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.

在自动化系统越来越广泛的应用下，需要建立一个法律和监管框架，以便确定这类系统何时及如何对他人造成伤害。然而对于定义伤害并无一定之规的问题，我们提出了一种基于因果模型和对比因果分析的质性伤害定义方法，用于处理多种伤害示例，并显示其对自动化系统相关情境推理的重要性。

有害因果分析