Defining and Identifying the Legal Culpability of Side Effects Using Causal Graphs

EasyChair Preprint 7303

Abstract

Deployed algorithms can cause certain negative side effects on the world in pursuit of their objective. It is important to define precisely what an algorithmic side-effect is in a way which is compatible with the wider folk concept to avoid future misunderstandings and to aid analysis in the event of harm being caused. This article argues that current treatments of side-effects in AI research are often not sufficiently precise. By considering the medical idea of side effect, this article will argue that the concept of algorithm side effect can only exist once the intent or purpose of the algorithm is known and the relevant causal mechanisms are understood and mapped. It presents a method to apply widely accepted legal concepts (The Model Penal Code or MPC) along with causal reasoning to identify side effects and then determine their associated culpability.

Keyphrases: Artificial Intelligence, Culpability, Intent, Structural Causal Model, causal DAG, safety, side effect, structural causal influence model

@booklet{EasyChair:7303,
  author    = {Hal Ashton},
  title     = {Defining and Identifying the Legal Culpability of Side Effects Using Causal Graphs},
  howpublished = {EasyChair Preprint 7303},
  year      = {EasyChair, 2022}}