A Risk-based Approach to AI Regulation: System Categorisation and Explainable AI Practices

Abstract

The regulation of artificial intelligence (AI) presents a challenging new legal frontier that is only just beginning to be addressed around the world. This article provides an examination of why regulation of AI is difficult, with a particular focus on understanding the reasoning behind automated decisions. We go on to propose a flexible, risk-based categorisation for AIbased on system inputs and outputs, and incorporate explainable AI (XAI) into our novel categorisation to provide the beginnings of a functional and scalable AI regulatory framework.