Yonathan A. Arbel (University of Alabama – School of Law), Simon Goldstein, (The University of Hong Kong – University of Hong Kong), & Peter Salib (University of Houston Law Center) have posted How to Count AIs: Individuation and Liability for AI Agents on SSRN. Here is the abstract:
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. AIs lack bodies. They can copy, split, merge, and swarm at will. Even today, a “single” AI agent is often an ensemble of instances based on multiple models. The complexity will only multiply as AI capabilities improve.
This Article is the first to comprehensively diagnose the legal problem of identifying AIs. For AI agents to be effectively governed, the Article argues, two kinds of identity are required: “thin” and “thick.” Thin identification is the project of tying every action taken by an AI to some human principal. Thin identity will be essential for law to hold accountable the humans who make and use AI agents. Thick identification is the project of distinguishing between AI agents, qua agents. It requires sorting millions of AI entities into discrete, persistent units with stable, coherent goals. Thick identity is essential for governing AIs’ behavior directly in the many contexts where principal–agent problems prevent humans from perfectly controlling AIs.
The Article is also the first to present a solution to the twin identity problem. We call it the “Algorithmic Corporation” or “A-corp,” a legal-fictional entity that can hold property, make contracts, and litigate in its own name. An A-corp is owned by humans. But it is designed to be run by AIs. The A-corp solves the thin identity problem by tying AI actions to a human owner. And it solves the thick identity problem via emergent self-organization. A-corps will own the resources which AIs need to accomplish their goals. AIs that control A-corps will thus have strong incentives to share control only with other AIs that share their goals. In equilibrium, both incentive and selection mechanisms will force A-corps to self-organize into persistent, legally legible entities with coherent underlying goals. These coherent, agentic entities will respond rationally to legal incentives, like liability.
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