A searchable glossary of workplace AI governance terms and AI safety definitions for Australian workplaces.
The umbrella term for computer systems that can perform tasks normally associated with human intelligence, such as analysing information, recognising patterns, generating content, making predictions, recommending actions or supporting decisions.
AI designed for a specific task or defined group of tasks, such as chatbots, image generation, fraud detection, translation, scheduling, document review or medical diagnosis support.
The structures, processes, responsibilities and controls used to manage AI safely and responsibly.
A machine-based system that uses inputs to generate outputs such as predictions, content, recommendations or decisions.
AI that can create new content such as text, images, audio, video, music, code, summaries or synthetic data in response to prompts or inputs.
AI that can plan, reason through steps and complete multi-step tasks with limited supervision, often by using tools, connecting to systems or taking actions toward a goal.
AI that can learn from new information, changing conditions or user feedback after deployment. In workplaces, adaptive systems need monitoring so changes do not create new risks, bias or accountability gaps.
A proposed form of AI with human-level general intelligence that could learn, reason and perform almost any intellectual task across many domains. AGI has not been achieved.
A theoretical form of AI that would vastly exceed the best human minds across most or all domains. ASI is speculative and does not exist today.
Multiple AI systems, agents or models working together as a coordinated intelligence. Some early multi-agent patterns exist, but broad collective AI remains an active research area.
AI that combines machine outputs with human expertise, judgement or approval. In workplaces, this includes AI-assisted decision-making where humans remain accountable.
A theoretical AI that would possess consciousness or self-awareness. There is no evidence that current AI systems are self-aware.
The use of a system to make or substantially support decisions, sometimes with limited human involvement.
The level and type of AI-related risk an organisation is prepared to accept in pursuit of its goals.
A record of approved AI tools and systems, including purpose, owner, data used, risks, controls and review date.
Testing, validation, audit or review processes that give confidence an AI system is operating as intended and within risk limits.
The knowledge and capability needed to understand AI benefits, limitations, risks and safe use.
Unfair outcomes caused or amplified by data, design, assumptions, deployment or use.
The management of data quality, security, access, lifecycle, legal compliance and appropriate use.
The ability to understand and explain how an AI system produced an output or influenced a decision.
Human monitoring, review, intervention or approval of AI outputs, decisions or actions.
AI use that is lawful, ethical, accountable, transparent, safe, fair and aligned with human values.
Unapproved AI use at work without formal oversight, policy approval or risk controls.
Research, development and practical use of methods and applications that make AI systems possible.
An automated entity that senses conditions, responds to them and takes actions to pursue goals.
A review, done internally or independently, that checks whether an AI system meets the requirements of a chosen framework.
Shared datasets, tasks and measures used as comparison points for AI system performance.
A person or organisation that uses or supplies an AI system in delivering a product or service, either internally or to people outside the organisation.
The stages an AI system moves through, from idea and development to testing, deployment, operation and retirement.
A representation of data, a process or a phenomenon that uses AI algorithms to interpret inputs, predict outcomes or generate responses. Machine learning models produce inferences or predictions from input data.
An organisation or entity that provides AI-related services, such as integration, data support, evaluation or audit services.
An organisation or entity that designs, develops, tests and provides AI technologies, models or components.
An organisation or entity that provides products or services that use one or more AI systems.
The resources needed to develop, deploy and maintain AI, including data, models, hardware, compute, people and funding.
A machine-based system that uses inputs and objectives to infer outputs such as predictions, content, recommendations or decisions that can affect digital or physical environments.
An entity that uses or relies on an AI system, including an organisation, an individual or another system.
The broader process of creating and delivering value through AI systems, extending beyond the supply chain.
Being answerable for actions, decisions and performance within a defined responsibility area. Accountability is the state of being accountable.
A person, organisation, community or system affected by the decisions or behaviours of an AI system.
Instructions a computer follows to perform tasks or solve problems. A machine learning algorithm learns model settings from data against selected criteria.
The process of measuring, evaluating and communicating evidence about a system, process or organisation to confirm it is operating as intended. AI assurance focuses on trustworthiness.
A systematic difference in how certain people, groups or objects are treated compared with others.
Ongoing incremental training of an AI system during its operational phase.
Giving relevant information to a party that may not already know it.
Assessment against criteria. This can include model evaluation, system evaluation, capability evaluation, benchmarking, testing, verification, validation, risk assessment and impact assessment.
The ability of an AI system to present the important factors behind a result in a way people can understand.
Treatment, behaviour or outcomes that are not driven by favouritism or unjust discrimination. Unfairness occurs when differential treatment is unjustified.
A set of computable properties that can identify digital content or near-duplicate content.
A very large AI model trained on broad data, often using self-supervision, and usable across many contexts.
An AI system type that can address a broad range of tasks and uses, including uses not originally intended by developers.
AI that uses generative models to create content such as text, images or other media with properties similar to training data.
A structured assessment of wider economic, social and environmental effects that may result from deploying and operating an AI system.
Trust in an AI system based on reliable evidence, avoiding both over-trust and under-trust.
Applying classification labels to information assets. For AI content, labelling can include visible notices that content was AI-generated and information about provenance.
Using quantitative, qualitative or mixed methods to assess, benchmark and monitor AI performance, risk and impact.
Qualitative or quantitative measures used to assess, compare and track quality or performance. Metrics may focus on the model, system behaviour, risk outcomes or broader impacts.
An AI system focused on defined tasks and uses for a specific problem.
Measurable results.
The history of an asset and its interactions with actors and other assets, represented through provenance data.
A simulated adversarial exercise, designed to reflect real-world conditions, used to assess AI system and organisational security capability.
Developing and using AI in ways that create benefits while reducing the risk of negative consequences for people, groups and society.
The relationship between likelihood and consequence, including uncertainty about objectives and the chance of harm. AI consequences may be positive, negative or both.
A systematic process for identifying and evaluating the likelihood and consequences of AI-related events or actions that could cause harm.
A measure that maintains or changes risk, including a process, policy, device, practice or other action.
Practices and tools used to reduce the likelihood or consequences of AI-related events or actions that could cause harm.
A specification for testing, including inputs, test steps and expected outcomes. A group of test cases is a test suite.
Executing an AI model or system to check whether it shows expected behaviours across selected test cases.
For organisations, communicating relevant activities and decisions to stakeholders in an accessible way. For systems, making appropriate information available, such as features, performance, limits, components, assumptions, data sources and evaluation methods.
The extent to which a stakeholder is persuaded that an AI system will behave as intended.
The ability of an AI system to meet stakeholder expectations in a way that can be verified.
Evidence-based confirmation that user needs have been fulfilled.
Evidence-based confirmation that specified requirements have been fulfilled.
Information embedded in digital content, visibly or invisibly, to show provenance or indicate that content was generated or materially modified by AI.