The Challenges of Integrating AI in Aviation Incident-Accident Investigations: A Human-Centric Approach

Abstract

Integrating Artificial Intelligence (AI) into aviation incident-accident investigations presents unique opportunities and significant challenges. This paper explores the complexities of incorporating AI into the aviation investigation process, emphasizing the importance of a human-centric approach to ensure the technology's reliability, transparency, and accountability. The application of AI in investigations necessitates thorough adherence to existing international frameworks, including ICAO Annex 13 and regulatory guidelines from the Federal Aviation Administration (FAA), the European Union Aviation Safety Agency (EASA), and the National Transportation Safety Board (NTSB). However, AI provides improved data analysis, predictive modeling, and pattern recognition capabilities. Through the examination of crucial case studies, such as the investigation into the Lion Air Flight 610 and Ethiopian Airlines Flight 302 (Boeing 737 MAX) accidents, we illustrate how AI-driven data analytics helped investigators to analyze large quantities of flight data recorder (FDR) and cockpit voice recorder (CVR) information (FAA, 2024). AI-based systems contributed to investigating the Air France Flight 447 accident (Airbus A-330), where advanced data analysis techniques provided insights into pilot responses under adverse conditions (Stewarts, 2017). These case studies highlight AI's strengths and limitations in understanding complex system failures and human-machine interactions.Moreover, these examples underscore the necessity of human oversight in interpreting AI outputs and ensuring accurate, context-driven conclusions. Considering regulatory differences, the research findings address the intricate challenges of harmonizing AI systems with established human-led investigative methodologies. Specifically, the research focuses on how AI can be effectively integrated without compromising the critical decision-making processes traditionally managed by human investigators.Furthermore, the presented research examines how human factors must be prioritized to prevent over-reliance on AI outputs, maintain investigative integrity, and foster cross-disciplinary collaboration between AI experts and aviation safety professionals. By analyzing these case studies and providing a comprehensive review of AI's role in modern aviation safety, the research team aims to illuminate the path toward developing AI frameworks that complement human expertise rather than replace it. Ultimately, this paper calls for a balanced approach that leverages AI's strengths while addressing its limitations, ensuring that future aviation incident-accident investigations remain human-centered and safety-focused.

Keywords: Artificial Intelligence, Machine Learning, EASA, FAA, ICAO, NTSB, Aviation, Incidents, Accidents, Investigations, Complex Systems

DOI: 10.54941/ahfe1005830