Upside Down: Liability, Risk Allocation and Artificial Intelligence

Fézer, Tamás (2024) Upside Down: Liability, Risk Allocation and Artificial Intelligence. PRO PUBLICO BONO - PUBLIC ADMINISTRATION, 12 (1). pp. 85-99. ISSN 2063-9058 (print); 2786-0760 (online)

Preview

Text 04_fezer_85-99_WEB-PPB_2024_1.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (193kB) | Preview

Abstract

The dynamic evolution of artificial intelligence (AI) and machine learning (ML) tools poses challenges to the existing liability concepts. This paper aims to examine some of the fields of tortious liability that are most affected by these developments to analyse whether the existing legal standards in civil liability can still be used, with slight reinterpretation, when approaching liability scenarios related to AI and ML, and whether fine tuning of the existing liability regimes is needed, or novel liability scenarios should be established. To answer this question, the paper begins by examining the nature of the regulation of AI and ML: whether it should be a regulatory regime neutral to technology or whether, instead, a sector specific approach is essential. The study considers the already existing legal authorities of the EU and the U.S. as starting points for the analysis, and briefly examines the interpretations municipal courts apply when deciding in AI and ML related tort cases.

Actions (login required)

Edit Item