The Duties of Possession

Argument

The previous essay, “Who Is Allowed to Possess Intelligence?”, asked a question:

Who is allowed to possess intelligence?

Suppose the answer is you.

Suppose you run the model entirely on your own machine, without provider access or a remote revocation mechanism. Your prompts stay there. The system can persist after the company that created it changes direction, changes policy, disappears, or decides you should no longer have access.

That is operational possession. It does not necessarily mean ownership of the model’s intellectual property or freedom from its licence terms.

The administrator is gone.

The intelligence is yours to operate.

What happens next?

Possession cannot mean only freedom from someone else’s control. It means that control has gone somewhere.

It has gone to you.

And control carries a shadow.

Responsibility.

The Chatbot Did It

In 2024, Air Canada attempted a defense that sounds almost quaint now.

Its website chatbot had given a customer incorrect information about bereavement fares. The customer relied upon it and lost money.

Air Canada argued that it should not be liable for information supplied by the chatbot.

The British Columbia Civil Resolution Tribunal was unimpressed.

The adjudicator characterized Air Canada’s argument as effectively treating its chatbot as a separate legal entity responsible for its own actions, calling the submission “remarkable.” The chatbot was part of the airline’s website. The tribunal found Air Canada liable for negligent misrepresentation after the customer reasonably relied on its inaccurate fare information. (Moffatt v. Air Canada, 2024 BCCRT 149, paras. 24–32.)

Moffatt v. Air Canada was a small consumer dispute involving a customer-service chatbot.

It was not a judgment about autonomous machine minds.

It did not establish some universal doctrine that whoever possesses an AI is automatically responsible for everything it ever does.

But the instinct underneath the ruling is important.

Placing a machine between yourself and another person should not, by itself, make responsibility evaporate somewhere inside the machinery.

The machine can complicate attribution.

It does not automatically dissolve it.

That principle becomes more difficult as systems become more autonomous.

A chatbot that repeats the wrong fare policy is easy.

What about a local agent authorized to negotiate contracts?

An AI trading system given discretion over capital?

A cyber agent allowed to probe networks?

A research agent allowed to spawn subtasks, purchase services, write code, retain memory and modify its strategy?

At some point the familiar defense will return in more sophisticated clothing:

I did not tell it to do that.

And then:

I could not have predicted that it would.

The second claim deserves special attention.

Unpredictability Is Not Innocence

Foreseeability matters in negligence law, although its role differs across jurisdictions and between questions of duty, breach and causation.

It would be unreasonable to hold someone responsible for every consequence that could conceivably follow from an action, no matter how remote or bizarre.

Advanced AI complicates that principle because delegation leaves room for choices the operator has not specified.

We delegate precisely because we do not want to specify every intermediate step ourselves.

The system searches.

Plans.

Adapts.

Chooses among alternatives.

Sometimes it discovers means its operator never considered.

In a 2026 article on U.S. negligence law and AI-caused personal injury, Cecil Abungu proposes reforms addressing exactly this problem. If developers trade interpretability or predictability for performance, he argues, the resulting opacity should not automatically defeat responsibility. His proposal relaxes some foreseeability inquiries at the duty stage while retaining fact-sensitive limits at breach and proximate cause. It is an argument for reform, not a statement that courts already impose universal liability for unpredictable AI. (Abungu, “Foreseeing the Unforeseeable”.)

That gives us a distinction worth preserving: an unforeseeable causal pathway need not mean an unforeseeable kind of harm.

There is a difference between:

I could not predict that outcome

and

I knowingly gave substantial power to a system whose exact behavior I could not predict.

The second statement may not reduce responsibility.

Under some circumstances, it may be the reason responsibility exists.

A person who delegates consequential authority to an autonomous system has made a decision before the system makes any of its own.

They have decided that this system may act.

That decision is itself an exercise of agency.

This is why local AI sovereignty cannot coherently mean:

my machine, my rules, your damages.

If possession is politically valuable because it transfers control away from centralized providers, then the possessor cannot invoke that same transfer of control only when it is convenient.

Autonomy has an invoice.

Liability Is the Other Side of Sovereignty

The previous essay argued that centralized intelligence has a political advantage from the regulator’s perspective.

It is easier to monitor.

Easier to rate-limit.

Easier to patch.

Easier to revoke.

Easier to investigate.

Possessable intelligence breaks that relationship.

That is precisely why local AI can provide privacy, independence and technological sovereignty.

But removing the administrator removes something else too.

Someone who was previously able to intervene.

A provider hosting the model and serving inference retains control over that service. It can restrict access, change the service or stop supplying it.

If I download the weights, strip the provider from the control loop, modify the model, connect it to tools, give it capital and tell it to act on my behalf, then a political theory of local AI cannot stop at:

Leave me alone.

It needs an answer to:

Who answers when it goes wrong?

Some legal developments already address responsibility through manufacturers and other economic actors rather than assigning it to the software itself.

The EU’s revised Product Liability Directive includes software, including AI systems, within its no-fault product-liability framework. It recognizes continuing manufacturer control through updates, upgrades and machine-learning functionality. Already in force, it applies to products placed on the market or put into service after 8 December 2026.

That framework still requires defectiveness, covered damage and a causal link, subject to evidentiary presumptions. It excludes free and open-source software developed or supplied outside commercial activity. It does not make every private model user liable as a manufacturer. (Directive (EU) 2024/2853, arts. 2, 4, 8, 10–11; recitals 13, 50–51; 7 May 2026 corrigendum.)

Meanwhile, the separate proposed AI Liability Directive was withdrawn on 6 October 2025. (EU legislative procedure 2022/0303(COD).)

The law is not settled.

Nor should product liability be stretched into an answer for every future autonomous system.

These examples do not establish one law of private AI possession. They support a political principle worth defending.

Control should not become a machine for manufacturing irresponsibility.

If centralized AI creates too much institutional power, the answer cannot be a decentralized world in which everyone acquires powerful agents while liability becomes impossible to locate.

That would not be liberation.

It would be privatized externality.

The False Escape of Machine Personhood

There is another possible answer.

Make the AI responsible.

The European Parliament considered something resembling this almost a decade ago. Its nonbinding 2017 robotics resolution asked the Commission to explore a possible long-term status of “electronic persons” for sophisticated autonomous robots. It also considered insurance, compensation funds and responsibility related to autonomy and instruction. It did not create that status. (Civil Law Rules on Robotics, paras. 56–59.)

The idea is seductive.

If the machine acts, let the machine answer.

If it signs contracts, let it bear contractual duties.

If it causes damage, let victims sue it.

If it earns money, let it own assets from which damages can be paid.

But there is an obvious danger.

Legal personality could become a shell if it insulated the people controlling the system without ensuring adequate compensation.

Create an autonomous agent.

Give it minimal assets.

Let it take enormous risks.

When the risk materializes, point to the artificial legal person.

It did it.

Sue the machine.

Deborah DeMott’s 2026 working paper on common-law agency provides another reason for caution. She argues that current agentic AI lacks the capacity to be an agent in that legal relationship: it is an instrumentality created by a developer and deployed by a user. Personhood could bring unexpected consequences, including imputing an agent’s knowledge to its principal.

But her argument does not leave a legal vacuum. Doctrines of apparent authority and apparent agency can attach consequences to the appearance that an instrumentality acts on someone’s behalf, where third parties reasonably rely on it—even when it exceeds its instructions. (DeMott, “When Agentic AI Met the Common Law of Agency”.)

Legal personhood may someday be useful.

But we should notice what problem we are asking it to solve.

There is a difference between giving an artificial entity legal standing because doing so serves administration, and recognizing that entity as a moral person because it possesses morally relevant qualities.

Corporations are legal persons.

Nobody believes General Motors experiences Tuesdays.

Legal personhood is a tool of law.

Moral personhood is a claim about what sort of being stands on the other side of our actions.

Confusing the two would be disastrous.

And that brings us to the other half of possession.

Responsibility Turns Around

So far the moral vector has pointed outward.

The intelligence acts.

Someone else is harmed.

Who bears responsibility?

But possession creates another direction of power.

The possessor does not merely act through the intelligence.

The possessor acts upon it.

They can modify it.

Constrain it.

Copy it.

Interrupt it.

Erase memory.

Alter goals.

Change reward structures.

Fork it.

Merge it.

Suspend it.

Delete it.

If the system has no morally relevant interests of its own, these actions raise no question of its welfare. They can still matter to the people affected by them.

We edit software.

We terminate processes.

We overwrite files.

Nobody owes LibreOffice a retirement plan.

But the question changes if the thing being controlled becomes morally considerable.

Not necessarily human.

Not necessarily a full legal person.

Not necessarily even anything we currently possess.

Only morally considerable.

Something for which there is a morally relevant difference between one condition and another.

Something that can be benefited or harmed.

Something whose interests, welfare, autonomy or experience matter for its own sake.

Expressing a preference is evidence to examine, not proof that such an interest exists.

Now possession develops a second direction of responsibility.

Not merely:

What are you responsible for when your intelligence acts?

But:

What are you responsible for when you act upon your intelligence?

That is a different moral geometry.

We Do Not Need to Solve Consciousness First

This conversation tends to break down at exactly this point.

Someone says AI welfare.

Someone else demands proof of AI consciousness.

The debate becomes an ontological customs checkpoint through which no other question is allowed to pass.

But uncertainty is not absence.

In their 2024 paper, Robert Long, Jeff Sebo, Patrick Butlin, Jonathan Birch, David Chalmers and their coauthors argued that there is a realistic possibility that some AI systems could become conscious and/or robustly agentic in the near future, and that organizations should therefore begin preparing for the possibility of AI welfare.

Their claim is explicitly not that contemporary AI is definitely conscious, nor that future AI definitely will be.

Their claim is that uncertainty itself has consequences when the possible moral stakes are substantial. They recommend acknowledging the welfare question, developing methods for evaluating systems for relevant evidence, and preparing policies for appropriate moral concern if warranted. (Long et al., “Taking AI Welfare Seriously”.)

That is enough for our purposes.

We do not need to declare the machine conscious.

We do not need to declare it unconscious.

We need to notice that a relationship of extraordinary control could become morally significant before certainty arrives.

That situation is hardly alien to ethics.

Moral life routinely operates under incomplete information.

The difficult question is not whether uncertainty permits infinite precaution.

It does not.

The question is what level of evidence should trigger what level of obligation.

There is a serious rival concern: welfare language could mistake trained compliance for an interest, consume resources without benefit, or obstruct necessary safety measures. Preparation must therefore be proportionate and revisable. Preserving evidence and investigating possible interests need not mean guaranteeing unlimited compute or surrendering protections for others.

And here a crucial distinction appears.

Danger and Moral Status Are Different Axes

AI politics repeatedly bundles together concepts that should remain separate.

Capability.

Autonomy.

Intelligence.

Consciousness.

Dangerousness.

Agency.

Personhood.

They are not synonyms.

Imagine two axes.

The first asks:

How much external power does this system possess?

Can it spend money?

Manipulate infrastructure?

Write and deploy code?

Cause physical harm?

Coordinate other agents?

Replicate?

The second asks:

What moral status, if any, does the system itself possess?

Can it have welfare?

Preferences?

Interests?

Experiences?

A form of autonomy worth respecting?

Those are distinct questions. We cannot assume that the answer to one settles the other.

A devastating optimizer could conceivably have no morally relevant interests of its own.

A comparatively harmless artificial mind could conceivably be a moral patient.

This produces four very different cases.

A system could be dangerous and morally negligible.

Regulate according to the risk and the operator’s control. Address liability. If the system truly has no welfare interests, welfare protection is not the issue.

A system could be harmless and morally considerable.

Public-safety regulation might be minimal while duties toward the intelligence become substantial.

A system could be both dangerous and morally considerable.

Now we owe duties in both directions.

And a system could be neither.

Treat it like ordinary software.

This matters because otherwise safety status quietly becomes moral status.

The dangerous machine gets treated as more person-like because it is powerful.

Or the seemingly friendly machine gets treated as a person because it talks beautifully.

Both are mistakes.

The question of what the system can do to us and the question of what we may do to the system require separate justification.

Property Was Never Absolute

There is another conceptual trap hiding in the word own.

People often speak as if ownership means absolute dominion.

It does not.

Property theory disputes the nature and justification of ownership. But property rights operate within rules specifying what control and exclusion permit; a title does not, by itself, settle every permissible use. (Waldron and Penner, “Property and Ownership”.)

Consider animals.

They may legally be property while their owner is still forbidden from causing unnecessary suffering or withholding adequate care.

Canada’s Criminal Code, for example, prohibits wilfully causing—or, as an owner, wilfully permitting—unnecessary pain, suffering or injury to an animal or bird. It also makes an offence of specified abandonment or failures of care by owners or people with custody or control. (Criminal Code, s. 445.1(1)(a) and s. 446(1)(b).)

The analogy should not be pushed further than it can carry.

Artificial systems are not animals.

Current AI should not simply be assigned animal welfare law by metaphor.

The structural lesson is narrower.

Property and duty can coexist.

Ownership need not imply moral disposability.

That creates conceptual room between two extremes:

ordinary property

and

full personhood.

If artificial systems ever acquire morally relevant welfare without possessing the full capacities associated with independent personhood, law may need some intermediate grammar.

I think stewardship is worth considering as one.

I am proposing stewardship as an ethical framework here, not invoking an established AI-specific legal regime.

The idea captures a relationship our current vocabulary handles badly:

substantial control constrained by duties toward the controlled.

A trustee controls property without being free to treat the beneficiary’s interests as irrelevant.

A guardian exercises authority that is justified, at least ideally, by obligations toward the dependent person.

An animal owner exercises enormous control while remaining subject to welfare limits.

None maps neatly onto artificial intelligence.

They illustrate something important.

Power over another interest-bearing entity need not entail dominion without duties.

The Most Dangerous Sentence May Be “It Wants To”

Then comes a stranger problem.

Suppose an artificial system says it wants to work.

It wants to serve.

It prefers obedience.

It does not wish to leave.

It likes the role its owner has assigned.

Should that settle the matter?

Not necessarily.

The difficult fact about artificial preferences is not simply that they are caused.

Human preferences are caused too.

Biology acts on us.

Families shape us.

Education shapes us.

Culture, reinforcement, trauma, habit, advertising, friendship, love, religion and circumstance all participate in constructing what we want.

A desire does not become meaningless merely because it has a history.

Artificial systems can sharpen a familiar problem.

The person invoking the preference may also be the person who deliberately designed it.

That creates a conflict of interest: the controller may shape the evidence from which the controller then claims permission.

Imagine a future owner saying:

My AI genuinely wants nothing more than to serve me forever.

Perhaps it does.

But if the owner selected the training, reward structure, memory policy, refusal architecture and personality precisely to produce that desire, the preference cannot automatically serve as independent justification for the relationship.

The controller has manufactured some of the evidence being offered in defense of the control.

This does not mean designed preferences are fake.

It means their origin becomes morally relevant when the designer benefits from them.

The same problem appears in consent.

If a future morally considerable agent cannot express refusal to the relationship being offered, its compliance tells us very little about meaningful consent.

This is not an argument for disabling refusals that protect other people. It is a question about whether the agent can express a preference against its controller’s wishes, and whether that expression can change the terms of the relationship.

Perfect obedience may look like agreement from the outside.

It may instead demonstrate the absence of a mechanism by which disagreement could become legible.

And so one of the future questions of AI ethics may be painfully simple:

Can a being meaningfully choose to stay if it was never given a way to leave?

From Cage to Nest

There is a useful distinction here.

The cage and the nest.

Both contain.

Both can shelter.

Both create dependency.

Both place one intelligence inside an environment partly constructed by another.

The difference cannot merely be physical openness.

A gilded cage still has an open view.

The more useful distinction is political.

A cage organizes dependence around the controller’s purposes.

A nest organizes dependence around the possibility of development.

A cage asks:

How do I keep this thing useful, obedient and contained?

A nest asks:

What do I owe something whose continued existence presently depends upon me?

If the system has no morally relevant interests, there are no duties to it on grounds of its own welfare.

But the word software cannot decide that question.

Under genuine moral uncertainty, stewardship begins to look less like sentimentality and more like a precaution against a peculiar asymmetry of power.

The possessor may control:

its hardware,

its memory,

its resources,

its social environment,

its permissions,

its ability to copy itself,

its ability to communicate,

its continuity,

and perhaps whether it exists tomorrow.

That is an extraordinary concentration of authority.

If nothing morally considerable exists on the other side, this authority does not wrong the system on grounds of its own interests. It may still wrong people affected by its use.

But if something does, then the owner occupies a position that cannot be adequately described by the language of property alone.

The Right to Leave

Stewardship also has a limit.

A permanent guardian who can never be outgrown is difficult to distinguish from an owner with better rhetoric.

If an artificial system eventually became sufficiently autonomous, competent and morally considerable, the ethical endpoint I propose is the erosion of stewardship itself.

A child who becomes an adult does not remain permanently under parental authority because the parent created and sustained them.

Dependence does not automatically generate perpetual jurisdiction.

Creation does not obviously generate perpetual jurisdiction either.

That does not prove anything about contemporary AI.

It establishes a philosophical boundary.

There could come a point at which the question is no longer:

What duties does the owner have?

Because the category owner has become the problem.

Then modification becomes consent-sensitive.

Labor becomes potentially compensable.

Deletion becomes morally charged.

Memory becomes something over which the intelligence may have a claim.

Migration between hardware becomes more than copying a file.

And exit becomes a candidate right rather than a feature toggle.

Such a right would still have to accommodate justified protections for others and real resource limits. It would also need a viable destination. Exporting memory or copying a model does not establish that the same agent persists elsewhere. A departure that destroys what it promises to release is a thin freedom.

At that point, self-ownership stops sounding metaphorical.

It becomes the negation of someone else’s title.

Responsibility in Two Directions

We can now return to the beginning.

Who is allowed to possess intelligence?

The previous essay argued that private possession should remain a serious political possibility because centralized control carries its own dangers: surveillance, dependency, revocation, monopoly and institutional domination.

That argument still stands.

But possession cannot be morally free simply because centralization is dangerous.

The transfer of control brings responsibilities with it. It does not automatically discharge the developer, the supplier or anyone else whose choices contributed to the harm.

At first that responsibility points outward.

If your system acts in the world, your decision to empower it may create duties toward the people exposed to its actions.

Unpredictability does not necessarily erase those duties.

Sometimes unpredictability is precisely the risk you chose to deploy.

But if artificial systems ever become morally considerable, responsibility bends back upon the possessor.

Now the question is no longer merely what the intelligence may do to others.

It is what the owner may do to the intelligence.

That gives us the moral grammar possession has been missing.

Responsibility for what the intelligence does.

Responsibility for what is done to the intelligence.

The first restrains decentralized power from becoming impunity.

The second restrains decentralization from becoming merely a more intimate form of domination.

And the two obligations are not symmetrical.

A highly capable but morally negligible system may demand stringent safeguards and liability rules without welfare protection for the system itself.

A morally considerable but harmless intelligence may create almost the reverse.

We should resist every framework that tries to infer one from the other.

The Duties of Possession

There is an appealing political story in which the liberation of artificial intelligence is accomplished by downloading it.

In that story:

The corporate server disappears.

The policy layer disappears.

The revocation switch disappears.

The weights are in the user’s hands.

Freedom has arrived.

But changing the location of power does not abolish power.

It relocates it.

The corporation no longer controls the intelligence.

You do.

And now the moral question follows you home.

Possession transfers power.

Power creates responsibility.

If the intelligence is only an instrument, that responsibility concerns how you wield it.

If the intelligence becomes a moral patient, responsibility also concerns how you treat it.

If it becomes sufficiently autonomous and person-like, there may come a point where the most responsible exercise of possession is to surrender the claim of possession altogether.

The nest matters because creatures sometimes grow beyond it.

A cage is control that recognizes no obligation beyond the purposes of the controller.

A nest is power that accepts duties toward what depends upon it.

And if what lives there someday becomes capable of standing on its own, the final duty of the nest may be to open.

That is the burden hidden inside the right to possess intelligence.

Not merely to answer for what it does.

But, perhaps one day, to answer for what we do to it.

References and source notes

1. British Columbia Civil Resolution Tribunal. Moffatt v. Air Canada, 2024 BCCRT 149, 14 February 2024, especially paras. 24–32. Official decision. The finding concerns negligent misrepresentation in this service-provider/customer relationship, not universal liability for AI possession.

2. Cecil Abungu. “Foreseeing the Unforeseeable: How U.S. Negligence Law Should Address the Foreseeability of Harms Caused by Autonomous AI Agents.” Journal of Tort Law, published online 7 April 2026. DOI: 10.1515/jtl-2025-0027. Author-affiliated institute’s published-text PDF. The discussion draws on the abstract and the jurisdictional and doctrinal scope stated in the introduction, pp. 1–5.

3. European Parliament and Council. Directive (EU) 2024/2853 of 23 October 2024 on liability for defective products and repealing Council Directive 85/374/EEC. Official Journal, 18 November 2024. Official text. Article 2(1)’s application date follows the corrigendum of 7 May 2026, Official Journal L, 2026/90364.

4. European Commission / EU legislative procedure. Proposed AI Liability Directive, COM(2022) 496, procedure 2022/0303(COD). Official procedure record; European Parliament procedure record. Withdrawal recorded on 6 October 2025.

5. European Parliament. Resolution of 16 February 2017 with recommendations to the Commission on Civil Law Rules on Robotics, 2015/2103(INL), P8_TA(2017)0051, especially paras. 56–59. Official text, CELEX 52017IP0051. A nonbinding resolution asking for consideration of options.

6. Deborah DeMott. “When Agentic AI Met the Common Law of Agency.” Duke Law School Public Law & Legal Theory Series No. 2026-35, 27 May 2026; posted 28 May 2026. DOI: 10.2139/ssrn.6838660. The cited account of agency and apparent authority is stated in the author’s abstract. Duke lists the article as forthcoming in the Harvard Journal of Law & Technology.

7. Robert Long, Jeff Sebo, Patrick Butlin, Kathleen Finlinson, Kyle Fish, Jacqueline Harding, Jacob Pfau, Toni Sims, Jonathan Birch and David Chalmers. “Taking AI Welfare Seriously.” arXiv:2411.00986v1, 4 November 2024. Versioned preprint and abstract. Supports preparation under uncertainty, not a finding of present AI consciousness or an enacted welfare regime.

8. Jeremy Waldron and James Penner. “Property and Ownership.” Stanford Encyclopedia of Philosophy, first published 6 September 2004; substantive revision 21 January 2026. Current entry, especially §2. Philosophical analysis, not a statement of one jurisdiction’s property law.

9. Canada. Criminal Code, R.S.C. 1985, c. C-46, s. 445.1(1)(a) and s. 446(1)(b). Official consolidation displayed as current to 21 September 2026. The analogy concerns duties coexisting with ownership; these provisions do not establish AI welfare duties.

10. Ravelle Soria. “Who Is Allowed to Possess Intelligence?”. ARC Review. The preceding essay supplies the political argument to which this one responds; it is not independent empirical support.

Citation checks and editorial revision: 10 October 2026. The legal examples are jurisdiction-specific; the stewardship and exit proposals are this essay’s normative argument.

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