AI Governance, Risk & Safety Glossary
Practical governance controls for managing AI risk, accountability, and system safety.
Accountability
Being responsible for what an AI system does and being prepared to answer for it, you can't blame the algorithm and walk away.
AI governance
The rules, roles, and processes an organisation puts in place to make sure its AI is developed and used responsibly, fairly, and in line with the law.
AI management system
A formal, certified system, based on the ISO/IEC 42001 standard, that an organisation uses to manage its AI activities responsibly and consistently.
AI risk management
The systematic practice of finding, assessing, and managing things that could go wrong with an AI system, from biased outputs to security vulnerabilities to legal non-compliance.
AI risk register
A living document that lists all the known risks of an AI system, what could go wrong, how serious it is, what is being done about it, and who is responsible.
Algorithmic bias
When an AI system consistently produces unfair results for certain groups of people, for example, being less accurate or less favourable for women, ethnic minorities, or older adults.
Algorithmic impact assessment
A structured analysis conducted before and during AI deployment to understand who might be harmed and how, enabling organisations to act before problems occur.
Auditability
How easy it is for an independent reviewer, a regulator, auditor, or assessor, to check that an AI system is doing what it claims to do and following the rules.
Bias mitigation
The methods used to find and reduce unfairness in an AI system, whether by improving training data, adjusting the model, or modifying outputs.
Continuous monitoring
Keeping a constant eye on an AI system after it goes live, watching for declining performance, unexpected behaviour, or new risks as data and conditions change.
Cybersecurity
Protecting an AI system from being hacked, manipulated, or misused, including protecting the data it uses and the outputs it produces.
Human oversight
Keeping a human meaningfully in the loop so they can catch mistakes, stop the AI when needed, and remain accountable for outcomes.
Incident management
The organised response to when something goes wrong with an AI system, detecting the problem, fixing it, reporting it if required, and learning how to prevent it next time.
Model card
A fact sheet for an AI model that tells you what it was built for, how well it works for different groups of people, and what it should not be used for.
Risk classification
Deciding which 'risk bucket' an AI system falls into so you know what rules and safeguards apply to it.
Robustness
How well an AI system keeps working correctly when things change, when data is noisy, unusual inputs arrive, or someone tries to manipulate it.
Safety component
A part of a product that stops people from getting hurt, if it fails, someone could be harmed. When AI acts as this kind of component, it falls under strict safety rules.
Traceability
Being able to follow the trail, from an AI output back to the data, model, and decisions that produced it, to understand exactly how a result was reached.
Transparency
Being open about how an AI system works, what it can and cannot do, where its data comes from, and who is responsible for it.