  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.
