AI, Authorship and Provenance
Last updated: June 2026
Effective communication comes first — how we think about tools, authorship, provenance, and human
responsibility.
Effective communication comes first
We recognise that contemporary writing and communication are increasingly produced through collaboration
between people, software and AI systems. This may include spelling and grammar tools, translation systems,
generative AI, large language model (LLM) reasoners, research tools, editorial systems and other forms of
computational assistance.
We do not consider the mere use of such tools to determine authorship, ownership, responsibility or the
value of a communication.
A tool is a means of production. Its use does not, by itself, establish that the resulting work was
authored by the tool, nor does it diminish the contribution of the person who directed, evaluated, edited
or accepted the result.
Authorship is not a binary property
We therefore distinguish between authorship, contribution and provenance.
A person may originate an idea, supply source material, direct an analysis, challenge a conclusion, make
editorial decisions, verify factual claims or accept responsibility for a final communication while using
computational tools extensively in its production.
Conversely, a person may make only nominal changes to material substantially produced by an AI system.
For this reason, statements such as "AI-generated" or "human-generated" can be insufficient to describe
how a significant communication was actually produced.
Where provenance is relevant, we prefer to describe the transformation history of an artefact: its
sources, material contributions, tools used, human interventions, editorial decisions and verification.
Provenance does not mean tool surveillance
We do not regard the detection of AI-like characteristics in a text as proof of AI authorship.
Statistical detection can provide evidence that a text resembles material produced by a particular class
of systems. It cannot, by itself, establish who produced the text, quantify the contribution of a
particular tool, or determine whether the use of that tool was legitimate.
Accordingly, AI-detection results should be treated as signals requiring appropriate context rather than
as definitive evidence of authorship or misconduct.
The same principle applies in reverse: the absence of an AI-detection signal does not establish that a
text was produced entirely by a human.
Responsibility remains human where responsibility is human
Where a person publishes, submits, approves or otherwise presents a communication as their work, that
person remains responsible for the claims and representations they make, subject to the applicable
contractual, professional and legal framework.
Using an AI or LLM system does not transfer responsibility to the system.
Human review should therefore be proportionate to the significance and potential consequences of the
communication. Particular care should be taken with factual claims, legal or regulatory assertions,
confidential information, personal data and other material where errors may cause harm.
Communication is the objective
The purpose of writing is not to demonstrate that a particular tool was absent from the production
process.
The purpose is to communicate something accurately, appropriately and effectively to its intended
audience.
We therefore favour assessment and governance based on the qualities that matter to the communication
itself: accuracy, meaning, clarity, context, provenance where relevant, accountability and fitness for
purpose.
Where a particular context requires unaided human performance — for example, an examination intended
specifically to establish an individual's independent capability — that requirement should be stated
explicitly and assessed directly.
Where tool-assisted performance reflects the real-world capability being assessed or exercised,
appropriate use of those tools should not automatically be treated as compromising authorship.
A practical principle
Our position can therefore be summarised simply:
The fact that a tool participated in producing an artefact is less important than understanding what the
tool contributed, what the human contributed, what has been verified, and whether the resulting
communication achieves its intended purpose.
We favour provenance over presumption, context over binary classification, and effective communication
over artificial distinctions between human and tool contribution.