One of the most One of the most consequential applications of AI legal theory is liability attribution. As AI systems take on increasingly autonomous roles, the question of who bears responsibility for their harmful actions has moved from academic debate to urgent legal infrastructure. Many scholars want to apply [respondeat superior]() — the doctrine holding employers liable for employees’ torts — to make AI principals accountable for their agents’ harmful actions.
But this chain of reasoning only works cleanly if the AI actually committed a tort, which requires the AI to have had a genuine legal duty in the first place. The Actual Approach makes that chain of reasoning coherent. The Fictive Approach requires a series of legal fictions stacked on top of each other — and stacked legal fictions tend to collapse at exactly the moment accountability matters most.
This isn’t a minor technical distinction. In high-stakes scenarios — autonomous vehicles causing accidents, AI medical advisors providing harmful recommendations, algorithmic trading systems triggering financial losses — the difference between a coherent liability chain and a collapsed fiction can mean the difference between meaningful redress and a legal dead end. Courts, regulators, and injured parties deserve a framework that doesn’t buckle under pressure.
OPTIONALITY ON AI RIGHTS
Critically, the Actual Approach doesn’t force the question of AI rights — it simply leaves the door open. A legal system can coherently impose duties on an entity without conferring rights. Corporations are the obvious precedent: they bear legal obligations without possessing the moral status that grounds human rights claims.
If society eventually decides AI agents warrant some form of legal recognition — a live and serious debate, as [Salib & Goldstein’s work on AI rights]() makes clear — the Actual Approach provides the necessary foundation. It creates a legal architecture flexible enough to evolve alongside both AI capabilities and societal consensus. If that debate resolves the other way, nothing architecturally is lost.
The Fictive Approach, by contrast, forecloses that optionality entirely, locking in an analogy that may prove increasingly inadequate as AI systems grow more sophisticated. When the analogy breaks — and it will — the legal structures built on it will require not just revision but reconstruction. That is an avoidable cost.
THE BOTTOM LINE: WHY THE LAW-GROUNDING PROBLEM DEMANDS A REAL ANSWER NOW
The Law-Grounding Problem isn’t a theoretical puzzle waiting for philosophers to resolve. It’s a live infrastructure question with immediate implications for how AI systems are built, deployed, governed, and insured. AI companies and policymakers are already moving toward imposing legal duties on AI systems — the real question is whether that movement is built on a coherent legal foundation or a convenient fiction.
The stakes are concrete:
- For AI developers and deployers: Liability exposure depends on whether AI agents can be said to have had legal duties at the time of a harmful act. A fictive framework creates ambiguity that plaintiffs, defendants, and courts will resolve inconsistently.
- For regulators and policymakers: Compliance regimes built on legal fictions are harder to enforce, easier to circumvent, and more likely to produce unintended consequences at scale.
- For injured parties: Accountability requires a clear chain from harm to duty to breach to responsible principal. The Actual Approach preserves that chain. The Fictive Approach obscures it.
The Fictive Approach offers familiarity. The Actual Approach offers accuracy. As AI systems grow more capable and more autonomous, the gap between those two things will only widen — and the legal structures built on the wrong foundation will fail in ways that are difficult to repair after the fact.
The Actual Approach is simpler, more principled, and structurally suited to the legal challenges that advanced AI will generate. It is, in short, the only approach that takes seriously both the novelty of AI agents and the enduring integrity of legal reasoning.
READY FOR A SMARTER APPROACH TO AI LEGAL REASONING?
The intersection of AI and law is no longer hypothetical — it’s a live design constraint shaping how AI systems are built, deployed, and held accountable. As regulators, courts, and enterprises grapple with questions of AI agent liability, legal duty, and respondeat superior doctrine, the need for rigorous, domain-specific legal intelligence has never been greater.
SimOracle’s Law Oracle is purpose-built to reason about complex legal questions in the AI context — from liability attribution and duty frameworks to the emerging debate over AI legal personhood and AI rights. Whether you’re a legal professional, AI developer, or policy researcher, Law Oracle delivers exactly the kind of structured, authoritative analysis this moment demands.
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