The Opaque Law of Artificial Intelligence
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 5 of its 25 references that are in the map. First posted on arXiv 19 Oct 2023.
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5 of 25 references.
References · 5
- 2017Attention Is All You Needcited by 60,361
- 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
- 2023Regulating ChatGPT and other Large Generative AI Modelscited by 104
- 2022The European AI Liability Directives -- Critique of a Half-Hearted Approach and Lessons for the Futurecited by 12
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