The Opaque Law of Artificial Intelligence
Vincenzo Calderonio
TL;DR
This paper analyzes the opacity of AI systems and the resulting responsibility gap by blending legal notions of causality, intent, and fault with computability theory, notably the halting problem. It argues that AI outputs are ultimately computable executions that remain under human control, and assesses regulatory responses in the EU (AI Act and AI Liability Directive) to assign accountability, including mechanisms for evidentiary disclosure and a presumption of causal link. The work distinguishes foundation models from AGI, highlighting regulatory implications for NLP and multimodal systems, and proposes the legal-fiction device 'finzione di continuità' to close the liability gap in human-machine causation. Together, these insights aim to guide policy design and ensure clearer accountability in AI-enabled harm scenarios.
Abstract
The purpose of this paper is to analyse the opacity of algorithms, contextualized in the open debate on responsibility for artificial intelligence causation; with an experimental approach by which, applying the proposed conversational methodology of the Turing Test, we expect to evaluate the performance of one of the best existing NLP model of generative AI (Chat-GPT) to see how far it can go right now and how the shape of a legal regulation of it could be. The analysis of the problem will be supported by a comment of Italian classical law categories such as causality, intent and fault to understand the problem of the usage of AI, focusing in particular on the human-machine interaction. On the computer science side, for a technical point of view of the logic used to craft these algorithms, in the second chapter will be proposed a practical interrogation of Chat-GPT aimed at finding some critical points of the functioning of AI. The end of the paper will concentrate on some existing legal solutions which can be applied to the problem, plus a brief description of the approach proposed by EU Artificial Intelligence act.