An Overview of the Risk-based Model of AI Governance

TL;DR

This paper critically examines the risk-based AI governance model, highlighting its limitations and proposing improvements through multi-faceted regulation and deeper scrutiny of risk definitions to promote fairer AI regulation.

Contribution

It offers a critique of the risk-based approach to AI governance, emphasizing the need for clearer risk definitions and diverse regulatory strategies.

Findings

Risk calculations reproduce existing inequalities

Risk is a problematic and normative concept

Multiple regulatory approaches are recommended

Abstract

This paper provides an overview and critique of the risk based model of artificial intelligence (AI) governance that has become a popular approach to AI regulation across multiple jurisdictions. The 'AI Policy Landscape in Europe, North America and Australia' section summarises the existing AI policy efforts across these jurisdictions, with a focus of the EU AI Act and the Australian Department of Industry, Science and Regulation's (DISR) safe and responsible AI consultation. The 'Analysis' section of this paper proposes several criticisms of the risk based approach to AI governance, arguing that the construction and calculation of risks that they use reproduces existing inequalities. Drawing on the work of Julia Black, it argues that risk and harm should be distinguished clearly and that the notion of risk is problematic as its inherent normativity reproduces dominant and harmful narratives about whose interests matter, and risk categorizations should be subject to deep scrutiny. This paper concludes with the suggestion that existing risk governance scholarship can provide valuable insights toward the improvement of the risk based AI governance, and that the use of multiple regulatory implements and responsive risk regulation should be considered in the continuing development of the model.