Ampovska, Marija (2023) Navigating liability in the age of AI: burden of proof, standard of proof, and causation challenges. In: International Conference on South East European Law, 23 Nov 2023, Faculty of Law, University in Belgrade. (Unpublished)
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Abstract
This contribution focuses on exploring the complex legal issues surrounding AI-related damages from a practical perspective as it will:
- Analyze how the burden of proof is allocated in cases involving AI-caused damages and develop a discussion on whether traditional legal standards need to be adapted to account for the unique challenges posed by AI technologies.
- Examine the appropriate standard of proof required to establish liability in AI-related cases and consider whether a preponderance of evidence, clear and convincing evidence, or a higher standard should be applied in different scenarios.
- Explore the difficulties in establishing causation in AI-related damages that will develop a discussion of how causation can be attributed to AI systems, especially in cases where multiple parties are involved or where AI operates autonomously.
The method used in this contribution will consist of conducting a comparative analysis of how different jurisdictions in the EU handle liability and proof standards in AI-related cases with the highlighting of any emerging trends or best practices. These questions certainly open a discussion of the potential future developments in AI liability law, considering the rapid advancements in AI technology and how might legal standards need to adapt as AI systems become more sophisticated. By focusing on these practical aspects of AI liability, this research can offer valuable insights into how the legal system can effectively address the challenges posed by AI technology while ensuring fair and just outcomes for all parties involved.
Item Type: | Conference or Workshop Item (Lecture) |
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Subjects: | Social Sciences > Law |
Divisions: | Faculty of Law |
Depositing User: | Marija Radevska |
Date Deposited: | 28 Nov 2023 10:59 |
Last Modified: | 28 Nov 2023 10:59 |
URI: | https://eprints.ugd.edu.mk/id/eprint/32645 |
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