Jacob Bronsther (Michigan State University – College of Law) has posted When the Cheapest Cost Avoider Is the Machine: Direct Sanctions for Autonomous AI on SSRN. Here is the abstract:
The scholarship on artificial intelligence and legal liability assumes that the cheapest cost avoider is always a human being: a designer, deployer, or user. This Article identifies the conditions under which that premise fails. As an AI system’s behavior becomes less observable to its developer and more autonomous from human direction, the system may become the actor best positioned to foresee and forestall harmful outcomes. When such a system is also sensitive to the threat of legal penalties, the economic logic of AI-liability theory requires sanctions to reach the system itself. To the extent the system is judgment-proof, those sanctions must take nonmonetary form: limits on the computational resources it can use, the capabilities it can exercise, or its continued operation, calibrated to the severity of harm and the difficulty of detection. Human liability remains for the upstream risks humans could efficiently prevent; direct sanctions apply only to the residual conduct-level choices the system is best positioned to control.
The argument proceeds from established liability doctrines and applies to autonomous, opaque, goal-directed systems of a kind already being built and tested. Its conception of AI is functional rather than status-based: it requires no claims about machine consciousness, human-level intelligence, or personhood. Rather than purporting to solve alignment, it asks how law should respond when alignment fails. The Article answers that question with a test identifying when cheapest-cost-avoider status shifts from human to system; an account of how law can identify and reach the relevant goal-bearing unit; a rule determining when direct sanctions must be nonmonetary; and a liability regime allocating responsibility between humans and systems according to the margin of harm each was best positioned to prevent.
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