Liability regimes in the age of AI: a use-case driven analysis of the burden of proof

التفاصيل البيبلوغرافية
العنوان: Liability regimes in the age of AI: a use-case driven analysis of the burden of proof
المؤلفون: Llorca, David Fernández, Charisi, Vicky, Hamon, Ronan, Sánchez, Ignacio, Gómez, Emilia
المصدر: Journal of Artificial Intelligence Research, Vol. 76 (2023), pp. 613-644
سنة النشر: 2022
المجموعة: Computer Science
مصطلحات موضوعية: Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning
الوصف: New emerging technologies powered by Artificial Intelligence (AI) have the potential to disruptively transform our societies for the better. In particular, data-driven learning approaches (i.e., Machine Learning (ML)) have been a true revolution in the advancement of multiple technologies in various application domains. But at the same time there is growing concern about certain intrinsic characteristics of these methodologies that carry potential risks to both safety and fundamental rights. Although there are mechanisms in the adoption process to minimize these risks (e.g., safety regulations), these do not exclude the possibility of harm occurring, and if this happens, victims should be able to seek compensation. Liability regimes will therefore play a key role in ensuring basic protection for victims using or interacting with these systems. However, the same characteristics that make AI systems inherently risky, such as lack of causality, opacity, unpredictability or their self and continuous learning capabilities, may lead to considerable difficulties when it comes to proving causation. This paper presents three case studies, as well as the methodology to reach them, that illustrate these difficulties. Specifically, we address the cases of cleaning robots, delivery drones and robots in education. The outcome of the proposed analysis suggests the need to revise liability regimes to alleviate the burden of proof on victims in cases involving AI technologies.
Comment: Paper published at the Journal of Artificial Intelligence Research
نوع الوثيقة: Working Paper
DOI: 10.1613/jair.1.14565
URL الوصول: http://arxiv.org/abs/2211.01817
رقم الأكسشن: edsarx.2211.01817
قاعدة البيانات: arXiv