Explainability is a property of the account and its audience, not of the model: an opaque model can be explainable, and an interpretable one can fail to be explained.
Fairness & Bias
The principle and practice of ensuring AI systems do not produce systematically different outcomes for individuals or groups because of protected characteristics or other vulnerable attributes. Algorithmic fairness is the same idea expressed as measurable criteria.
NIST AI Risk Management Framework
The US National Institute of Standards and Technology's voluntary framework for managing AI risk, built around four functions: Govern, Map, Measure and Manage. Version 1.0 was published in January 2023 and is being revised under the US government's July 2025 AI Action Plan. Since 1.0 NIST has added a Generative AI Profile, an adversarial machine learning taxonomy, a draft Cybersecurity Framework profile for AI, and work on control overlays and AI agent standards. None of it is mandatory, and most of it is used as a common vocabulary rather than a checklist.
Transparency
The disclosure of information about an AI system to the people who need it: that AI is being used, what it does, what it is for and what risks it poses, in terms they can understand. For people whose personal data it uses, being informed is also a legal right.