Defined Terms for Writing About Automated Systems

A working glossary for legislation, standards, policy and reporting

• Testability. A term earns its place in a statute or standard only if compliance against it can be assessed. A term with no stable referent cannot be conformity-tested.
• Accountability. Language either preserves or dissolves the link between an action and the person answerable for it. Terms are chosen to keep that link visible in the grammar itself.
• Durability. The technology turns over faster than legislation. Terms that name a mechanism outlive terms that name a product category or a marketing era.

Rationale is given per term as a drafting note, so the reasoning travels with the definition.

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Part A - The umbrella term

artificial intelligence (AI)

A colloquial umbrella for a shifting family of technologies and products, currently centred on systems built from trained statistical models. Useful as a pointer to the public conversation; unsuitable as an operative term in legal or technical text.

Drafting note. The term fails all three criteria. It has no testable referent: no examination can determine whether a given system "is AI", so no obligation attached to the phrase can be conformity-assessed. It bundles a description with a capability claim: calling software "intelligent" asserts something the document then never has to prove. And its scope has moved every decade - expert systems were artificial intelligence in the 1980s and are not now; the same drift will overtake today's usage.

The workable protocol is to use the term exactly once, as an anchor to what the public debate is about ("systems commonly described as artificial intelligence"), and then define and use precise operative terms. There is solid drafting precedent for this move: statutes do not regulate "the cloud", they regulate hosted data services; they do not regulate "cyber", they regulate unauthorised access to a computer system. The colloquialism points; the defined term operates.

There is a further, quieter cost to the term. "Intelligence" imports agency, and imported agency is where responsibility goes to dissolve. A document that says "the AI decided" has already misplaced the decision. The remainder of this glossary is largely a toolkit for keeping decisions where they belong.

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Part B - What the technology actually is

The terms in this Part name the technology by its mechanisms. Each refers to something an auditor could point to - a file, a process, a configuration - so that obligations drafted with them have a solid object to attach to.

trained model (also: data model)

A file, or set of files, of numerical parameters ("weights") produced by running a training process over a body of data. A trained model is an artefact - it can be copied, hashed, versioned, licensed and audited like any other file.

Drafting note. Treating the model as an artefact rather than an entity is the foundation of everything else here. Artefacts have provenance, custody and versions; entities have intentions. Obligations attach cleanly to artefacts.

large language model (LLM)

A trained model that takes a sequence of text (and, in current systems, other inputs) and produces a continuation, one token at a time, by statistical prediction over its training distribution.

Drafting note. Name the mechanism, not the aspiration. "Large language model" describes what the artefact is; "AI" describes what its vendor hopes you think it is. Where a document concerns these systems specifically, this is the operative term.

vision-enabled model (also: multimodal model)

A trained model that accepts images, audio or video as input alongside text. "Multimodal" is the general term; "vision-enabled" is preferable where image input is the specific capability at issue.

generative model

A trained model whose output is new content (text, images, audio, video) rather than a classification or score. Preferable to "generative AI" for the Part A reasons; the noun does the work the adjective was pretending to do.

training; training data

Training is the process that produces a trained model from data. Training data is the body of material consumed by that process.

Drafting note. Keep "training" for the production of the model and do not let it blur into "learning". A system that is fine-tuned has been modified by its operator; it has not "learned" in any sense that transfers responsibility to it.

fine-tuning; adapter

Fine-tuning is further training applied to an existing model to specialise its behaviour. An adapter is a small set of additional weights applied to a base model to the same end, separable from the base.

Drafting note. The separability of adapters matters for governance: a capability added by adapter can be individually identified, licensed and withdrawn. Definitions that treat "the model" as indivisible foreclose this.

inference

The act of running a trained model on an input to produce an output. (Distinct from the everyday and legal senses of "inference"; where both senses could operate in one document, define this one as "model inference".)

agent (in the software sense)

A community and industry packaging term for a configuration of software that can invoke tools - search, code execution, data access, transactions - in a loop directed at a goal, with a trained model selecting the actions. Put plainly: a configuration of software that can invoke tools, or an instance of software configured to carry out a specific task. Like "artificial intelligence" in Part A, the word gathers several nameable things into one that sounds singular and animate. In operative text, name the components instead.

An "agent" is assembled from things this glossary already names: a trained model performing inference to select actions; a set of tools the software is permitted to invoke; a deployment configuration that scopes and bounds what it may do; and the developer or deployer who configured it and remains answerable for it. The word describes an architecture and a packaging convention, not a status; it is configured and bounded by a person or organisation throughout.

Drafting note. This is the term most likely to smuggle agency back in - it is, after all, the word "agent". Defining it as a configuration, and pointing to the components by their real names, keeps the configuring party in frame. Where a document must refer to the assembled thing rather than its parts, "the configured system" or "the automated system" (Part C) carries the meaning without the personification.

frontier model

A trained model at or near the current maximum of general capability. A relative and time-indexed term; usable in policy prose, too unstable to bear obligations without a capability-threshold definition attached.

open weights

Distribution of a trained model's parameter files such that others can run, inspect or modify them. Distinct from open source, which additionally implies access to training data and code.

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Part C - What systems do in deployments

This Part manages one boundary: what a system produces on one side, and what a person decides on the other. The terms are built so that every crossing of that boundary leaves a record.

automated system

A system that performs a task without human action at the moment of performance. The general operative term for regulation: it is testable (was there human action at that step or not), mechanism-neutral and durable.

Drafting note. "Automated", not "autonomous". Automation describes a mechanism; autonomy claims a status. Every deployed system is configured, invoked, scoped and terminable by people, and "autonomous" erases the configuring hand exactly where accountability needs it visible. If a document must describe high degrees of automation, "highly automated" says it without the claim.

computer-assisted; model-assisted; software-guided

Modifiers for work performed by a person with automated support: a model-assisted review, a computer-assisted assessment, a software-guided inspection. Choose the modifier that names the mechanism actually involved.

Drafting note. This family solves a real drafting problem: how to describe hybrid work without either erasing the machine or erasing the person. The modifier names what the system contributed; the noun keeps the person the agent of the work. "The officer completed a model-assisted assessment" survives scrutiny; "the AI assessed" does not.

suggestion

An output of an automated system presented to a person for possible adoption, carrying no effect until adopted.

inferred result

A conclusion produced by model inference, recorded as such. "Inferred" is chosen with care: it conveys derivation from evidence while implying neither authority nor finality. An inferred result is an input to a decision, not a decision.

Drafting note. The available alternatives each fail. "Finding" implies a finder with standing. "Assessment" implies an assessor. "Result" alone implies completion. "AI-generated conclusion" imports Part A's problems. "Inferred result" says precisely what happened - a model derived this from that - and nothing more.

determination

A conclusion adopted by a person or body with the standing to make it, from which consequences follow. A determination always has an author.

author of a determination

The identified person (or, where legislation permits, the identified body) answerable for a determination. Recording the author is what makes review, appeal and accountability mechanically possible rather than forensically reconstructed.

Drafting note. The grammatical test for any sentence about a decision: can it be written with a human subject? "The delegate determined X, informed by an inferred result" passes. "It was determined that X" fails, and the failure is the finding - the passive voice in decision records is not a style problem, it is an accountability defect.

The Royal Commission into the Robodebt Scheme documented at national scale what happens when determinations are made with no identifiable author. A defined term that forces the author into the record is the cheapest structural remedy available.

automated determination

A determination given legal or practical effect without a person adopting it. Where legislation permits automated determinations at all, this term should carry the attached safeguards: notice to the affected person, a recorded statement of what produced the determination, and a right to review by a person with authority to substitute their own.

human review

Review of a determination or inferred result by a person with the authority, information and time to reach a different conclusion. Review without the power to substitute is not review; a definition that omits the substitution power licenses rubber stamps.

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Part D - Governance terms

Terms for permission and proof: who may use what, on whose grant, and how a relying party checks a claim instead of trusting it.

developer; deployer

The developer produces or substantially modifies a trained model or automated system; the deployer puts it to use in a context affecting others. One organisation can be both. Obligations differ by role and should be drafted against the role, not the organisation.

provenance record

A record of the sources that produced an output or an artefact: for a trained model, the training data and process; for an output, the model, the inputs and any adapters involved. A provenance record may be declarative (asserted by the record-keeper) or verifiable (checkable by a third party, for instance cryptographically). Standards should say which they require; the two differ in kind, not degree.

consent grant

A permission issued by the person or organisation a set of data is about or belongs to, authorising a named party to access or use it. A well-formed grant is explicit about five things: who is granted, what data, for what purpose, until when, and how it is revoked. Absence of any of the five should render the grant unenforceable by the grantee, not the grantor.

Drafting note. Consent language in current instruments tends to specify the asking (how consent is sought) and neglect the artefact (what a consent actually is, as a record). Defining the grant as a record with mandatory elements moves consent from a ritual at collection time to an inspectable object over the data's whole life.

mandate (of data)

A granted consent package: the data itself, bound to its grant, such that the scope, purpose, window and revocation state travel with the material rather than in a policy filed elsewhere. Where the binding is cryptographic, use or decryption outside the grant's terms is prevented rather than prohibited.

Drafting note. The word is deliberately turned around. In ordinary administrative usage a mandate runs downward, from an authority to its subject. Here it runs upward: the person issues the mandate; the institution holds it on terms. A defined term that inverts an expectation is worth the momentary friction when the inversion is the policy.

capability

A distinct thing a system is able to do: transcribe speech, match faces, draft correspondence, execute payments. Capabilities, not systems, are the right unit of permission - a system is an ever-changing bundle, while a capability can be named, granted and withdrawn.

capability licence

Working reference code. A running reference implementation of the licensed-capability mechanism is published at git.meanwhile.computer/meanwhile/research - clone it and check the claims yourself.

A scoped, time-bounded, revocable authorisation for a system to exercise a named capability in a named context, issued by a party with the standing to issue it and verifiable by the parties relying on it.

Drafting note. This is the pattern society already applies to people. Driving, prescribing, practising law and handling firearms all follow the same structure: threshold, examination, scoped authorisation, time limit, revocation for cause. The anomaly is the pattern's absence from automated systems, not the proposal to extend it to them. It is also drafting-economical: one licensing mechanism in a framework act, with capabilities and thresholds specified in instruments, ages far better than per-technology prohibitions.

revocation

Withdrawal of a grant or licence. A standard should say what a revocation reaches: advisory revocation (relying parties are told to stop trusting), access-gated revocation (further use is prevented), and cryptographic erasure (previously issued material becomes permanently unusable). These are three different remedies; an instrument that says only "may be revoked" has not chosen one.

attestation

Evidence, checkable by a relying party, that a system has a claimed property - that it runs a particular model, within particular restrictions, in a particular environment. The mechanism that turns "the provider assures us" into "the verifier confirmed". Conformity regimes that stop at self-assessment are attestation-free by construction, and should declare it.

conformity assessment

The process of establishing that a system or deployment meets a standard. Drafters should distinguish documentary conformity (records reviewed) from operational conformity (behaviour tested) from continuous conformity (properties verified in use, e.g. by attestation). The three catch different failures; naming which is required is most of the work of an enforceable standard.

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Part E - Terms to retire, and what to say instead

The left column is in wide circulation. The middle column names the defect; the right column gives the working replacement. None of the replacements is a euphemism - each says more than the phrase it retires.

Retire	Because	Say instead
the AI decided / assessed / found	Misplaces the decision; no author in frame	the delegate determined, informed by an inferred result
it was determined that	Passive voice as accountability solvent	[name or role] determined
the system learned	Transfers responsibility for a modification to its object	the model was trained / fine-tuned to
hallucination	Anthropomorphic; implies a mind having an experience	fabricated output; unsupported output
autonomous system	Claims a status; erases the configuring party	automated system; highly automated system
the agent decided / did / knows	Personifies a configuration; drops the party who configured and bounds it	the configured software invoked [tool]; [name or role]'s configuration set the scope; the model selected the action
the algorithm	Vague across code, model and policy; usually means the model or the deployment rules	the trained model; the decision rules; the deployment configuration
smart [anything]	Capability claim with no test	name the actual capability
the AI's knowledge / understanding / intent	States of mind attributed to an artefact	the model's training data; the model's output; the operator's configuration
human in the loop (undefined)	Satisfiable by a rubber stamp	human review, defined with the power to substitute
responsible AI (as a compliance claim)	Adjective doing the work obligations should do	the specific obligations met, listed

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Using this glossary

The definitions are written to be lifted: into drafting instructions, standards, agency style guides, procurement templates and newsroom style books, with or without the drafting notes. Terms are versioned; the criteria at the top are the test for any addition. Suggested practice for instruments: define the operative terms in the instrument itself (definitions travel badly by reference), and keep "artificial intelligence" to the single anchoring use described in Part A.