Challenges and new directions for the certification of AI and advanced automation in civil aviation

Abstract

This paper examines the certification challenges and opportunities posed by advanced automation and AI-based systems in Air Traffic Management (ATM). The findings presented are the outcome of research carried out in the first part of the SESAR Joint Undertaking's HUCAN project, which aims to develop a holistic, unified certification framework for highly automated systems, addressing both technical reliability and human factors, to ensure their safe and efficient integration into ATM. Based on key issues from ongoing technical and regulatory discussions, the research evaluates current innovative certification approaches and explores how levels of automation can enable a more refined development and certification process. In light of the results obtained, the paper proposes new directions that could advance a holistic approach to certification, considering the guidance provided by EASA.