Toward Airworthiness Certification for Artificial Intelligence (AI) in Aerospace Systems

Boeing “produced an airplane that had an unsafe condition” and admitted full responsibility for the second of its 737 MAX plane crash in Ethiopia in October 2018, killing 189 people. A major cause was found to be new flight control software, based on the AI (Artificial Intelligence) system called Maneuvering Characteristics Augmentation System (MCAS) which needed to be modified. It appears that the MCAS was originally designed as a limited AI system aimed at nose correction based on wind resistance and force sensors. However, in 2012, Boeing expanded that the MCAS and these changes, coupled with inadequate pilot training made it more vulnerable to malfunctioning. These unfortunate developments have further underscored the urgency to address airworthiness given the rise in autonomous systems and their integration into the aerospace domain. While there is an increased interest in effectively managing/regulating the AI certification problem for isolated complex functions, there is little work done on the certification of AI for aircraft that jointly evaluates System of Systems (SoS) integration in tandem with the human machine team dynamics involved in the engineering and deployment of AI-based systems. As a result, the development of airworthiness certification criteria will lag the development of state-of-the-art intelligent systems. This paper presents various considerations to enable the development of certification criteria for AI when consulting aerospace system design guidance and standards, including MIL-HDBK-516C, DO-178, and FAA’s Airworthiness Certification Regulations. To allow for the validation and verification of AI-based systems to be safe, a systems engineering model is proposed to facilitate certification that includes an AI certification profile, verifiable implementation measures and a continuous process flow to ensure airworthiness that cannot be verified through traditional, test-based means. Furthermore, safety assurance approaches are identified for safety-critical systems that may include learning-enabled functions, with examples for both a Safety Critical Function Thread Analysis (SCFTA) and Failure Modes and Effects Testing (FMET). This paper represents a critical step towards the tailoring of airworthiness standards to include AI.