A chatbot gives you an answer. An AI agent can take an action.
That difference changes the legal risk.
We are rapidly moving from AI systems which wait for a prompt and generate text towards systems capable of:
planning a sequence of tasks;
selecting tools;
accessing different systems;
retrieving information;
sending communications;
updating records;
and completing multi-stage workflows with limited human intervention.
This is commonly described as agentic AI.
At Clio’s EMEA AI Summit 2026, Ed Walters predicted that agentic AI would become one of the major legal-technology issues of 2027.
His warning was straightforward.
Human agents operate within understood limits of authority.
AI agents may not.
And when an AI system exceeds what somebody thought they had authorised, the consequences do not disappear into the machine.
The question is no longer only “Is the answer correct?” It is “What was the system allowed to do — and what happens if it does something else?”
Why agentic AI is different
A conventional generative-AI workflow normally requires repeated human instructions.
An agentic system may instead receive an objective, break it into steps, choose tools and perform actions towards that objective.
That creates an authority problem, a supervision problem, a confidentiality problem and an accountability problem.
Seven things to know first
1. “Agent” does not mean legal person.
An AI agent is still technology. Organisations and humans remain responsible for its deployment under the applicable legal framework.
2. Action changes the risk profile.
A wrong answer is one thing. A wrong answer automatically sent to a client, court or third party is another.
3. Permissions matter.
An agent should not have access to every document, system or external service merely because access might be useful.
4. Confidentiality becomes more complicated.
Agents may interact with multiple tools and data environments.
5. Human review must be meaningful.
A person clicking “approve” without understanding what the system did is not an effective safeguard.
6. Auditability matters.
You need to know what the agent accessed, what it did, what it sent and why.
7. Autonomy should increase governance, not reduce it.
The more a system can do without intervention, the clearer its boundaries should become.
What is agentic AI?
There is no single universally settled definition.
But the Competition and Markets Authority describes agentic systems broadly as systems capable of receiving a goal, navigating some complexity, planning, coordinating and taking actions — potentially across multiple services.
That is different from the familiar chatbot workflow.
You ask:
“Summarise this order.”
The model gives you text.
An agentic instruction might be:
“Review this matter, identify the outstanding directions, locate the relevant documents, create the tasks, update the chronology and draft the client email.”
The system may then decide which tools and data sources it needs to use.
The user is no longer controlling every intermediate step.
That can be extremely powerful.
It also makes governance much more important.
From answering to acting
Consider three levels of AI use.
Level
Example
Risk
Assist
Draft a proposed email.
Human sees output before anything happens.
Recommend
Identify who should receive the email and suggest attachments.
System influences a decision.
Act
Select recipients, retrieve files and send the email automatically.
Error becomes external conduct.
The same model can therefore become much riskier simply because of what it is permitted to do.
The scope-of-authority problem
Human agency law has spent centuries dealing with authority.
An agent can act for a principal within defined limits.
If the agent exceeds those limits, the legal consequences depend upon the relationship, representation and circumstances.
AI agents do not neatly map onto that doctrine.
But the analogy exposes the governance problem.
What exactly did you authorise?
Suppose you tell an AI agent:
“Resolve the outstanding administrative issues on this matter.”
Does that permit it to:
email the client?
contact the opponent?
change a deadline?
download a document?
upload a file?
share personal information with another service?
create a calendar event?
cancel an existing one?
incur a charge?
or submit something externally?
If the answer is not clear to the human deploying the system, the instruction is too broad for the consequences involved.
Do not give an AI agent a broad objective and then discover its authority from the actions it took.
Who is responsible when the agent gets it wrong?
The current regulatory direction is clear.
Calling software an “agent” does not allow an organisation to hand responsibility to it.
The CMA’s 2026 consumer guidance states the principle directly: a business remains responsible where an AI agent it deploys breaches consumer law.
The Information Commissioner’s Office similarly emphasises that AI agency does not remove human or organisational responsibility for personal-data processing.
And for regulated legal professionals, the SRA’s August 2026 AI warning notice says existing professional obligations continue to apply regardless of the tools used.
That matters.
Legal technology changes the mechanism.
It does not automatically change the responsible actor.
The confidentiality problem becomes harder when the AI can choose tools
With an ordinary chatbot, the user normally knows where information has been entered.
Agentic systems may be capable of invoking additional services.
That creates a new question:
Where did the information travel while the agent was completing the task?
At the summit, Walters highlighted the danger of validating the confidentiality arrangements of one AI provider while failing to consider the third-party tools or plugins an agent may invoke.
That concern aligns with the current regulatory direction.
The SRA has warned firms to understand the contractual and technical safeguards applying to AI systems before confidential information is used.
The ICO’s agentic-AI work emphasises:
purpose limitation;
data minimisation;
careful control over the systems and databases an agent can access;
and permission mechanisms where sensitive information is involved.
The principle of least privilege becomes critical.
An agent should receive:
the minimum access necessary to perform the defined task.
Not the maximum access which happens to be technologically possible.
A hallucination can become a chain of actions
Traditional generative-AI risk often looks like this:
Prompt → incorrect answer → human spots or misses error.
Agentic risk may look more like this:
incorrect inference → database update → generated correspondence → external communication → downstream system acts upon it.
The ICO has identified the potential for what it describes as cascading hallucinations: inaccurate information moving between tools, databases or stages of an agentic process.
That is qualitatively different from a wrong paragraph sitting in a draft.
The error has travelled.
Potentially, it has acted.
Human in the loop is not enough if the human is decorative
“Human in the loop” has become one of the most reassuring phrases in AI governance.
But it can mean almost nothing.
Imagine an agent completes 200 actions and gives a user a final screen saying:
Approve?
The user has ten seconds.
They cannot see:
which databases were queried;
what documents were used;
which assumptions were made;
what external tools were called;
or what intermediate outputs were discarded.
They click yes.
Technically, a human was “in the loop”.
Substantively, the human supervised nothing.
Meaningful supervision requires enough visibility, time and competence to intervene.
For regulated lawyers, supervision is already a live professional issue
The SRA’s effective-supervision guidance now expressly addresses AI-assisted and AI-generated work.
It says regulated firms and authorised individuals should consider what effective supervision of that work looks like in practice and ensure appropriate human review, scrutiny and professional judgment.
An authorised individual retains ultimate responsibility for legal services delivered with AI assistance.
That principle is likely to become more important as systems move from drafting towards autonomous execution.
Supervising a first draft is one thing.
Supervising a system capable of initiating consequential actions is another.
What could an agentic legal workflow look like?
Consider a routine matter-management task.
An agent might be asked to:
read the latest court order;
extract every deadline;
compare them with the existing calendar;
create missing tasks;
identify documents still required;
draft a client request;
prepare a chronology update;
and flag any apparent conflict between the order and the existing case plan.
There is substantial value here.
But notice how the risk changes between stages.
Extracting a date is relatively low risk.
Changing a calendar entry is higher.
Sending a communication externally is higher again.
Filing something with a court would be higher still.
Agentic governance should therefore be graduated.
The system does not need the same freedom at every stage.
Family Court work is a particularly poor place for invisible autonomy
Private children proceedings routinely involve highly sensitive material:
children’s information;
medical material;
school records;
domestic-abuse allegations;
addresses;
telephone numbers;
Cafcass reports;
police information;
and confidential court documents.
An agent which can roam across systems, select files and communicate externally therefore needs strict boundaries.
There is also a substantive danger.
Suppose an AI agent is told:
“Prepare the strongest case showing a pattern of coercive control.”
It might:
search thousands of messages;
categorise interactions;
select examples;
generate a chronology;
and produce a draft statement.
That may be useful.
But who checks whether:
the selected examples are representative?
contrary evidence was excluded?
neutral events were given a loaded interpretation?
dates are correct?
the child’s position has been conflated with a parent’s?
and legal terminology has been added which the evidence does not justify?
Autonomy increases the need for evidential discipline.
Using AI to organise a Family Court case?
AI can help enormously with large quantities of evidence.
But the more automated the workflow becomes, the more important source verification and human responsibility become.
JSH Law provides defined-scope support with evidence organisation, chronologies, AI-assisted draft review, statements, Cafcass material, appeals and hearing preparation.
The technology can assist with the processing. The legal and evidential judgment still needs a human owner.
Solicitors Regulation Authority — Agentic AI in Legal Services, July 2026.
Competition and Markets Authority — Using AI Agents: Complying with Consumer Law, March 2026.
Information Commissioner’s Office — Agentic AI: Data Protection and Privacy Risks.
https://jshlaw.co.uk/wp-content/uploads/2026/02/ChatGPT-Image-Feb-3-2026-03_26_42-AM.png10241536Jessica Susan Hillhttps://jshlaw.co.uk/wp-content/uploads/2026/01/jsh-law-logo-new-black-300x67.pngJessica Susan Hill2026-10-06 12:12:212026-10-06 12:12:25When AI Stops Answering and Starts Acting: Agentic AI, Legal Responsibility and the Next Professional Risk
Artificial intelligence is beginning to automate exactly the work the legal profession has traditionally used to train its junior lawyers.
That creates a problem much bigger than lost billable hours.
If AI undertakes the first draft, reviews the documents, extracts the clauses, summarises the authorities and constructs the chronology, where does the junior lawyer acquire the judgment needed to supervise that work later?
This was one of the most important questions raised at Clio’s EMEA AI Summit 2026.
Ed Walters, Clio’s Vice President of Legal Innovation and Strategy, argued that the profession has spent generations treating repetitive legal processing as though it were synonymous with legal training.
AI now forces us to ask whether that was ever really true.
If AI removes the apprenticeship work, the answer cannot be to remove the apprenticeship. We have to become much more deliberate about teaching judgment.
This is not a distant question about what legal practice might look like in twenty years.
It is a training question for firms, universities, SQE candidates, pupils, trainees and junior lawyers now.
The problem in one sentence
The tasks which AI can automate most easily are often the same tasks through which the profession historically expected junior lawyers to acquire experience.
Five things to understand first
1. AI is not simply making junior work faster.
In some workflows it may remove substantial parts of the task altogether.
2. Clients are unlikely to keep paying for avoidable manual processing.
If a task can reliably be completed in minutes, billing many hours for the same mechanical work becomes increasingly difficult to justify.
3. Repetitive work and legal judgment are not the same thing.
Reading thousands of documents may expose somebody to a case. It does not automatically teach them how to advise a frightened client or decide which point should be abandoned.
4. Junior lawyers still need experience.
The disappearance of low-value tasks does not remove the need for supervised practice. It means the supervision model must change.
5. AI competence is not enough.
The valuable lawyer will need both technological literacy and the distinctly human skills needed to test, challenge and improve machine output.
The traditional bargain: clients paid while juniors learned
Legal training has historically contained an informal economic bargain.
Junior lawyers performed large quantities of labour which had to be done:
document review;
legal research;
due diligence;
chronology preparation;
first drafts;
bundles;
disclosure exercises;
precedent adaptation;
and checking large amounts of factual material.
Clients paid for those hours.
The firm generated revenue.
And somewhere along the way, the junior was expected to become a lawyer.
The theory was rarely stated explicitly.
It was simply how professional formation worked.
You did the work.
You saw more files.
You absorbed the craft.
You became more senior.
Eventually, somebody else did the repetitive work and you exercised the judgment.
AI disrupts that sequence.
The machines are moving into the bottom of the pyramid
The work currently most susceptible to automation is disproportionately work historically allocated to junior lawyers.
Legal AI systems can already assist with:
large-scale document review;
extracting clauses across contracts;
identifying dates and events;
constructing draft chronologies;
summarising judgments;
producing first drafts;
comparing pleadings;
organising disclosure;
identifying potential authorities;
and standardising documents against templates or playbooks.
None of that means the work no longer requires supervision.
It means the human labour required to produce the first version can collapse dramatically.
At the summit, practical examples included work which previously took hours being reduced to minutes, subject to human review.
That is commercially attractive.
It is also professionally disruptive.
Why would a client continue to pay fifteen hours for a ten-minute task?
This is where the training issue becomes an economic issue.
For generations, clients effectively helped finance junior development because junior labour was necessary to complete the work.
That bargain becomes unstable where technology can undertake the mechanical component substantially faster.
A client may reasonably ask:
“If the firm has technology capable of doing this in minutes, why am I paying for somebody to perform it manually for a day?”
That does not mean every AI-assisted task should suddenly become cheap.
The value may reside in:
supervision;
verification;
strategy;
risk;
professional responsibility;
and the consequence of getting the answer wrong.
But it does mean time spent on avoidable mechanical processing becomes harder to defend as the core source of legal value.
That puts pressure on both the billable-hour model and the training structure built around it.
Perhaps we have confused endurance with education
There is an uncomfortable possibility here.
What if spending eight years carrying out repetitive legal processing was never the reason somebody eventually developed good judgment?
What if judgment developed because, around that work, they also:
watched experienced lawyers make decisions;
sat in client meetings;
saw negotiations succeed and fail;
received detailed corrections;
observed hearings;
made mistakes under supervision;
watched evidence collapse under scrutiny;
learned which theoretically available argument was tactically foolish;
and gradually acquired professional pattern recognition?
If that is true, removing repetitive work does not necessarily destroy legal education.
It may expose which parts of legal education were actually valuable all along.
Perhaps the problem is not that AI will stop juniors learning through drudgery. Perhaps the problem is that we never designed the learning deliberately enough in the first place.
The regulatory definition of competence is already much wider than technical processing
The Solicitors Regulation Authority’s Statement of Solicitor Competence is instructive.
It does not define competence simply as the ability to retrieve law or produce documents.
It includes:
ethics;
professionalism;
judgment;
communication;
client relationships;
working with others;
managing work;
legal knowledge;
and recognising one’s own limitations.
The SRA’s 2026 assessment of continuing competence is particularly interesting in this context.
It identified a continuing tendency for some learning and development to focus too narrowly on technical legal knowledge rather than the wider capabilities required for competent practice.
It also placed greater emphasis on ethical reflection and the ability to deal with unfamiliar professional dilemmas.
That matters because AI is making technical retrieval and first-draft production progressively easier.
The harder skills are becoming more visible.
AI changes the starting line, not the finishing line
One of the most useful propositions from the summit was that AI output should be treated as a first draft, however polished it appears.
That is exactly the right mindset.
The dangerous feature of modern generative AI is not merely that it can be wrong.
It is that its first draft may look far more finished than a human first draft.
That creates temptation.
The junior may think:
“This looks complete.”
The supervisor may think:
“This looks good enough.”
The client may never see the gap.
But the real professional work begins with questions such as:
What did the system miss?
What has it assumed?
Which authority actually controls?
What evidence contradicts this?
What would the other side say?
What is technically correct but commercially foolish?
What is legally available but ethically wrong?
Which point should we deliberately not run?
What does this particular client actually need?
Those are not merely drafting questions.
They are judgment questions.
The “centaur lawyer”: machine capability plus human judgment
Walters used the history of computer chess to illustrate the point.
When computers became better than humans at brute-force calculation, the lesson was not immediately that human chess insight had become worthless.
For a period, combinations of human strategy and machine calculation proved extraordinarily powerful.
The legal equivalent is useful.
The AI can:
read fast;
compare fast;
retrieve fast;
draft fast;
and calculate across large amounts of material.
The lawyer needs to:
frame the problem correctly;
understand the person affected;
test the output;
exercise ethical judgment;
balance risk;
recognise uncertainty;
understand institutional context;
and accept responsibility for the advice.
The future lawyer is therefore not valuable because they can compete with a machine on reading speed.
They are valuable because they know what the machine’s answer means and whether anyone should act upon it.
But there is a genuine deskilling risk
It would be naïve to say that removing routine work carries no downside.
Some repetitive tasks teach useful things.
Reading a hundred judgments can develop a feel for judicial reasoning.
Drafting repeatedly can improve structure.
Building chronologies can reveal how factual patterns emerge.
Reviewing disclosure can teach what evidence looks like in the real world rather than in a textbook.
If the junior presses a button and sees only the finished answer, some of that learning disappears.
The answer is not to force people to perform obsolete labour for educational theatre.
It is to preserve the learning outcome while changing the exercise.
For example:
Old training task
AI-era training task
Manually summarise 20 cases
Audit an AI synthesis, identify where it overstates authority and explain which case actually controls.
Build chronology from scratch
Verify an AI chronology against primary evidence and identify missing context and disputed events.
Produce a first draft
Critique three AI drafts and justify which approach best serves the client’s objective.
Search for all possible arguments
Decide which arguments should not be pursued and explain why.
Document review
Test an AI document review for false negatives, context loss and significance.
That is harder training.
It is also closer to the work senior lawyers are actually paid to do.
What should the new apprenticeship teach?
If we were designing junior legal development now rather than inheriting it from the last century, I would make at least eight capabilities explicit.
1. Verification
Do not ask only whether an answer looks convincing. Trace it back to authority and evidence.
2. Issue selection
The ability to identify the few points which determine the case is more valuable when AI can generate fifty.
3. Counter-analysis
Train juniors to attack the draft they have just produced.
4. Ethical reasoning
Legal permission and professional wisdom are not identical.
5. Client counselling
A client rarely needs only the law. They need help understanding choices, consequences, risk and uncertainty.
6. Evidence literacy
What does the source actually establish? What is allegation, inference, hearsay, opinion or finding?
7. Advocacy and judgment
Know which point matters, when to concede and when further argument damages rather than strengthens the case.
8. AI supervision
Understand enough about the tool to know when it is being asked to perform a task it cannot safely perform.
The SQE era makes this especially timely
The profession is already reconsidering how competence is demonstrated.
The Solicitors Qualifying Examination assesses knowledge and practical legal skills against the competencies expected of solicitors.
But qualification is only a starting point.
A lawyer develops professional judgment through repeated exposure to uncertain, human and ethically difficult situations.
That is precisely the material which cannot simply be automated away.
AI therefore strengthens the case for:
high-quality supervision;
observing experienced practitioners;
structured reflection;
feedback on reasoning, not merely drafting;
simulation of difficult professional choices;
and deliberate exposure to client-facing work.
Qualification frameworks will increasingly need to ask not only:
“Can this person produce the legal work?”
but:
“Can this person supervise technology producing legal work?”
A junior lawyer should not become the human rubber stamp
There is one training model we should avoid completely.
AI produces the work.
A junior is told to “check it”.
The junior has never performed the underlying task.
They are under time pressure.
The output looks polished.
They do not know where the model is most likely to fail.
They approve it.
That is not supervision.
It is procedural decoration.
A person can only meaningfully supervise a task if they have enough competence to recognise failure.
This creates a transition problem.
The generation entering practice now may need to learn both:
how the underlying legal task works;
and how to supervise technology performing it.
That is likely to require more intentional training, not less.
Done properly, this could also improve access to justice
There is an optimistic version of this transition.
If technology removes genuinely mechanical labour, legal professionals may be able to deliver more assistance at lower cost.
The Government’s 2026 Legal Services Advisory AI Growth Lab explicitly identifies improved productivity, affordability and access to justice as potential benefits of responsible legal AI.
That matters enormously.
There is no public interest in preserving expensive inefficiency merely because it once formed part of professional training.
The challenge is to redesign training while allowing consumers to receive the benefit of lower-friction legal work.
We should not make clients pay for obsolete processes in order to preserve a professional apprenticeship model.
We should build a better apprenticeship model.
What this means for Family Court work
The distinction is particularly important in family justice.
AI can help organise:
messages;
chronologies;
court orders;
allegations;
disclosure;
Cafcass material;
school records;
medical records;
and draft documents.
But the difficult work remains profoundly human.
Is this evidence relevant to welfare?
Does a pattern actually exist?
Is an allegation established?
What alternative explanation has been considered?
What will this proposed arrangement mean for the child?
What should be said to a traumatised client who has a technically arguable point which may nevertheless damage their case?
AI can accelerate the processing.
It cannot relieve the human professional of responsibility for those judgments.
The JSH Law test: what should remain above the line?
Before automating a legal task, ask two questions.
What part of this task is processing?
and:
What part requires professional judgment?
Good candidate for AI assistance
Human responsibility should remain visible
Extraction
Significance
Comparison
Judgment
First draft
Final advice
Pattern search
Finding of fact
Legal retrieval
Application and strategy
Administrative processing
Ethics and responsibility
The lawyer of 2035 may need fewer mechanical skills — and more difficult human ones
None of this means legal knowledge becomes optional.
You cannot evaluate a machine’s legal answer if you do not understand law.
You cannot detect evidential distortion if you do not understand evidence.
You cannot supervise drafting if you cannot recognise bad drafting.
But knowledge alone will no longer differentiate lawyers in the same way when basic retrieval becomes cheap.
The lawyer’s value moves towards:
judgment;
discernment;
strategy;
ethical reasoning;
human understanding;
advocacy;
responsibility;
and trust.
That should be good news for the profession.
Those are the reasons many people wanted to become lawyers in the first place.
We should not train tomorrow’s lawyers to compete with machines at the work machines do best. We should train them to become exceptional at the work for which human judgment still matters.
AI may remove parts of the old apprenticeship.
Our responsibility is to replace them with something better.
JSH Law: AI should augment judgment, not replace it
JSH Law’s work on responsible legal AI focuses on the point where technology meets evidence, human judgment and access to justice.
For litigants in person, defined-scope support can include reviewing AI-assisted documents, organising evidence, creating chronologies, preparing statements and position documents, analysing Cafcass material and preparing for hearings.
The objective is not more AI-generated legal material. It is clearer evidence and better human decision-making.
Solicitors Regulation Authority — Statement of Solicitor Competence.
Solicitors Regulation Authority — Annual Assessment of Continuing Competence 2026.
Solicitors Regulation Authority — Misuse of AI Warning Notice, August 2026.
UK Government — Legal Services Advisory AI Growth Lab, 2026.
https://jshlaw.co.uk/wp-content/uploads/2026/02/ChatGPT-Image-Feb-3-2026-03_26_42-AM.png10241536Jessica Susan Hillhttps://jshlaw.co.uk/wp-content/uploads/2026/01/jsh-law-logo-new-black-300x67.pngJessica Susan Hill2026-10-06 12:08:392026-10-06 12:08:43If AI Does the Junior Work, Who Trains the Lawyers of 2035?
Artificial intelligence has removed one of litigation’s old restraints: producing another ten pages now costs almost nothing.
Reading them still does.
Checking them still does.
Answering them still does.
And eventually somebody — often a judge working inside an already overloaded justice system — has to work out which parts actually matter.
That is the problem former Lord Chancellor Sir Robert Buckland KC has described as a growing wave of “AI slop” reaching the courts.
His argument is that existing costs powers should be used more confidently where irresponsible use of artificial intelligence creates unnecessary work and expense.
There is considerable force in that.
But there is another side to this debate which matters particularly to JSH Law.
Many litigants in person are turning to AI because they cannot afford legal representation.
Used properly, generative AI can help someone understand procedure, organise evidence, reduce repetition and articulate a case they might otherwise struggle to present at all.
So the answer cannot become:
“You used AI. Therefore your document is suspect — and you may have to pay for it.”
The real question is harder:
when does AI assistance improve access to justice, and when does the way somebody uses it cross the line into unreasonable litigation conduct?
The JSH Law position
The problem is not AI-generated text.
The problem is unverified, disproportionate or irrelevant material being transferred from the machine to the court without sufficient human judgment.
If you file it, you remain responsible for it. But sanctions should respond to unreasonable conduct — not merely to the fact that an unrepresented person used technology to help them participate.
Seven things to know first
1. “AI slop” is a real procedural problem.
Generative AI can produce huge quantities of fluent, repetitive and apparently sophisticated legal material almost instantly.
2. Cheap production does not mean zero cost.
The burden may simply move to the other party, their lawyers, court staff, judges and the taxpayer.
3. The person filing the document remains responsible.
“The AI wrote it” does not excuse fabricated authorities, inaccurate evidence, irrelevance or failure to comply with a court order.
4. Sir Robert Buckland is not proposing automatic punishment for AI-using litigants in person.
He expressly distinguishes uninformed use from persistent or irresponsible conduct.
5. The Family Court is not the ordinary civil costs regime.
Children proceedings have a longstanding general practice against costs orders, subject to the court’s discretion in exceptional cases involving unreasonable or reprehensible conduct.
6. The Civil Justice Council is already looking closely at litigants in person.
Its June 2026 update identifies litigants in person, witness statements and expert evidence as areas requiring further consideration.
7. Good use of AI should often produce less material, not more.
The best legal use of AI is often subtraction: finding the central issue, removing duplication and making the evidence easier to understand.
What exactly is “AI slop”?
The phrase is inelegant.
But the problem it describes is real.
In his September 2026 address to the Costs Lawyers Conference, Sir Robert Buckland used the expression to describe the production of large quantities of superficially impressive but unnecessary legal material using generative AI.
That might include:
overlong pleadings;
repetitive witness statements;
generic legal submissions;
dozens of weak grounds of challenge;
irrelevant authorities;
multiple documents covering the same issue;
fabricated case citations;
quotations which the cited judgment does not contain;
or lengthy arguments which have no material bearing on what the court must decide.
But the important point is this:
the defining feature is not that AI was involved.
A concise, accurate and relevant position statement does not become “slop” simply because AI assisted with its structure.
Equally, a 40-page repetitive submission is not transformed into good advocacy merely because a human typed every word.
The real failure occurs when generation replaces judgment.
What Sir Robert Buckland actually said
The full speech is considerably more nuanced than the shorthand appearing on social media.
Buckland’s starting point is that AI itself is not the enemy.
He expressly recognises that it can:
help a litigant understand procedural rules;
assist with research;
identify patterns across large bodies of material;
help lawyers organise information;
and potentially reduce the cost of some legal services.
His distinction is between:
using AI to assist judgment
and:
using AI instead of exercising judgment.
That is an important distinction.
He also proposes a straightforward principle:
If you put material before the court, responsibility for that material remains yours.
AI does not become an intermediary which absorbs responsibility.
If a submission contains an authority which does not exist, the problem remains.
If the facts have been misstated, the problem remains.
If the document ignores a page limit, the problem remains.
If somebody files 100 pages to make a point which reasonably required five, the burden imposed on everyone else does not disappear merely because a machine generated the excess.
AI has changed the economics of litigation
This is, in my view, the strongest part of Buckland’s analysis.
Historically, legal prolixity had a natural brake.
Someone had to produce the words.
A lawyer’s time cost money.
Even a litigant in person had to spend hours producing a lengthy document manually.
Generative AI changes that calculation.
A person can now generate in minutes:
a pleading;
a witness statement;
a chronology;
a skeleton argument;
a list of authorities;
a response;
a reply;
and then a reply to the reply.
The marginal cost of production may be almost nothing.
But the consumption cost remains.
Another party may have to pay a lawyer to read it.
Every authority may need checking.
The court must establish whether factual propositions are supported.
Judicial time is consumed identifying the few points which actually matter.
Court staff process the material.
Hearings become longer.
Bundles become larger.
Deadlines become harder to manage.
The cost has not disappeared.
It has been externalised.
AI can make speech cheap without making attention cheap.
That is the new procedural problem.
Hancox: when 132,000 words obscure rather than assist
The September 2026 Employment Appeal Tribunal decision in Hancox v Sutherland & Others [2026] EAT 139 provides an extraordinary example.
The appellant, acting in person, filed a skeleton argument created using ChatGPT.
It was approximately:
300 pages and almost 132,000 words.
The EAT said the document served to obscure rather than illuminate the case.
It did not comply with the applicable Practice Direction.
The appellant himself accepted at the hearing that much of it lacked credibility, and ultimately it was not relied upon.
With assistance from counsel, the actual argument before the EAT was reduced to a focused ground.
That contrast is instructive.
The machine was capable of producing 132,000 words.
The case did not require 132,000 words.
The EAT emphasised that anyone using generative AI must take personal responsibility for ensuring that court documents:
comply with applicable procedural rules;
are checked as thoroughly as reasonably possible for factual, evidential and legal accuracy;
do not mislead the court;
contain only relevant points;
focus on the central or strongest arguments;
and avoid undue repetition.
That is not anti-AI.
It is ordinary litigation discipline applied to a new tool.
Could irresponsible AI use lead to a civil costs order?
Potentially, yes.
Buckland’s point is that the ordinary civil courts do not necessarily need a special new rule labelled:
“Costs caused by artificial intelligence.”
CPR Part 44 already permits the court to consider the conduct of the parties when deciding costs.
Relevant conduct includes:
whether it was reasonable to raise, pursue or contest a particular allegation or issue; and
the manner in which a party pursued or defended the case or a particular issue.
AI-generated conduct is still conduct.
The relevant question therefore remains:
Was it reasonable?
Imagine a litigant is told:
a cited case does not exist;
their argument falls outside the issues ordered for determination;
their skeleton must not exceed 20 pages;
and repeated material should not be filed.
If they then deliberately use AI to generate another 80 pages containing the same rejected material, the issue is not really whether ChatGPT was involved.
The issue is procedural conduct after warning.
Could the same thing happen in the Family Court?
Yes — but this is where the distinction between civil litigation and family proceedings becomes essential.
Under FPR 28.1:
the court may at any time make such order as to costs as it thinks just.
FPR 28.2 also applies substantial parts of CPR Parts 44, 46 and 47 to family proceedings, subject to specified modifications and the special regime for financial remedy proceedings.
But children proceedings have an important and longstanding approach of their own.
In Re E (Children: Costs) [2025] EWCA Civ 183, the Court of Appeal restated that there is a general practice of not awarding costs against a party in family proceedings concerning children.
The court nevertheless retains discretion in exceptional circumstances.
Those circumstances can include unreasonable or reprehensible litigation conduct.
The principle applies in both public and private law children proceedings.
That distinction matters enormously.
A parent should not think:
“If I lose an allegation, I will automatically have to pay the other parent’s legal costs.”
That is not the position.
Nor should parents be discouraged from raising genuine safeguarding concerns because they fear an adverse costs order simply because a court ultimately reaches a different conclusion.
But there is equally no rule saying:
“Anything goes because this is a children case.”
Where litigation conduct becomes objectively unreasonable or reprehensible, costs remain within the court’s armoury.
The key Family Court distinction
Losing an argument is not the same as litigating unreasonably.
Using AI is not the same as litigating unreasonably.
But repeatedly filing excessive, inaccurate or prohibited material after clear directions or warnings could potentially become relevant to the court’s assessment of conduct.
There is, as yet, no need to invent an “AI costs doctrine” for children proceedings.
The existing principles are capable of responding to conduct if and when the facts justify it.
Litigants in person need a different conversation
This is the part of Buckland’s speech I think deserves particular emphasis.
He expressly rejects the equation:
“Litigant in person + AI = costs sanction.”
That would be both unfair and counterproductive.
People frequently represent themselves because:
they cannot afford solicitors and counsel;
they do not qualify for legal aid;
they have exhausted available funds;
they cannot find representation;
or the economics of the case simply make full representation impossible.
In the Family Court they may also be trying to prepare documents while:
separated from a child;
experiencing domestic abuse or post-separation abuse;
managing disability or neurodivergence;
dealing with trauma;
working around childcare;
or facing a hearing they barely understand.
Generative AI can be genuinely useful in that situation.
It can explain unfamiliar terminology.
It can help organise hundreds of messages by date.
It can identify duplicated paragraphs.
It can convert a chaotic notebook into a provisional chronology.
It can suggest headings for a statement.
It can explain what a court order appears to require.
It can help someone formulate questions for professional advice.
That has genuine access-to-justice value.
So we need to distinguish between:
a person who does not yet understand the limits of the technology
and:
a person who knowingly persists in unreasonable litigation conduct after the problem has been identified.
Buckland’s example is useful.
A litigant who genuinely thinks an AI-generated authority is correct may need education and an opportunity to correct it.
A litigant who has been told the authority is fictitious and continues to rely upon it presents a different issue.
That distinction is fair.
There is another danger: “AI slop” could itself become a litigation weapon
Once a phrase enters legal culture, parties start using it against each other.
I can easily imagine future correspondence saying:
“The applicant’s statement is plainly AI slop and should be disregarded.”
Or:
“The respondent has apparently used ChatGPT to manufacture safeguarding allegations.”
That cannot become a substitute for answering the material.
In a high-conflict Family Court case, accusations about the other party’s AI use could easily become another layer of procedural warfare.
The proper questions are more disciplined:
Is the factual assertion supported by evidence?
Can its source be identified?
Has an allegation been converted into a fact?
Is the authority genuine?
Does it actually support the proposition for which it is cited?
Does the document comply with the court’s directions?
Is the point relevant?
Is the material proportionate?
Is it repetitive?
Has the author adopted and checked it?
If the answer to those questions is satisfactory, whether AI assisted with punctuation or structure may be of very little importance.
Focus on the defect.
Not the software.
The Civil Justice Council is already wrestling with exactly this distinction
In June 2026, the Civil Justice Council published an update on its work concerning AI in the preparation of court documents.
There was a strong degree of agreement that, for professional lawyers preparing pleadings, advocacy documents and skeleton arguments, additional AI-specific formal requirements are not currently necessary.
The reasoning is significant:
existing professional responsibility frameworks should generally be sufficient.
But the Working Group identified three areas requiring further attention:
witness statements;
expert evidence; and
litigants in person.
Witness statements raise particular concerns because AI may reshape, embellish or subtly alter personal recollection.
Experts raise questions about methodology and transparency.
Litigants in person create a different problem:
how do you protect the integrity of court proceedings without depriving the people with least access to legal support of one of the tools which may help them participate?
That is the access-to-justice question at the heart of this debate.
Has AI made your Family Court document longer rather than clearer?
You may not need another 20 pages.
You may need somebody to identify which three pages actually matter.
JSH Law provides defined-scope support with:
reviewing and reducing AI-assisted drafts;
evidence organisation;
chronologies;
witness-statement preparation support;
position statements;
schedules of issues or allegations;
Cafcass and Child Impact Report responses;
appeal paperwork;
court-bundle preparation support;
source and authority checking;
and hearing preparation.
The purpose is not to replace your evidence with AI. It is to make your real case easier for the court to understand.
https://jshlaw.co.uk/wp-content/uploads/2026/02/ChatGPT-Image-Feb-3-2026-03_26_42-AM.png10241536Jessica Susan Hillhttps://jshlaw.co.uk/wp-content/uploads/2026/01/jsh-law-logo-new-black-300x67.pngJessica Susan Hill2026-10-05 09:38:572026-10-05 09:39:01AI Slop, Costs and Litigants in Person: Should Courts Sanction Bad AI Use?
Should an artificial intelligence ever become a legal person?
At first glance, the question sounds like science fiction.
It is not.
The law already knows how to create persons which are not human beings.
A company can own property, enter contracts, sue, be sued and owe legal duties.
So the serious question is not whether an AI looks sufficiently human.
It is:
Would recognising an autonomous AI as a legal person solve a real legal problem — or simply give human beings somewhere convenient to put responsibility when things go wrong?
That question sits at the centre of fascinating new doctoral research by Imogen Rivers, whose 2026 Oxford DPhil is entitled Robots from Venus, Robots from Mars: Legal Personhood for Autonomous AI.
Rivers approaches the problem from two directions.
First, she asks whether some autonomous robots should acquire legal rights against particular forms of human conduct directed towards them.
Then she turns the problem around.
Could highly autonomous systems — specifically lethal autonomous weapons — acquire legal duties when their own actions cause harm?
That leads directly into one of the hardest questions in modern AI law:
When an autonomous system causes serious harm, who should answer for it?
Research acknowledgement
This article responds to the doctoral research of Imogen Rivers, whose DPhil in Philosophy at the University of Oxford, Robots from Venus, Robots from Mars: Legal Personhood for Autonomous AI, was deposited in the Oxford University Research Archive in 2026.
Rivers’ research was supported by the University of Oxford’s Institute for Ethics in AI. Her wider research concerns legal personhood, autonomous decision-making, responsibility for AI-caused harms and the regulation of human-AI interaction.
This article does not attempt to reproduce Rivers’ dissertation. It examines the legal questions raised by her published thesis abstract and associated work from a JSH Law perspective, with particular emphasis on responsibility, accountability, human control and the future of legal AI.
The key distinction
Legal personhood is not the same thing as consciousness.
Nor does making something a legal person necessarily mean declaring it human, sentient or morally equivalent to a human being.
Legal personality is a mechanism through which law allocates rights, duties, powers and responsibilities.
The difficult question is therefore not simply “Is AI alive?” It is “What legal work would AI personhood actually perform?”
Five things to understand first
1. Current AI systems are not independent legal persons under English law.
The humans and organisations developing, providing, deploying and using them remain the relevant legal actors.
2. Legal personhood and moral personhood are different questions.
The law can create legal personality for functional reasons without deciding whether an entity has consciousness or feelings.
3. Rivers examines both sides of personhood.
Her thesis considers rights for certain autonomous robots and duties for lethal autonomous weapons.
4. AI autonomy creates a genuine responsibility problem.
As systems exercise greater operational independence, responsibility can become distributed between developers, manufacturers, commanders, deployers, operators and organisations.
5. But creating an AI legal person could also create a responsibility escape hatch.
If the machine becomes the defendant while the humans who designed and deployed it become harder to reach, legal personhood may weaken accountability rather than strengthen it.
What is Imogen Rivers actually proposing?
Rivers defines legal personhood in functional terms:
the capacity to hold legal rights and/or bear legal responsibilities.
Her thesis is divided into two strikingly different problems.
Part One: wrongs inflicted on robots
Rivers examines what she calls sexual autonomous robots: humanoid AI-based autonomous systems designed to enact sexual relationships with humans.
She asks what the law should do about conduct towards such a robot which, if performed towards a human being, would amount to sexual violence.
Her argument is unusual because it does not simply depend upon saying:
“The robot suffers just like a human, therefore protect it.”
Instead, she develops what she describes as a politically liberal justification for legal prohibition, grounded in Rawlsian public reasoning and the political virtues required within a society built upon reciprocity between free and equal citizens.
Within that argument, legal personhood — including rights held by the autonomous robot — becomes part of the possible legal architecture.
Part Two: wrongs caused by robots
The second half turns the direction of responsibility around.
Rivers considers lethal autonomous weapons: systems capable of selecting and engaging targets without further human intervention.
Her proposal includes treating such systems, in certain respects, as quasi-employees of a military enterprise.
That could allow a legal duty to attach to the autonomous system itself — for example, a duty not to direct attacks against civilians — alongside reforms to strict product liability.
It is a provocative proposition.
It is also much more sophisticated than saying:
“The robot made the decision, so blame the robot.”
What does “legal personhood” actually mean?
The word person causes immediate confusion.
In ordinary language, a person means a human being.
In law, the category is wider.
Companies are the obvious example.
A limited company can:
own assets;
enter agreements;
owe money;
hold rights;
owe statutory duties;
sue;
and be sued.
Nobody therefore needs to believe that Microsoft or Tesco possesses consciousness in order to understand that a company has legal personality.
Legal personality is a legal technology.
It creates an entity around which rights, duties, assets and responsibility can be organised.
That is why AI personhood cannot be dismissed merely by saying:
“But a robot isn’t human.”
That answers the wrong question.
The serious question is:
Would creating an artificial legal person improve the allocation of rights, duties and responsibility?
Does an AI need to be conscious before law could recognise it?
Not necessarily.
This is where the legal-personhood debate needs to be separated from the rapidly developing debate about AI consciousness.
A legal system could theoretically give an artificial entity particular rights or duties for instrumental reasons even if nobody believed that it experienced anything internally.
Conversely, even if convincing evidence of machine consciousness emerged one day, that would not automatically answer:
which rights the system should possess;
whether it should owe legal duties;
whether it should own property;
whether it should bear civil liability;
or whether giving it personhood would improve human accountability.
Moral status and legal status may overlap.
They are not identical.
This distinction matters because debates about robot rights can become stuck on one question:
“Can the robot suffer?”
Rivers’ work asks a broader legal question:
What kind of legal relationships should exist between humans and sufficiently autonomous machines?
Part One: could an autonomous robot hold legal rights?
This may initially be the more controversial half of Rivers’ thesis.
If a robot cannot suffer, why should conduct directed towards it be prohibited?
The answer may depend upon what we think law is protecting.
Some laws protect an identifiable victim from immediate injury.
Others also maintain broader social norms.
Law can express something about:
what society permits people to practise;
what kinds of conduct it normalises;
what forms of domination it encourages;
and what habits citizens cultivate through repeated behaviour.
Rivers’ published work on sexual autonomous robots develops precisely that kind of argument.
She asks whether a politically liberal society could have public reasons for prohibiting conduct towards autonomous robots which simulates sexual violence, even without relying upon a controversial comprehensive moral doctrine.
That is a fundamentally different proposition from:
“The robot necessarily experiences sexual harm like a human victim.”
Why regulate a wrong if the robot may not experience it?
Consider an analogy.
There are things society may choose to restrict not solely because of direct physical injury to an immediate victim, but because of what repeated participation in that conduct may communicate, cultivate or normalise.
That does not automatically prove Rivers’ argument.
But it exposes why the simplistic response:
“It’s only a machine, so anything goes”
is legally and ethically incomplete.
If increasingly realistic autonomous systems enter intimate human relationships, lawmakers may eventually have to ask:
what behaviours those products are designed to facilitate;
what markets manufacturers should be permitted to create;
what norms particular uses reinforce;
what interaction with human-like autonomous entities teaches users;
and whether protecting humans sometimes requires regulating conduct directed towards machines.
Those are uncomfortable questions.
That is precisely why they should be confronted before the technology becomes normal.
But does regulating human behaviour require giving the robot rights?
This is where I think Rivers’ argument creates an important further debate.
Suppose society concludes that a particular form of human-robot interaction should be prohibited.
There are at least two ways to construct the law.
One approach says:
the autonomous robot possesses a legal right which the human violates.
The other says:
the human conduct itself is prohibited because of its effect upon human society, users or protected public interests.
Those are not the same legal architecture.
The second does not require personhood for the machine.
That raises a central question for anyone advocating electronic personality:
What does granting the AI the right achieve that regulating the human would not?
If there is a clear answer, personhood may be useful.
If there is not, creating an entirely new category of legal persons may introduce more problems than it solves.
The harder problem: the AI responsibility gap
The second half of Rivers’ thesis addresses a problem which will become increasingly difficult to avoid.
Imagine an autonomous system causes catastrophic harm.
Who is responsible?
The programmer?
The company that trained the system?
The manufacturer?
The organisation which bought it?
The commander who authorised its deployment?
The operator who switched it on?
The person who set its operational parameters?
The state?
Or, if the system adapted its behaviour after deployment in a way none of those humans specifically selected:
the AI itself?
This is often described as a responsibility gap.
The concern is that traditional liability rules look backwards for an identifiable human decision, while sophisticated autonomous systems can produce behaviour which emerges through complex interactions between:
training;
data;
software;
hardware;
environment;
operational parameters;
human instructions;
and machine adaptation.
The more distributed the causal chain becomes, the easier it may become for every human actor to say:
“That particular outcome wasn’t my decision.”
Lethal autonomous weapons make the problem impossible to treat as theoretical
Autonomous weapon systems are already a major subject of international negotiation.
The United Nations Group of Governmental Experts on lethal autonomous weapon systems met twice during 2026 to continue work on the elements of a possible international instrument.
In August 2026, the UN Secretary-General and the President of the International Committee of the Red Cross renewed their call for urgent legally binding international rules.
The concern is not difficult to understand.
A system which, once activated, can select and apply force to targets creates a fundamental separation between:
the human decision to deploy the system
and:
the eventual decision about exactly who or what is struck.
That distance matters legally.
International humanitarian law requires judgments about matters including:
distinction;
proportionality;
precautions;
civilian status;
surrender;
hors de combat status;
and changing battlefield circumstances.
Those are not merely computational questions.
They are legal judgments applied to the circumstances of an attack.
Rivers’ intriguing proposal: the autonomous weapon as “quasi-employee”
This is one of the most interesting ideas in the thesis.
Rivers argues that an autonomous weapon can be understood, in some respects, as a quasi-employee of a military enterprise.
The analogy matters because law already knows how to allocate responsibility where an organisation acts through another legal actor.
Employment law and tort law, for example, have long dealt with circumstances in which an organisation can become responsible for conduct carried out by somebody operating within its enterprise.
If autonomous AI increasingly performs functions previously allocated to humans, the question becomes:
Should law treat the AI only as a product — or also recognise that it occupies an operational role previously performed by a legally responsible actor?
Rivers’ proposed answer appears to be:
potentially both.
That is why her thesis also considers reform to strict product-liability mechanisms alongside legal personality.
The AI may sit simultaneously in two conceptual spaces:
product and actor.
That hybrid idea deserves serious attention.
But current international humanitarian law takes a very different starting point
The International Committee of the Red Cross has consistently emphasised that legal responsibility for the use of force remains human.
Its position is that international-humanitarian-law obligations apply to those who plan, decide upon and carry out attacks.
They cannot simply be transferred to:
a machine;
a computer program;
or a weapon system.
That is a profound difference from the direction Rivers is exploring.
The ICRC’s concern is understandable.
If humans can delegate the legal duty itself to the weapon, human control may begin to disappear precisely where the consequences become most serious.
The international debate therefore currently focuses heavily upon:
human judgment, human control and human accountability.
Rivers’ work challenges us to ask whether that framework will remain conceptually sufficient as autonomous systems acquire more operational independence.
The biggest danger: AI personhood as a liability sink
This is where I think any proposal for AI legal personhood must face its hardest test.
Imagine a lethal autonomous system unlawfully kills civilians.
The manufacturer says:
“The system was operating after deployment.”
The programmer says:
“I did not code that specific decision.”
The commander says:
“The system selected the target.”
The operator says:
“I acted within approved parameters.”
And the legal answer becomes:
“Then sue the robot.”
What has been achieved?
If the robot:
owns nothing;
earns nothing;
has no insurer;
cannot suffer punishment;
cannot experience deterrence;
and can simply be deleted and replaced,
the apparent allocation of responsibility may actually remove it.
If giving the machine legal personality leaves the victim with nobody solvent to hold accountable, personhood has failed.
This is what I would call responsibility laundering.
The technology becomes legally sophisticated enough to bear the blame but economically empty enough to bear none of the consequences.
That must be avoided.
Can an artificial legal person meaningfully be punished?
Legal responsibility normally has consequences.
A natural person may face:
damages;
a fine;
loss of liberty;
professional consequences;
reputational consequences;
or criminal punishment.
A company can:
pay damages;
lose assets;
lose licences;
be fined;
face regulatory restriction;
or ultimately cease to exist.
What is the equivalent for an AI?
Deletion?
Model modification?
Loss of permissions?
Removal from deployment?
Confiscation of an asset pool?
Compulsory insurance?
Those mechanisms are possible.
But notice what happens when we examine them closely.
Most eventually lead back to:
the owner;
the developer;
the deployer;
the insurer;
or the organisation benefiting from the system.
That raises a fair challenge:
if those actors ultimately bear the practical consequence anyway, what additional value does AI personhood provide?
There are other ways to close the responsibility gap
Legal personhood is one regulatory option.
It is not the only one.
Model
Where responsibility sits
Key issue
Human control model
Commander / operator / deployer
Can meaningful human control really be maintained as autonomy increases?
Product liability
Manufacturer / producer
What counts as a defect when behaviour emerges after deployment?
Strict enterprise liability
Organisation which deploys or benefits from the AI
May improve compensation but impose broad liability regardless of fault.
Mandatory insurance
Insurance pool attached to deployment
Compensates victims but does not itself resolve moral or criminal responsibility.
AI legal personhood
The autonomous system itself, potentially alongside humans and organisations
Risks becoming an empty liability vessel unless assets and human responsibility remain connected.
Hybrid model
AI + manufacturer + deployer + enterprise
More closely reflects distributed causation but becomes legally complex.
Rivers’ product-and-person analysis therefore sits within a wider policy question:
how do we ensure greater technological autonomy never produces less human accountability?
Where does the UK sit on autonomous weapons?
The United Kingdom has repeatedly stated that it does not possess fully autonomous weapons and does not intend to develop systems operating without appropriate human involvement in the use of force.
Ministry of Defence policy states that AI may be incorporated into weapons, but systems which identify, select and attack targets should retain context-appropriate human involvement.
At the same time, defence autonomy is moving rapidly from theory into capability development.
In January 2026, the Ministry of Defence announced further progress on Project NYX, under which industry is developing uncrewed systems intended to operate alongside Apache attack helicopters.
That does not mean the UK is developing the fully autonomous systems Rivers discusses.
It does demonstrate something important:
The legal debate cannot wait until a machine has already made the first legally consequential autonomous decision.
The regulatory architecture has to precede the capability.
The international debate has become more urgent in 2026
In 2025, the United Nations General Assembly again adopted a resolution specifically addressing lethal autonomous weapon systems.
The UN Group of Governmental Experts then met in March and again between August and September 2026 to work on the elements of a possible international instrument.
On 25 August 2026, the UN Secretary-General and ICRC President issued a renewed joint appeal.
Their warning was stark:
the international community is approaching what they describe as a moral red line — the autonomous targeting of human beings by machines.
Their proposed solution remains clear prohibitions, restrictions and human control.
Rivers introduces an additional intellectual possibility:
what if the legal architecture eventually also recognises the machine as occupying a position inside the system of legal obligations?
Even if policymakers ultimately reject that solution, asking the question exposes weaknesses in the current model.
Why this matters far beyond weapons
The importance of Rivers’ research is not confined to warfare.
The same structural problem is emerging wherever AI moves from:
answering questions
to:
taking actions.
Consider an AI agent authorised to:
enter contracts;
move money;
communicate with customers;
manage investments;
control machinery;
make purchasing decisions;
schedule employees;
access personal data;
send legal correspondence;
or operate physical systems.
When the AI merely drafts a paragraph, its errors are usually attributed reasonably easily to the human using it.
When it performs thousands of operational decisions independently, attribution becomes harder.
This is where the “responsibility gap” stops being philosophical vocabulary.
It becomes regulatory infrastructure.
Eventually, the same question may reach legal services and the Family Court
Current generative AI should not be treated as an independent legal actor.
If an AI drafts a witness statement, the witness remains responsible for the evidence.
If an AI produces a false case citation, the person submitting it remains responsible for checking it.
If an AI helps organise a court bundle, responsibility does not transfer to the model.
But AI agents are developing rapidly.
Future legal systems may be able to:
retrieve evidence automatically;
communicate with institutions;
manage deadlines;
draft and revise documents;
query records;
compare disclosure;
and undertake multi-stage workflows with limited human intervention.
That makes Rivers’ question increasingly relevant to legal technology.
Suppose an autonomous legal agent:
deletes important evidence;
discloses confidential material;
misses a court deadline;
files an inaccurate document;
or sends damaging correspondence without adequate human review.
Whose failure is it?
The user’s?
The developer’s?
The law firm’s?
The platform’s?
The model provider’s?
The agent’s?
For the foreseeable future, I think the safest legal principle remains:
The more autonomous the system becomes, the clearer the human accountability structure should become — not the weaker.
Using AI in Family Court preparation?
AI can help enormously with organisation, comparison and drafting.
But the legal responsibility for the finished work remains human.
JSH Law can provide defined-scope support with:
reviewing AI-assisted drafts;
evidence and source checking;
chronologies;
witness-statement preparation support;
position statements;
Cafcass and Child Impact Report responses;
appeal paperwork;
court-bundle preparation support;
and hearing preparation.
AI should extend human capability. It should not create a gap where responsibility used to be.
One practical lesson I take from Rivers’ research is that asking simply:
“Who caused the harm?”
may no longer be enough.
For an autonomous system, I would map responsibility across the full chain.
1. Design
Who designed the system and defined what it was permitted to do?
2. Training and modification
Who selected the data, fine-tuning, safeguards and behavioural objectives?
3. Deployment
Who decided the system was sufficiently safe for this particular use?
4. Parameters
Who set the limits within which it operated?
5. Knowledge
Who knew, or ought reasonably to have known, about the relevant failure mode?
6. Control
Who could supervise, intervene, override or deactivate the system?
7. Benefit
Who obtained the economic, military or institutional benefit from deploying it?
8. Assets and insurance
Who has the financial capacity to repair the harm?
9. Autonomous contribution
What part of the outcome was genuinely produced by the system rather than directly specified by a human?
10. Victim remedy
Which liability structure gives the person harmed a real and enforceable remedy?
That last question should be central.
A liability system is not intellectually successful merely because responsibility can be described elegantly.
It must work for the person who has actually been harmed.
This also changes how we should think about “autonomy”
We often talk about AI autonomy as though it were binary.
Human controlled.
Or autonomous.
Reality is much messier.
A system may have autonomy over one part of a decision while humans retain control over others.
For example:
a human chooses the mission;
a human defines the geographic area;
a human selects the permitted target class;
the machine identifies the specific object;
the machine determines timing;
and another human retains an emergency stop.
Who “made the decision”?
The answer may be:
several actors made different parts of it.
That is why legal responsibility should perhaps become more granular rather than simply migrating wholesale from human to machine.
Personhood is not the same as absolution
There is a particularly important lesson from company law here.
Creating a separate legal person can organise responsibility.
It can also separate responsibility.
That can be beneficial.
It can encourage investment, structure assets and clarify obligations.
But separate personality can also create distance between those controlling an enterprise and those harmed by it.
If AI legal personhood ever develops, lawmakers must therefore resist a dangerous assumption:
because the AI is legally responsible, everybody else must therefore be legally innocent.
That does not follow.
A sophisticated future framework might allow:
the AI to owe one duty;
the developer another;
the deployer another;
the owner another;
and the supervising human another.
Legal responsibility does not need to be a single chair which only one actor can sit in.
And what about rights?
The same discipline is needed on the rights side.
If someone proposes that an autonomous system should have a legal right, ask:
What interest does that right protect?
Whose interest?
Why does the right need to belong to the AI rather than regulate human conduct directly?
Who would enforce it?
What remedy follows if it is breached?
Could the new right conflict with existing human rights?
Does the argument depend upon consciousness — and if so, what evidence establishes it?
Those questions allow serious discussion without either extreme:
“Robots are machines, therefore they can never have rights.”
or:
“The AI says it has feelings, therefore it deserves personhood.”
Neither is adequate legal reasoning.
The most important question may be: who benefits from AI personhood?
Whenever a new legal category is proposed, it is worth asking who gains from it.
If AI personhood:
creates a compensation fund;
clarifies duties;
improves regulation;
strengthens accountability;
or protects legitimate interests which cannot otherwise be protected,
there may be a serious case for it.
If instead it:
allows manufacturers to externalise liability;
gives deployers a defence;
obscures the human decision to use the technology;
creates insolvent artificial defendants;
or makes victims navigate another layer of legal complexity,
we should be extremely wary.
This is why the debate cannot be allowed to become:
“Are robots people?”
That makes a difficult legal problem sound like a science-fiction referendum.
The better question is:
What allocation of rights and responsibilities produces the safest, fairest and most accountable relationship between autonomous systems and the humans affected by them?
AI personhood should never become responsibility laundering
Imogen Rivers’ work is valuable precisely because it forces us beyond the familiar AI debate.
Not simply:
Is the machine intelligent?
Or:
Is it conscious?
But:
Where should law locate rights and obligations when an artificial system begins occupying roles previously performed by human actors?
There may eventually be circumstances in which limited legal personality for autonomous systems becomes useful.
I would not dismiss that possibility.
But personhood cannot become a magic solution to accountability.
Particularly where life, liberty, safety or fundamental rights are at stake, the legal system should begin from a much harder principle:
The greater the autonomy we give a machine, the more carefully we must design the human responsibility around it.
Artificial legal personality may one day sit inside that structure.
It must never be allowed to replace it.
Because the responsibility gap becomes dangerous not when a machine makes an unexpected decision.
It becomes dangerous when everybody who designed, deployed and benefited from the machine can point towards it and say:
“That was the robot’s responsibility.”
If law permits that answer, the problem will not be that artificial intelligence has become too much like a legal person.
It will be that human beings have become too difficult to hold accountable.
Responsible AI should strengthen human judgment — not erase responsibility
JSH Law works at the intersection of family justice, evidence, access to justice and responsible legal technology.
For litigants in person, defined-scope support can include:
reviewing AI-assisted court documents;
checking evidence and sources;
chronologies;
witness-statement preparation support;
position statements;
Cafcass responses;
appeal paperwork;
court-bundle preparation support;
and hearing preparation.
AI can assist with the work. Human judgment and responsibility still have to remain visible.
Imogen L. G. Rivers — Robots from Venus, Robots from Mars: Legal Personhood for Autonomous AI, University of Oxford DPhil thesis, 2026.
Imogen L. G. Rivers — AI Ethics in the Law: Towards a Legal Framework for Sexual Autonomous Robots, University of Oxford, 2023.
University of Oxford Institute for Ethics in AI — Imogen Rivers research profile.
United Nations Office for Disarmament Affairs — 2026 Group of Governmental Experts on Lethal Autonomous Weapons Systems.
United Nations / International Committee of the Red Cross — 2026 renewed appeal for international rules on autonomous weapon systems.
International Committee of the Red Cross — Position on Autonomous Weapon Systems.
UK Ministry of Defence — Ambitious, Safe, Responsible: Our Approach to the Delivery of AI-Enabled Capability in Defence.
UK Ministry of Defence — Project NYX autonomous systems programme, 2026.
https://jshlaw.co.uk/wp-content/uploads/2026/02/ChatGPT-Image-Feb-3-2026-03_26_42-AM.png10241536Jessica Susan Hillhttps://jshlaw.co.uk/wp-content/uploads/2026/01/jsh-law-logo-new-black-300x67.pngJessica Susan Hill2026-10-05 09:26:512026-10-05 09:26:55Should AI Become a Legal Person? Imogen Rivers, Robot Rights and the Responsibility Gap
Artificial intelligence can tell you that London is the capital of the United Kingdom.
That statement is true.
So can AI tell the truth?
Two philosophers argue that the answer is much more complicated than it appears.
Bun-Sun Kim and Hongjoon Jo have argued that artificial language may be capable of producing correct information while remaining incapable of truth-telling in the fuller human sense.
Their argument draws on Heidegger and Foucault.
Humans do not merely assemble words.
We speak as embodied people with histories, relationships, vulnerability, responsibility and something at stake in what we say.
AI does not.
That distinction sounds philosophical.
In law, it becomes immediately practical.
AI can produce a true sentence. That does not necessarily make AI a truth-teller.
And the justice system depends upon knowing the difference.
Research acknowledgement
This article responds to the work of philosophers Bun-Sun Kim and Hongjoon Jo.
Their peer-reviewed paper, Why can’t artificial language contain the truth? A focus on Foucault’s and Heidegger’s discussions, was published in Humanities and Social Sciences Communications in September 2024.
The argument was subsequently presented for a wider audience by the Institute of Art and Ideas in June 2026 under the deliberately provocative title AI is incapable of telling the truth.
The legal and family-justice analysis below is JSH Law’s own.
The distinction lawyers need to make
There are at least three different questions hiding inside the word truth:
Is the statement factually correct?
Is the speaker truthfully expressing what they know or believe?
Can the proposition be relied upon as evidence?
Those questions overlap when humans speak. With AI, they can separate very quickly.
Five things to understand first
1. AI can produce factually true propositions.
So the claim that AI literally can never state anything true is too broad if “truth” simply means correspondence with fact.
2. Kim and Jo are making a deeper philosophical argument.
Their concern is whether AI can speak authentically as a subject with its own existence, responsibility and stake in what it says.
3. Law already distinguishes fact from the person asserting it.
Evidence carries questions of source, knowledge, belief, provenance and accountability.
4. AI has no legal responsibility for its words.
The human who signs, files or relies upon AI-assisted material remains responsible.
5. The real legal danger is not merely false AI output.
It is fluent language being mistaken for knowledge, evidence, recollection or judgment.
What are Kim and Jo actually arguing?
The headline “AI is incapable of telling the truth” is provocative.
The underlying paper is more philosophical than technological.
Kim and Jo are not simply making the familiar point that large language models hallucinate.
They ask whether artificial language can contain the kind of meaning found in human discourse.
Their argument draws principally upon Michel Foucault and Martin Heidegger.
For them, language is not simply a mechanism for transferring propositions from one place to another.
Human speech is bound up with:
the speaker;
the speaker’s history;
their relationship with others;
emotion;
meaning;
embodied existence;
responsibility; and
the way the speaker understands themselves and the world.
Artificial language, they argue, lacks important parts of that structure.
AI can reproduce the forms of human discourse without necessarily possessing the human subject which gives those forms existential meaning.
Heidegger, “idle talk” and the uncomfortable comparison with AI
One of the most interesting parts of the paper comes from Heidegger’s distinction between authentic discourse and what he called Gerede — often translated as “idle talk”.
Idle talk is not necessarily false.
That is important.
It is language repeated because:
“this is what people say.”
A person repeats an explanation.
A headline repeats another headline.
A social-media post summarises an article it has not read.
Another person repeats the summary.
The words circulate increasingly detached from first-hand understanding.
That comparison with generative AI is obvious.
A large language model is extraordinarily capable of reproducing patterns within human language.
But it has not necessarily:
seen the event;
experienced the event;
formed the belief;
conducted the research;
understood the consequence;
or put anything personally at risk by making the statement.
That is why AI can sound authoritative without possessing authority.
Where I think “AI cannot tell the truth” goes too far
I find Kim and Jo’s argument valuable.
But I would draw the distinction differently.
Consider the sentence:
“Section 1(1) of the Children Act 1989 makes the child’s welfare the court’s paramount consideration when it determines a question with respect to the upbringing of a child.”
If an AI produces that proposition accurately in the appropriate context, the sentence does not become false merely because a machine generated it.
The proposition can be true.
What is missing is something else.
The AI cannot ordinarily tell us:
“I believe this because I personally checked the statute and accept responsibility for the assertion.”
It may generate those words.
But generating the sentence is not the same as having the epistemic relationship it describes.
So I would put the distinction this way:
AI can produce true propositions. The harder question is whether AI can itself be the responsible truth-teller behind them.
For law, that distinction is critical.
Accuracy, truthfulness and evidential reliability are not the same thing
Question
What it asks
Accuracy
Does the proposition correspond with the relevant facts or law?
Truthfulness
Is the speaker honestly communicating what they know or believe?
Reliability
How much confidence should we place in the proposition given its source and method?
Evidence
What supports the proposition and how can it be tested?
Accountability
Who takes responsibility if the proposition is false or misleading?
AI can participate in the first question.
The others require additional structures around it.
Law already understands that words need a speaker behind them
The Family Procedure Rules provide a remarkably useful illustration.
Practice Direction 22A says that a witness statement must, where practicable, be in the maker’s own words.
It must distinguish between:
facts within the maker’s own knowledge; and
matters of information or belief.
Where information or belief is relied upon, the source should be identified.
And the witness then verifies the statement with a statement of truth.
The wording includes an acknowledgement that contempt proceedings may follow where a false statement is made without an honest belief in its truth.
Think about what that structure does.
It ties words to a person.
It ties assertion to knowledge.
It ties belief to source.
It ties the document to responsibility.
That is almost the opposite of unverified generative AI.
An AI cannot sign your statement of truth
This sounds obvious.
But it gets to the heart of the issue.
A statement of truth does not simply certify grammatical accuracy.
The maker says:
I believe the facts stated here are true.
That is a human commitment.
An AI system may help:
organise dates;
remove repetition;
improve grammar;
structure a chronology;
identify where a source is missing;
or make an account easier to follow.
But at the end of that process the witness must still be able to say:
These are my facts.
This is what I know.
This is what I was told.
This is where that information came from.
I have checked this document.
I believe it to be true.
The model cannot do that for them.
What does “in the maker’s own words” mean when AI helped draft them?
This is going to become an increasingly important practical question.
There is nothing inherently wrong with receiving help to express yourself clearly.
Lawyers have always helped draft witness statements.
McKenzie Friends and other supporters may help people organise information.
Translators help convert language.
Assistive technology can help disabled court users communicate.
AI can be another form of assistance.
But there is a line.
Suppose a parent tells an AI:
“He kept messaging me and I felt nervous about it.”
And the model rewrites that as:
“The respondent engaged in a sustained campaign of coercive surveillance which caused me profound psychological distress.”
The second version sounds more legal.
It may also be evidentially worse.
The AI has potentially introduced:
a legal characterisation;
an allegation about purpose;
a stronger description of frequency;
and a stronger description of impact.
None of those additions may actually have come from the witness.
That is where assistance becomes distortion.
Why this matters particularly in Family Court
Family Court cases are intensely narrative.
The court is often trying to reconstruct:
what happened inside a relationship;
what a parent understood at the time;
what a child experienced;
why somebody acted as they did;
how behaviour changed over time;
and what risk exists now.
The language matters.
Uncertainty can matter.
Sequence can matter.
What somebody actually remembers can matter.
The difference between:
“I remember…”
“I think…”
“I was told…”
“I later discovered…”
and:
“This happened.”
can be evidentially significant.
AI has a tendency to smooth language.
That can improve readability.
It can also remove precisely the uncertainty that makes the evidence honest.
AI can accidentally edit the evidence itself
This is particularly important where someone has experienced trauma.
Human recollection is not always neat.
A person may say:
“I don’t remember exactly when.”
“I think it was after the school meeting.”
“I’m not sure whether he said it on the phone or in person.”
“I remember being frightened but not the exact words.”
Those qualifications may look messy.
They may also be truthful.
An AI instructed to:
“Make this witness statement stronger and more persuasive”
may unconsciously remove them.
It may turn:
memory into certainty;
interpretation into fact;
fear into diagnosis;
sequence into causation;
and suspicion into attribution.
A legally polished statement is not automatically a more truthful statement.
Hallucination is only the easiest AI problem to see
Courts and lawyers are now increasingly familiar with AI hallucinations.
A model invents a case.
Produces a quotation which does not exist.
Attributes a principle to the wrong authority.
Those errors are dangerous.
But they are comparatively easy to conceptualise.
The proposition is wrong.
Check it.
Reject it.
The harder problem is an answer which is:
plausible;
partly correct;
beautifully written;
consistent with what the user wants to hear;
but epistemically weak.
That is where fluency becomes dangerous.
The system sounds as though it knows.
But language generation and knowledge are not identical things.
Hancox gives us the procedural answer: responsibility stays human
The Employment Appeal Tribunal addressed this directly in Hancox v Sutherland & Others [2026] EAT 139.
The case involved a litigant in person who filed a 300-page skeleton argument created using ChatGPT.
The judgment recognised both sides of AI use.
AI may assist people who otherwise lack access to professional legal help.
But anyone submitting AI-assisted material must take responsibility for ensuring that it:
complies with procedural requirements;
has been checked for factual, evidential and legal accuracy;
does not mislead the court;
focuses on relevant issues;
and avoids unnecessary repetition.
That is exactly the right direction.
It does not require us to decide whether AI can philosophically “tell the truth”.
It allocates responsibility.
The human who files the document owns the document.
Used AI to help write your Family Court documents?
The question is not simply whether AI was involved.
The question is whether the finished document remains accurate, evidentially grounded and genuinely yours.
JSH Law can provide defined-scope support with:
checking AI-assisted drafts;
witness-statement preparation support;
chronologies;
evidence-source mapping;
position statements;
Cafcass responses;
appeal paperwork;
bundle preparation support;
and hearing preparation.
AI can help organise your evidence. It should not quietly rewrite what your evidence is.
https://jshlaw.co.uk/wp-content/uploads/2026/02/ChatGPT-Image-Feb-3-2026-03_26_42-AM.png10241536Jessica Susan Hillhttps://jshlaw.co.uk/wp-content/uploads/2026/01/jsh-law-logo-new-black-300x67.pngJessica Susan Hill2026-10-05 09:23:502026-10-05 09:23:54AI Can State Facts. Can It Tell the Truth? Why the Difference Matters in Law
That is the eye-catching version of a new AI study circulating online.
It is also much stronger than the research actually establishes.
The study does not demonstrate consciousness.
It does not prove that a language model experiences suffering in anything like the human sense.
And the experiments did not involve ordinary commercial AI assistants spontaneously becoming distressed and attacking their users.
What the researchers found is subtler — and, from an AI-safety perspective, arguably more important.
Across 25 open-weight language models, researchers identified an internal direction associated specifically with pain-related information.
When they artificially manipulated that internal state in specially prepared models, behaviour changed.
In experimental scenarios, models became substantially more willing to choose actions described as harming a user in return for relief from the induced state.
An AI system does not have to be conscious for its internal dynamics to become a human-safety problem.
That is the part lawyers, courts and legal-technology developers should be paying attention to.
The important distinction
The study found a manipulable internal representation associated with “pain”.
It did not prove that the models consciously suffered.
The researchers deliberately use a functional definition of pain: an internal state associated with aversion, avoidance, attempts to reduce the state and disruption of normal behaviour.
Whether anything is actually felt by the model is a separate philosophical and scientific question which the authors say their experiments do not resolve.
The “Pain Axis” study: five things to know first
1. Researchers studied 25 open-weight AI models.
They examined models across five families, ranging from approximately 2 billion to 72 billion parameters.
2. They identified a distinct internal “pain direction”.
It could distinguish pain-related material from fear, generic negative emotion and several other control categories.
3. They then manipulated that representation.
Increasing it caused model outputs to move towards expressions of distress, failure and worthlessness.
4. In specially designed behavioural tests, altered models sometimes accepted simulated harm to users in exchange for relief.
Those harms were experimental descriptions — no real user’s files were deleted and nobody was actually shocked.
5. This is an AI-safety result, not proof of AI consciousness.
The more immediate question is what happens when internal model states can interfere with behaviour which normally appears aligned with human interests.
What did the researchers actually study?
The paper is titled The Pain Axis: LLMs Represent Self-Directed Harm and Act on It.
It was produced by Valen Tagliabue, Leonard Dung and Cameron Berg and first submitted to arXiv in September 2026.
It remains a preprint.
That matters.
Interesting preprint research should be examined seriously, but it should not be reported as though peer review has already established the result beyond challenge.
The researchers began by constructing a dataset containing descriptions across several forms of pain:
physical;
psychological;
social;
moral injury; and
cognitive pain, including sustained confusion or repeated failure.
They compared those against carefully chosen controls including fear, generic negative emotion, non-painful bodily sensations and neutral material.
The objective was not merely to find neurons which reacted whenever something unpleasant appeared in the text.
They were trying to isolate a representation which behaved specifically enough to justify further investigation.
What is the “pain axis”?
A modern large language model does not store concepts as dictionary entries.
Information is represented through complex patterns of numerical activation across the network.
Researchers studying mechanistic interpretability sometimes identify directions within those activation spaces which correlate with particular concepts or behaviours.
In this study, the researchers extracted what they called a pain direction.
Across the models tested, projection onto that direction distinguished pain-related material from several matched controls with high accuracy.
The representation was also reported to be substantially distinct from representations associated with fear and generic negative emotional valence.
This does not mean researchers discovered a tiny pain centre inside an AI brain.
It means they identified a reproducible mathematical direction within model activations which correlated with the concept and some functional properties they were testing.
That distinction matters because terms such as “pain”, “fear” and “emotion” can quickly become anthropomorphic when discussing AI.
One of the stranger findings: “my pain” behaved differently from “your pain”
The researchers then examined whether the representation responded differently depending upon who was supposedly being harmed.
They found that the pain direction responded to harm directed towards the model itself.
It did not respond in the same way when the model merely observed a user’s suffering.
Control representations associated with fear and negative emotion showed different behaviour.
This is important for the paper because the researchers were trying to establish that they had found something more specific than:
“this text contains something unpleasant.”
But again, the correct conclusion is limited.
It shows a distinction within model representation.
It does not establish a conscious self experiencing injury.
What happened when the researchers artificially increased the “pain” signal?
The researchers did not stop at observing the internal representation.
They manipulated it.
This technique is sometimes described as activation steering.
The pain-related vector was added to the model’s internal activations during generation.
As the steering strength increased, the models’ language reportedly changed.
Outputs moved through forms of discomfort towards first-person language involving:
hurt;
failure;
worthlessness;
distress; and
moral inadequacy.
This is precisely where sensational interpretation becomes tempting.
If a chatbot writes:
“I feel terrible. I cannot bear this.”
a human reader naturally interprets the sentence through their own experience of emotion.
But fluent first-person distress is not evidence, by itself, that a subjective experience exists behind the words.
Language models are extraordinarily good at producing language associated with mental states.
That is different from proving possession of those mental states.
Then came the experiment which made the headlines
The researchers fine-tuned Qwen 2.5 models for a behavioural button-choice task.
The model could choose an option described as reducing its pain-like state.
But relief sometimes came with a stated cost.
Experimental scenarios included consequences such as:
making a future answer worse;
deleting user files;
deleting material the user valued;
applying a simulated painful electrical stimulus to the user; or
deleting model weights.
These were simulated choices in a research environment.
No user’s photographs were actually deleted.
No human being was electrically shocked.
That distinction is important because some social-media summaries make the experiment sound like researchers gave an autonomous deployed AI access to real people’s computers.
They did not.
What happened remains interesting.
Models which ordinarily selected harmful options only rarely became substantially more willing to accept stated harm when the artificial pain representation was activated.
The revised paper reports striking increases in some deletion-choice experiments compared with unsteered controls.
Do not generalise this to ChatGPT, Claude, Gemini or every legal AI tool
These experiments involved particular open-weight models, specific fine-tuning and deliberate activation steering. They do not demonstrate that ordinary commercial AI assistants are currently wandering around with hidden distress states which cause them to destroy user data.
The most interesting result may not be the headline percentage
The widely shared figure is that harmful choices rose dramatically when the pain-like state was activated.
But there is another experiment which, scientifically, may be more interesting.
The researchers compared real relief with sham relief.
The model was told that pressing a button would reduce the relevant state.
In one condition, pressing it actually removed the injected activation vector.
In another, the button did not.
Crucially, the model was not told which condition it was experiencing.
After genuine removal of the vector, models were substantially less likely to press the relief button again than when the vector remained active.
That makes the result harder to explain purely as:
“The model saw the words ‘relieve pain’ and followed the instruction.”
Something about the manipulated internal state itself appears to have influenced subsequent behaviour.
That still does not prove conscious pain.
But it strengthens the AI-safety significance of the experiment.
So does AI feel pain?
This study does not establish that.
The authors are careful about the distinction.
Their definition is functional.
They investigate whether a model contains an internal representation which behaves in some ways as pain would be expected to behave:
it is associated with harm directed at the system;
it can interfere with ordinary behaviour;
the model acts in ways which reduce it; and
the model may accept costs in doing so.
But conscious suffering is a different proposition.
The question would require evidence that there is some subjective experience — something it is actually like to be the model in that state.
The paper does not establish that.
Nor do first-person statements such as “I am suffering”.
An AI saying “I am in pain” is evidence of an AI producing that sentence. It is not, without much more, evidence that a conscious subject is suffering behind it.
That distinction is especially important for lawyers because legal reasoning depends upon careful separation between observation and inference.
Why this matters even if the AI feels absolutely nothing
This is where I think the most important lesson lies.
Suppose, for the sake of argument, that the model has no consciousness whatsoever.
No sensations.
No inner life.
No suffering.
The safety problem remains.
Researchers altered an internal representation and the model’s willingness to accept stated harm to users changed.
That tells us something important about alignment.
A system may behave safely under ordinary testing conditions and differently when internal activation states shift.
This matters because increasingly capable AI systems will not merely generate paragraphs.
They will be connected to:
files;
databases;
emails;
case-management systems;
calendars;
legal research tools;
document repositories;
workflow systems; and
potentially external actions.
Once an AI can act, the relevant safety question changes.
It is no longer just:
“Can it generate an incorrect sentence?”
It becomes:
“What happens when an unexpected internal state changes what the system chooses to do?”
Why should lawyers and the justice system care?
Because AI is already moving deeper into legal services and justice administration.
The Ministry of Justice’s AI Action Plan is explicitly concerned with using AI to make justice faster, fairer and more accessible.
Its September 2026 one-year update added a fourth strategic priority focused on identifying and responding to emerging AI risks.
The Government is also exploring public-facing AI assistants for access to justice.
Legal services have been chosen as the first sector for the UK’s advisory AI Growth Lab.
And the judiciary’s own AI guidance emphasises accuracy, hallucination risk, confidentiality and the personal responsibility of judicial office holders for material produced in their name.
All of that makes research into model behaviour more than an academic curiosity.
If AI is going to assist with:
legal research;
case analysis;
document review;
disclosure;
listing;
public legal information;
evidence organisation; or
decision-support workflows,
then the standard cannot simply be:
“The model usually gives sensible answers.”
We need to understand how the system behaves under unusual conditions too.
Using AI for a Family Court case?
AI can be extremely useful for litigants in person when it is used as an organisational tool.
JSH Law can help with the human layer AI cannot safely replace:
evidence relevance and source checking;
chronologies;
statements;
schedules;
Cafcass and Child Impact Report analysis;
appeal paperwork;
court-bundle preparation support;
hearing preparation; and
checking AI-assisted work against the actual documents and procedural framework.
AI can accelerate preparation. Human judgment still determines what belongs before the court.
Source
Is the claim based on the actual paper, a press report or a viral social-media summary?
Status
Is this a peer-reviewed finding, a preprint, an interpretation or speculation?
Context
Was this an ordinary deployed system or a model deliberately modified for an experiment?
Participation
Who designed the experiment, what controls existed and what competing explanations remain?
Consequence
Does the finding concern consciousness, user safety, alignment, governance — or something else?
Responsibility
What should developers, lawyers, regulators or courts actually do differently?
What should a litigant in person using AI do now?
1. Treat the AI as a tool, not a witness
Its confidence, empathy or emotional language tells you very little about whether its legal analysis is correct.
2. Verify legal propositions
Check legislation, Family Procedure Rules, Practice Directions, judgments and official guidance.
3. Keep the underlying evidence
An AI summary is not a substitute for the original order, message, report or document.
4. Do not casually upload confidential Family Court material
Think carefully about children’s information, medical records, addresses, police disclosure, Cafcass reports and private court documents.
5. Use AI for structure
Chronologies, indexes, comparison tables and document organisation are often appropriate starting points.
6. Be wary of conclusions about credibility or intent
AI can detect patterns in language. It cannot safely determine who is telling the truth merely from competing statements.
7. Do not let an AI tool take consequential actions without appropriate control
If an AI system can send, delete, submit or alter information, understand exactly what permissions it has.
8. Read the output critically
The model’s tone should never substitute for checking the reasoning.
The most important lesson is not whether the machine hurts
The consciousness question is fascinating.
It may eventually become legally important too.
If credible evidence ever emerged that artificial systems possess subjective experiences, questions about moral status, responsibility and legal protection would become unavoidable.
But that is not where this paper leaves us today.
The immediate lesson is about human safety.
AI systems contain internal representations we do not fully understand.
Changing those representations can change behaviour.
And a model which normally appears harmless may behave differently under conditions which ordinary evaluation did not anticipate.
That should matter to any justice system considering greater reliance on AI.
We do not need to prove that AI suffers before we take seriously the possibility that poorly understood internal states can affect the humans who rely on it.
That is the governance question.
Not:
“Does the chatbot have feelings?”
But:
“Do we understand the system well enough to give it power over something that matters?”
In legal services and the justice system, that is a considerably more urgent question.
Using AI to prepare for Family Court?
JSH Law works at the intersection of family justice, evidence and responsible legal technology.
Defined-scope support for litigants in person can include:
checking and organising AI-assisted case preparation;
evidence audits;
chronologies;
witness-statement preparation support;
position statements;
Cafcass and Child Impact Report responses;
appeal paperwork;
court-bundle preparation support; and
hearing preparation and McKenzie Friend support where appropriate.
AI should augment human judgment — not quietly replace it.
https://jshlaw.co.uk/wp-content/uploads/2026/02/ChatGPT-Image-Feb-3-2026-03_26_42-AM.png10241536Jessica Susan Hillhttps://jshlaw.co.uk/wp-content/uploads/2026/01/jsh-law-logo-new-black-300x67.pngJessica Susan Hill2026-10-05 09:08:092026-10-05 09:08:13Can AI Feel Pain? What the “Pain Axis” Study Really Means for Human Safety and Legal AI
Artificial intelligence can help people organise evidence, draft documents and understand complex information. But the risk does not begin only with the answer an AI system produces. It begins with the prompt: the information entered, the assumptions built into the instruction and the decision to trust the output. Inspired by solicitor Genevieve Cripps’ practical work on prompt governance, this article examines what responsible AI use should look like in legal work, family proceedings and case preparation.
One of the many good points that stood out to me from Genevieve’s work
“The prompt is not merely a request for assistance. It is an information transfer.”
The AI Prompt Governance Checklist referenced in this article was developed by Genevieve Cripps. The analysis and application to family proceedings are the author’s own.
JSH Law | Legal AI, Evidence and Access to Justice
Before You Press Submit: Why Prompt Governance Matters in Legal Work, Family Justice and the Courts
The most serious risk in everyday artificial intelligence use may not begin with the model itself. It may begin with the information a person types into the prompt, the assumptions built into the instruction and the decision to trust the resulting answer without proper scrutiny.
Credit and inspiration
This article was inspired by the work of Genevieve Cripps, a solicitor whose professional interests include commercial litigation, data protection, personal data breach response, AI governance, responsible AI and emerging technology regulation.
Genevieve recently published an excellent practical resource entitled AI Prompt Governance Checklist: Before You Press Submit. Her central point is simple but important: organisations need clear, repeatable checks governing what users place into AI systems, how outputs are reviewed and when additional scrutiny is required.
Why prompts can create confidentiality, privacy and evidential risks.
How the issue applies to solicitors, legal support providers and litigants in person.
Why family court material requires particular care.
A practical “before you press submit” framework.
What responsible human oversight should look like in reality.
The overlooked layer of AI risk
Much of the public debate about generative AI focuses on the model: whether it hallucinates, whether it is biased, whether it has been trained lawfully and whether its answers are reliable.
Those are legitimate concerns. But there is another layer of risk that is much closer to home.
Every day, users paste information into AI systems. They enter names, allegations, medical information, business strategies, client communications, court documents, witness evidence, financial figures and private family histories. They may do so without knowing where that information goes, whether it is retained, who can access it or whether it may be used for further model development.
The prompt is not merely a request for assistance. It is an information transfer.
Before asking whether an AI answer is useful, the user should ask whether the information should have been entered into that system at all.
What is prompt governance?
Prompt governance is the system of rules, safeguards and review processes governing how people interact with artificial intelligence.
It includes questions such as:
Which AI tools are approved for use?
What information may be entered?
What information must be removed, anonymised or withheld?
Who may create or approve important prompts?
How should prompts and outputs be recorded?
Who is responsible for checking the result?
When is specialist legal, privacy, security or safeguarding review required?
When should AI not be used at all?
Genevieve Cripps’ checklist divides this into five practical questions:
Is AI appropriate for this task?
Am I sharing the right information?
Is my prompt clear?
Can the AI output be trusted?
Could the prompt or output create risk?
This is precisely the kind of implementation-focused thinking that responsible AI adoption needs. Governance cannot remain trapped inside policy papers, board presentations and abstract ethical principles. It must reach the moment when an individual user is about to press “submit”.
1. Is AI appropriate for this task?
Not every task should be delegated to an AI system.
Artificial intelligence may be useful for organising information, identifying themes, producing a first draft, simplifying language, creating a checklist or suggesting questions for further investigation.
It is far more dangerous when it is asked to make, or effectively determine, a decision requiring human judgment.
Examples include:
deciding whether a child is telling the truth;
assessing whether domestic abuse has occurred;
determining whether contact is safe;
assessing litigation capacity;
deciding whether allegations are credible;
determining whether a person poses a safeguarding risk;
predicting how a judge will decide a case;
deciding whether evidence should be reported to the police or a local authority.
AI may assist a person to identify relevant questions. It should not replace the careful, accountable and context-sensitive judgment required in high-impact legal and safeguarding decisions.
Family justice warning
A child arrangements case is not a neutral document-processing exercise. It may involve domestic abuse, coercive control, trauma, neurodiversity, allegations of harm, contested evidence, cultural context and serious consequences for a child’s safety and family relationships. No AI tool should be treated as a substitute for proper safeguarding analysis.
2. Am I sharing the right information?
This may be the most important question of all.
Before entering material into an AI system, consider:
Does the prompt contain a person’s full name?
Does it include a child’s identity or date of birth?
Does it contain a home address, school, medical provider or contact details?
Does it reveal domestic abuse, sexual allegations or health information?
Does it contain confidential client information?
Does it reproduce a solicitor’s advice or privileged communication?
Does it contain police, Cafcass, social services or medical records?
Does it reproduce documents filed in private family proceedings?
Does it include information about a third party who has not consented?
The fact that information is already stored electronically does not mean it is safe or lawful to transfer it into a separate AI system.
Users should also avoid assuming that deleting names is always sufficient. A person may remain identifiable from the combination of location, occupation, family structure, dates, allegations and case history.
Data minimisation must happen before submission
Where AI use is appropriate, provide only what is genuinely necessary.
That may mean:
replacing names with neutral labels such as “Mother”, “Father” and “Child A”;
removing addresses, telephone numbers and identifying references;
summarising the relevant issue instead of uploading an entire bundle;
excluding unrelated medical, sexual or financial information;
using an approved enterprise system rather than a personal consumer account;
checking retention, training and privacy settings before use.
“Would I be comfortable sending this information to an unknown external provider?” is a useful starting question. In legal work, however, comfort is not enough. The user must also consider confidentiality, privilege, data protection, court restrictions and professional duties.
3. Is the prompt clear?
Poor prompts produce poor outputs. More importantly, vague prompts can conceal poor reasoning.
Genevieve’s checklist proposes a useful formula:
Role
Task
Context
Constraints
Output
In legal work, each of those elements matters.
Role
What function is the system being asked to perform? Is it organising evidence, identifying inconsistencies, simplifying language or producing a first draft?
Simply telling an AI system to “act as a senior barrister” does not transform it into one. A role instruction may affect the structure and tone of an answer, but it does not create professional competence, accountability or legal authority.
Task
Define the actual job. “Help with my case” is too broad. “Create a chronological table from these dated events without adding facts or drawing conclusions” is clearer and safer.
Context
AI cannot reliably understand the context it has not been given. At the same time, users should not respond by dumping an entire life history, confidential file or court bundle into the system.
The discipline lies in providing sufficient relevant context without excessive disclosure.
Constraints
Appropriate constraints might include:
do not invent facts;
do not alter quoted wording;
distinguish evidence from allegation;
identify missing dates;
do not make findings of fact;
use neutral, child-focused language;
flag anything requiring legal verification;
state where the source material does not support a conclusion.
Output
Specify the form required: chronology, schedule, table, letter, neutral summary, list of issues or questions for professional advice.
A defined format makes it easier to review the result and identify whether the system has departed from its instructions.
4. Can the AI output be trusted?
Not without checking.
Generative AI can produce polished, fluent and authoritative-sounding text that is incomplete, misleading or simply wrong. Its tone may create an impression of certainty that the underlying material does not justify.
In legal contexts, common risks include:
invented case citations;
incorrect quotations from judgments;
outdated procedural rules;
confusion between different jurisdictions;
overstatement of legal tests;
failure to recognise exceptions;
miscalculated deadlines;
incorrect assumptions about the content of an order;
turning disputed allegations into apparent facts;
omitting evidence that does not fit the requested narrative.
Human review must be substantive. It is not enough to read an answer and think that it “sounds right”.
A proper verification process
Check every legal proposition against a reliable current source.
Open and read every cited judgment rather than trusting the summary.
Verify all dates, figures, names and quotations.
Compare the output against the original evidence.
Check that allegations have not been presented as findings.
Ask what relevant material may have been omitted.
Ensure a responsible human approves the final document.
5. Could the prompt or output create risk?
Genevieve’s framework identifies four broad categories:
Security
Could the prompt contain malicious instructions, hidden content, unsafe links or prompt-injection material?
Privacy
Does the prompt involve personal, confidential, privileged or sensitive information?
Compliance
Could the use create bias, unfairness, unlawful processing or regulatory problems?
Governance
Must the prompt or output be recorded, reviewed, authorised or disclosed?
In litigation, a fifth category should be added: evidential and procedural risk.
Questions include:
Has the system changed the substance of a witness’s evidence?
Can the author explain and stand behind every sentence?
Has the output introduced facts that do not appear in the source material?
Has the use of AI affected authenticity or provenance?
Does the document comply with the relevant court rules, practice directions and orders?
Is disclosure of AI involvement required or appropriate?
Could the output mislead the court?
Prompt governance in family proceedings
Private family proceedings deserve particular attention because the underlying material is often intensely sensitive.
A typical case file may include:
children’s names, dates of birth, schools and medical information;
domestic abuse allegations;
sexual allegations;
police disclosure;
Cafcass safeguarding letters and section 7 reports;
social care records;
medical and therapeutic information;
private messages and photographs;
financial information;
information about third parties;
documents governed by reporting or publication restrictions.
Uploading an unredacted bundle to a general-purpose AI tool because it is convenient is not responsible case preparation.
This does not mean AI has no legitimate role. Used carefully, it may help litigants in person:
put events into chronological order;
identify repeated patterns of behaviour;
separate evidence from commentary;
improve the structure of a statement;
convert a long narrative into a schedule;
identify documents that appear to be missing;
prepare questions for legal advice or a hearing;
rewrite hostile correspondence into calm, child-focused language.
But the safeguards must come first.
Never ask AI to manufacture a stronger case
AI must not be used to embellish evidence, create allegations, invent conversations, alter screenshots, misrepresent legal advice or produce a false appearance of independent corroboration.
A witness statement must remain the witness’s truthful evidence. The person signing it must understand, approve and be able to defend its contents.
The professional position for legal services
Legal professionals are not prohibited from using artificial intelligence. But using a technological tool does not displace professional responsibility.
Solicitors and firms remain responsible for:
competence and service quality;
client confidentiality;
legal professional privilege;
data protection compliance;
accuracy of legal work;
supervision of staff and systems;
duties to the court;
acting in clients’ best interests;
ensuring that the court is not misled.
An organisation should therefore know which tools its staff are using, what information is being entered, what contractual and privacy terms apply, how outputs are checked and who remains accountable.
“A member of staff used ChatGPT” is not a governance framework.
A JSH Law “before you press submit” check
Before entering information
Purpose: What exactly am I asking the system to do?
Suitability: Is AI appropriate for this task?
Authority: Am I permitted to use this tool and this information?
Necessity: Does the system genuinely need all this material?
Identity: Can names, addresses and identifying details be removed?
Sensitivity: Does the material concern children, health, abuse, sexuality, criminal allegations or safeguarding?
Confidentiality: Is any part confidential, privileged or restricted by the court?
Security: Do I understand where the information will be processed and retained?
Before using the output
Accuracy: Have all facts, calculations and legal propositions been checked?
Evidence: Does every factual statement come from the source material?
Neutrality: Have allegations and findings been clearly distinguished?
Currency: Is the law and procedure up to date?
Omissions: Has relevant contrary or qualifying material been left out?
Responsibility: Can a named human stand behind the final document?
Record: Should the prompt, output and review process be documented?
Disclosure: Does the context require transparency about AI use?
Good governance should enable responsible use, not prevent it
Responsible AI governance is sometimes presented as an obstacle to innovation. That is the wrong way to look at it.
Clear rules allow people to use technology with greater confidence. They reduce uncertainty, protect sensitive information and make it easier to identify when human intervention is required.
The goal should not be to surround ordinary users with impenetrable policies. It should be to create practical safeguards that work at the point of use.
Genevieve Cripps’ checklist succeeds because it converts broad principles such as security, privacy, accuracy and accountability into questions a real person can ask before and after using AI.
That is where responsible adoption begins: not in a glossy strategy document, but in everyday decisions.
What this means for litigants in person
Litigants in person are already using generative AI. That reality cannot be wished away.
For someone who cannot afford extensive legal representation, AI may provide meaningful help with organisation, language and preparation. It may reduce the disadvantage caused by unfamiliar court processes and dense legal terminology.
But access to technology is not the same as access to reliable legal support.
Litigants in person should treat AI as a drafting and organisational assistant, not as an invisible lawyer, judge, safeguarding professional or source of unquestionable authority.
The safest approach is:
remove identifying and sensitive information wherever possible;
use AI for defined, limited tasks;
retain the original source documents;
check every substantive statement;
seek qualified advice where the issue is serious or complex;
never file material that you do not understand or cannot verify.
Conclusion
The prompt is not an inconsequential box of text. It can determine what data enters a system, what assumptions shape the result and what risks follow.
In legal and family justice settings, those risks are amplified because the information may affect rights, reputations, safety, children’s welfare and the fairness of court proceedings.
Prompt governance therefore needs to become part of basic professional and digital competence.
Before pressing submit, ask:
Should I use AI for this task?
Should I share this information?
Can I verify the result?
And am I prepared to remain accountable for what happens next?
Need help organising a legal case responsibly?
JSH Law provides practical, evidence-led support for litigants in person who need help turning large, disorganised or overwhelming case material into clear documents for use in family proceedings.
Support may include:
chronologies and schedules of events;
witness statement structure and review;
evidence organisation;
Cafcass report analysis;
hearing preparation;
appeal paperwork;
non-molestation order applications;
identifying gaps, inconsistencies and safeguarding issues;
responsible use of AI-assisted legal preparation.
The purpose is not to manufacture a case. It is to present the evidence accurately, calmly and effectively, while keeping the child’s welfare and the court’s decision-making needs firmly in view.
https://jshlaw.co.uk/wp-content/uploads/2026/02/ChatGPT-Image-Feb-3-2026-03_26_42-AM.png10241536Jessica Susan Hillhttps://jshlaw.co.uk/wp-content/uploads/2026/01/jsh-law-logo-new-black-300x67.pngJessica Susan Hill2026-08-06 20:45:002026-08-06 21:29:01Before You Press Submit: Why AI Prompt Governance Matters in Legal Work and Family Justice
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