Are we ready for the first EASA guidance on the use of ML in Aviation?

NASA has been working for the past 12 years on software tools for the assurance of software in Aviation critical systems. For now two years, NASA has focused more on the use of AI-based techniques in Aviation than the traditional software systems used in the past. The primary focus has been on machine learning (ML), and more specifically, on supervised off-line learning ML systems. NSA’s research has been driven by case studies such as a vision-based centerline tracking system (implemented using deep neural networks) and the new generation of collision avoidance systems developed under the FAA guidance, i.e., the family of ACAS-X products. Since EASA has recently released its first usable guidance for Level 1 machine learning applications, it is opportunity to see how the research done at NASA is mapping to this first guidance for ML. In this talk I will use the EASA guidance document as a guide to present the past, present, and future tools and techniques being developed at NASA. The intent is to not only provide an overview of the research effort at NASA but also to see how this effort is addressing the concerns listed in the EASA first usable guidance for ML.