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Comparative analysis of Criminal Liability of autonomous driving and strong artificial intelligence
Nanos, Andreas ; Gřivna, Tomáš (advisor) ; Bohuslav, Lukáš (referee) ; Šcerba, Filip (referee)
XXV Abstract: The rapid development of artificial intelligence (AI) has raised important legal and ethical questions regarding the potential criminal liability of AI systems. This comparative analysis explores the distinctions in criminal liability between weak and strong artificial intelligence, considering their varying levels of autonomy and decision-making capabilities. The study begins by defining weak and strong AI, with weak AI referring to systems that are narrowly focused and exhibit limited autonomy, while strong AI denotes systems capable of general intelligence and independent decision-making. It then delves into the legal frameworks governing criminal liability and encompassing traditional legal principles and legislation. Drawing on relevant case law, the analysis examines the challenges of attributing criminal responsibility to weak AI. Due to their limited autonomy and reliance on human input, weak AI systems are typically treated as tools rather than independent agents. Consequently, liability is more likely to be assigned to the human actors responsible for designing, operating, or utilizing the AI system, rather than the AI system itself. In contrast, strong AI presents unique legal and ethical complexities. With their potential to exhibit cognitive abilities akin to human intelligence,...

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2 Ščerba, Filip
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