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.

翻译：由人工智能（AI）驱动的新兴技术有望以颠覆性方式推动社会向更美好方向转型。特别地，数据驱动的学习方法（即机器学习（ML））已在多个应用领域的技术进步中引发真正革命。但与此同时，这些方法论中某些固有特征（对安全及基本权利带来的潜在风险）日益引发关注。尽管采用过程中存在最小化风险的机制（如安全法规），但这并不能排除损害发生的可能性，一旦发生损害，受害者应有权寻求赔偿。因此，责任制度将在确保使用或交互这些系统的受害者获得基本保护方面发挥关键作用。然而，导致AI系统具有内在风险的特性（如缺乏因果性、不透明性、不可预测性，以及自主持续学习能力），可能在证明因果关系时带来重大困难。本文通过三个案例研究及其实现方法，阐释了这些困难。具体而言，我们探讨了清洁机器人、送货无人机及教育机器人的场景。分析结果表明，需要修订涉及AI技术的案件中的责任制度，以减轻受害者的举证负担。