Autonomous AI and Ownership Rules

TL;DR

This paper explores legal and economic frameworks for ownership and accountability of AI-generated outputs, addressing issues of traceability, autonomous behavior, and strategic evasion to improve governance and market stability.

Contribution

It introduces a comprehensive analysis of ownership rules for AI outputs, proposing mechanisms like bounty systems and subsidies to mitigate evasion and promote responsible AI integration.

Findings

Accordance with accession doctrine aids ownership attribution when AI is traceable.

First possession rules incentivize reallocation of untraceable AI to productive custodians.

Proposed mechanisms include bounty systems and government subsidies to prevent ownerless AI issues.

Abstract

This Article examines the circumstances in which AI-generated outputs remain linked to their creators and the points at which they lose that connection, whether through accident, deliberate design, or emergent behavior. In cases where AI is traceable to an originator, accession doctrine provides an efficient means of assigning ownership, preserving investment incentives while maintaining accountability. When AI becomes untraceable -- whether through carelessness, deliberate obfuscation, or emergent behavior -- first possession rules can encourage reallocation to new custodians who are incentivized to integrate AI into productive use. The analysis further explores strategic ownership dissolution, where autonomous AI is intentionally designed to evade attribution, creating opportunities for tax arbitrage and regulatory avoidance. To counteract these inefficiencies, bounty systems, private incentives, and government subsidies are proposed as mechanisms to encourage AI capture and prevent ownerless AI from distorting markets.