A Causal Analysis of Harm

TL;DR

This paper introduces a formal, causality-based definition of harm using causal models and contrastive causation, addressing challenges in legal and philosophical understanding of harm in autonomous systems.

Contribution

It proposes a novel qualitative harm definition grounded in causal models and actual causality, improving handling of complex harm scenarios involving autonomous systems.

Findings

The new definition handles diverse harm examples effectively.

It demonstrates the importance of causal models in harm analysis.

The approach clarifies causality in autonomous system failures.

Abstract

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.