Date of Defense
29-10-2025 10:00 AM
Location
H2-1005
Document Type
Thesis Defense
Degree Name
Master of Public Law (MPL)
College
COL
Department
Public Law
First Advisor
Dr. Gehad Mohammed Abdulaziz
Keywords
Criminal liability rules, Artificial intelligence, Self-driving vehicles.
Abstract
This study aims to define the concept and levels of artificial intelligence (AI) to understand its legal nature, personality, and impact on criminal liability. It analyzes the current legal frameworks and examines existing legislation related to AI, assessing their ability to address issues concerning criminal responsibility for the actions of these systems. The study begins by defining the precise concept of criminal liability to understand how it can be applied in a technological environment. It then defines AI and explores its legal nature, to analyze current frameworks and evaluating how effectively they deal with questions of criminal accountability for AI’s actions. The study also discusses the adequacy of traditional concepts of criminal liability in addressing crimes that may be committed by AI systems.
The research concluded with several key findings, most notably that the mental element (mens rea) of traditional crimes does not align with the nature of autonomous AI systems capable of self-learning and independent decision-making. It also found that distributing responsibility among different parties (developers, manufacturers, owners, and users) is challenging, particularly when fault is unclear or complex. Accordingly, the study recommends that legislators establish a specific legal classification for AI systems that considers their dual nature—both tangible and non-human software-based—so that they are neither treated as mere “objects” nor granted full legal personality. Instead, a special legal status should be created to reflect their technical characteristics and define the scope of liability arising from their use. Additionally, new national legislation should be enacted to regulate the criminal responsibility of AI systems, particularly self-driving vehicles, including clear definitions of such systems and a classification of possible types of liability.
Included in
THE CRIMINAL LIABILITY PROVISIONS OF ARTIFICIAL INTELLIGENCE (AI) ACTIONS
H2-1005
This study aims to define the concept and levels of artificial intelligence (AI) to understand its legal nature, personality, and impact on criminal liability. It analyzes the current legal frameworks and examines existing legislation related to AI, assessing their ability to address issues concerning criminal responsibility for the actions of these systems. The study begins by defining the precise concept of criminal liability to understand how it can be applied in a technological environment. It then defines AI and explores its legal nature, to analyze current frameworks and evaluating how effectively they deal with questions of criminal accountability for AI’s actions. The study also discusses the adequacy of traditional concepts of criminal liability in addressing crimes that may be committed by AI systems.
The research concluded with several key findings, most notably that the mental element (mens rea) of traditional crimes does not align with the nature of autonomous AI systems capable of self-learning and independent decision-making. It also found that distributing responsibility among different parties (developers, manufacturers, owners, and users) is challenging, particularly when fault is unclear or complex. Accordingly, the study recommends that legislators establish a specific legal classification for AI systems that considers their dual nature—both tangible and non-human software-based—so that they are neither treated as mere “objects” nor granted full legal personality. Instead, a special legal status should be created to reflect their technical characteristics and define the scope of liability arising from their use. Additionally, new national legislation should be enacted to regulate the criminal responsibility of AI systems, particularly self-driving vehicles, including clear definitions of such systems and a classification of possible types of liability.