Modern Artificial Intelligence (AI) poses new challenges for tort law. The ability of AI systems to learn and evolve from their initial programming is capable of generating significant regulatory gaps that law is called upon to address. On the one hand, there is no ex ante safety discipline related to the production phase of smart products; on the other hand, the characteristics of modern AI systems have the potential to challenge the tightness of existing national liability rules. The aim of the present research is to investigate the aforementioned protection gaps and the regulatory tools that the institutions of the European Union are preparing to fill them, in a perspective that necessarily has to consider the transition period that the study of the subject is going through, as well as the way in which the different levels of regulation are able to interact with each other.

Intelligenza Artificiale e responsabilità civile. Un'indagine sui criteri di imputazione fra tradizione e innovazione.

De Mari Casareto Dal Verme, Tommaso
2023

Abstract

Modern Artificial Intelligence (AI) poses new challenges for tort law. The ability of AI systems to learn and evolve from their initial programming is capable of generating significant regulatory gaps that law is called upon to address. On the one hand, there is no ex ante safety discipline related to the production phase of smart products; on the other hand, the characteristics of modern AI systems have the potential to challenge the tightness of existing national liability rules. The aim of the present research is to investigate the aforementioned protection gaps and the regulatory tools that the institutions of the European Union are preparing to fill them, in a perspective that necessarily has to consider the transition period that the study of the subject is going through, as well as the way in which the different levels of regulation are able to interact with each other.
8-giu-2023
Italiano
Bellantuono, Giuseppe
Università degli studi di Trento
Trento
377
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14242/175872
Il codice NBN di questa tesi è URN:NBN:IT:UNITN-175872