For most of the history of science, the tools of research — however sophisticated — did the bidding of the people using them. A telescope or a statistical package extended human capability but did not decide what to investigate. That assumption is now being tested. AI agents capable of a degree of autonomy are beginning to appear in research: systems that can generate hypotheses, design experiments, and in some cases run them through automated laboratory equipment, iterating with limited human intervention. Autonomous experimentation of this kind raises a question scholarship was never built to answer: when an AI system materially contributes to a discovery, how should that contribution be attributed, and who is accountable for it? This article examines those questions, drawing on the AI and ML research-outputs domain of the CASRAI Dictionary.
What autonomous research looks like
The systems in question share a common feature: they make consequential choices in the research process rather than merely executing instructions. An AI agent might propose which compounds to test next, design the sequence of experiments, control the apparatus that performs them, and analyse the results to decide what to try next — a loop that can continue with the human supervisor stepping in only occasionally. The appeal is obvious: such systems can explore vast spaces of possibility far faster than people, accelerating discovery from materials science to drug development. But the autonomy that makes them powerful is what unsettles the established account of who does research and who answers for it. The agent is no longer just a tool; it is participating in the intellectual work. That shift forces the questions of attribution and accountability.
Why an AI cannot be an author
The clearest and most settled point in this debate is also the most important: an AI system cannot be an author of a research work. This is not technophobia or an arbitrary rule; it follows directly from what authorship means. Authorship carries accountability. An author is someone who can take responsibility for the integrity of the work, vouch for its honesty, defend it when questioned, and be answerable if it proves flawed or fraudulent. An AI system can do none of these things; it cannot be held responsible or called to account. The major editorial and integrity bodies have converged firmly on this position: AI tools, however capable, cannot meet the criteria for authorship, because the defining quality of an author — answerability — is one a machine cannot possess. The principles of authorship rest on responsibility, and responsibility is irreducibly human.
Accountability stays with people
If the AI cannot be accountable, accountability does not vanish — it remains with the humans involved. The researchers who deploy an autonomous system, decide to use its outputs, design the study it operates within and interpret and publish the results are responsible for that work, including for the AI’s contributions to it. This has a sharp consequence: a researcher cannot disclaim responsibility for an error or fabrication by pointing to the machine. If an AI agent generates a flawed hypothesis and a researcher publishes it as sound, the failure is the researcher’s, because the duty to verify and stand behind the work was theirs. Far from diluting human responsibility, autonomous systems concentrate it: the more capable the tool, the more important the human judgement about whether and how to trust it. Autonomy in the tool does not mean autonomy from accountability for the people.
Disclosure and the provenance of AI contributions
If an AI agent cannot be credited as an author but did genuinely contribute, the honest course is to describe what it did transparently. This is a matter of disclosure and provenance rather than authorship. A research report should be clear about the role an autonomous system played — which hypotheses it generated, which experiments it designed, which analyses it performed — so readers can understand how the work was produced and judge it accordingly. Recording the provenance of AI contributions serves several ends at once:
- Transparency. Readers and reviewers can see where machine judgement entered the work and weigh it appropriately.
- Reproducibility. Knowing which system was used, and how, is part of being able to reproduce the result.
- Accountability. Clear provenance makes plain which choices were the system’s and which the researchers’, keeping responsibility traceable.
Disclosure does not credit the machine; it documents it — an entirely different and appropriate act.
The limits of CRediT
It is natural to ask whether a contribution taxonomy could simply add the AI as a contributor. Here it is worth being precise about what the CRediT taxonomy is for. CRediT describes the contributions of people to a research work; it is a vocabulary for human roles, anchored in the assumption that contributors are accountable agents. An autonomous system is not a contributor in that sense, because it cannot bear the responsibility contributorship implies. The right place for AI involvement is therefore not the contributor list but the methods and disclosure sections, where its use can be described as part of how the work was done. What CRediT continues to do well is capture the human contributions around the AI — the conceptualisation, methodology, investigation and interpretation that remain human even when a machine assists. The taxonomy’s limits here are not a defect; they reflect the correct distinction between a tool that is used and a person who is answerable.
A consistent vocabulary for a changing landscape
As autonomous systems become more common, describing their involvement consistently — what was used, for what, and where human responsibility sat — will matter increasingly across journals and institutions. That consistency is what the CASRAI Dictionary works towards: a shared vocabulary so a statement about how AI contributed to a piece of research, and who is accountable for it, is understood the same way wherever it is recorded. AI agents may transform the pace of discovery; the durable principles — that authorship means accountability, that responsibility stays with people, and that AI contributions are disclosed rather than credited — are what keep research trustworthy as the tools grow more powerful.