AI Can State Facts. Can It Tell the Truth? Why the Difference Matters in Law

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

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.

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.

Get help checking your court documents

The real danger is AI becoming an epistemic authority

There is a difference between using AI as a tool and treating AI as the thing which knows.

Consider these two interactions.

Use 1

“Here are six documents. Help me identify their dates and organise them chronologically.”

The human can check the result against the documents.

Use 2

“Based on everything I have told you, who is lying?”

That asks the system to make a judgment for which it does not possess the evidential competence, procedural safeguards or authority of the court.

Similarly:

“Does this message contain coercive control?”

may be useful as a brainstorming question.

It is not a legal finding.

AI can assist in identifying possibilities.

Human reasoning still has to examine:

• source;
• context;
• alternative explanations;
• relevant law;
• contradictory evidence;
• and what has actually been established.

A body would not automatically solve the problem

Kim and Jo place substantial emphasis upon embodiment.

Following Heidegger, they connect authentic human discourse with embodied finitude, anxiety, conscience and awareness of mortality.

There is something valuable in that.

A human speaker exists in the world affected by what they say.

An AI model ordinarily does not.

But I would be cautious about treating embodiment itself as the decisive threshold.

Giving an AI:

• cameras;
• sensors;
• a robotic body;
• memory;
• or the ability to act in the physical world

would give it additional information and agency.

It would not automatically establish:

• consciousness;
• sincerity;
• moral responsibility;
• legal responsibility;
• or an honest belief in the truth of its statements.

The relevant problem for law is therefore not simply:

Does the AI have a body?

It is:

What is the epistemic and legal status of the assertion it has produced?

Humans are not reliable truth-machines either

There is also an important criticism of any argument which contrasts artificial language too neatly with truthful human speech.

Humans:

• lie;
• misremember;
• misunderstand;
• repeat rumours;
• become overconfident;
• interpret facts through bias;
• and sincerely believe things which are wrong.

The justice system knows this.

That is why courts do not simply assume:

“A human said it, therefore it is true.”

Instead we have:

• statements of truth;
• cross-examination;
• disclosure;
• contemporaneous documents;
• corroboration;
• rules of evidence;
• professional duties;
• reasoned judgments;
• and appeal.

Those mechanisms do something an AI output does not do on its own.

They create a framework for testing claims.

That may be the more useful legal response to the philosophical problem.

Apply the JSH Law Six-Question Check to anything AI tells you

The JSH Law Six-Question Check works particularly well here.

That last question may be the defining one for legal AI.

How should a litigant in person use AI when truth matters?

The justice system is already moving towards this model

The judiciary’s current AI guidance warns expressly about hallucination, misleading output, bias and confidentiality.

It also emphasises something more important:

judicial office holders remain personally responsible for material produced in their name.

The Ministry of Justice is simultaneously expanding the use of AI across the justice system.

Its September 2026 update says AI tools must be assessed for:

• accuracy;
• bias;
• fairness;
• security;
• and reliability.

That is sensible.

But those qualities still do not turn AI into a witness.

They turn it into a better tool.

That distinction should remain.

Perhaps law does not need AI to “tell the truth”

This is where I ultimately depart slightly from the headline claim.

I am not sure the justice system needs an AI which experiences truth in the way a human being does.

We may not need the model to possess:

• conscience;
• anxiety;
• a body;
• mortality;
• or an existential relationship with its words.

We need something more practical.

We need AI systems whose outputs can be:

• traced;
• checked;
• challenged;
• replicated where appropriate;
• distinguished from human evidence;
• and placed under meaningful human responsibility.

The model may not need to be a truth-teller.

But humans must never forget that they still are.

AI can help us find, organise and articulate information. It cannot relieve us of the responsibility to decide what we are prepared to say is true.

That may ultimately be the most important boundary between artificial language and legal evidence.

Using AI for Family Court preparation?

JSH Law provides defined-scope support at the point where AI assistance still needs human evidential judgment.

Support can include:

• reviewing AI-assisted drafts;
• evidence and source checking;
• chronologies;
• witness-statement preparation support;
• position statements;
• schedules;
• Cafcass responses;
• appeal paperwork;
• court-bundle preparation support; and
• hearing preparation.

The aim is not to replace your voice with AI. It is to make your real evidence clearer.

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Related JSH Law analysis

• AI Can Write 300 Pages. That Does Not Make It Advocacy.
• AI Evidence in the Family Court: Deepfakes, Screenshots & Digital Evidence
• Technology-Facilitated Abuse & Digital Evidence in the Family Court
• The JSH Law Six-Question Check

Research, legal and official sources

• Bun-Sun Kim & Hongjoon Jo — Why can’t artificial language contain the truth? A focus on Foucault’s and Heidegger’s discussions
• Institute of Art and Ideas — AI is incapable of telling the truth
• Family Procedure Rules — Practice Direction 22A: Written Evidence
• Hancox v Sutherland & Others [2026] EAT 139
• Courts and Tribunals Judiciary — Artificial Intelligence Guidance
• Ministry of Justice — AI Action Plan for Justice: One Year On