  As autonomous systems rapidly become ubiquitous, there is a growing need for
a legal and regulatory framework to address when and how such a system harms
someone. There have been several attempts within the philosophy literature to
define harm, but none of them has proven capable of dealing with with the many
examples that have been presented, leading some to suggest that the notion of
harm should be abandoned and "replaced by more well-behaved notions". As harm
is generally something that is caused, most of these definitions have involved
causality at some level. Yet surprisingly, none of them makes use of causal
models and the definitions of actual causality that they can express. In this
paper we formally define a qualitative notion of harm that uses causal models
and is based on a well-known definition of actual causality (Halpern, 2016).
The key novelty of our definition is that it is based on contrastive causation
and uses a default utility to which the utility of actual outcomes is compared.
We show that our definition is able to handle the examples from the literature,
and illustrate its importance for reasoning about situations involving
autonomous systems.

Artificial Intelligence