Space Applications of a Trusted AI Framework: Experiences and Lessons Learned

Artificial intelligence (AI), which encompasses machine learning (ML), has become a critical technology due to its well-established success in a wide array of applications. However, the proper application of AI remains a central topic of discussion in many safety-critical fields. This has limited its success in autonomous systems due to the difficulty of ensuring AI algorithms will perform as desired and that users will understand and trust how they operate. In response, there is growing demand for trustability in AI to address both the expectations and concerns regarding its use. The Aerospace Corporation (Aerospace) developed a Framework for Trusted AI (henceforth referred to as the framework) to encourage best practices for the implementation, assessment, and control of AI-based applications. It is generally applicable, being based on terms and definitions that cut across AI domains, and thus is a starting point for practitioners to tailor to their particular application. To help demonstrate how the framework can be tailored into mission assurance guidance for the space domain, Aerospace sought the involvement of the Jet Propulsion Laboratory (JPL) to engage with actual examples of AI-based space autonomy. We report here on the framework's application to two JPL projects. The first, Machine learning-based Analytics for Automated Rover Systems (MAARS), is a suite of algorithms that is intended to run onboard a rover to enhance its safety and productivity. The second, the Ocean Worlds Life Surveyor (OWLS), is comprised of an instrument suite and onboard software that is designed to search for life on an icy moon using microscopy and mass spectrometry while judiciously summarizing and prioritizing science data for downlink. Both MAARS and OWLS are intended to have minimal manual control while relying on complex autonomy software to operate within the unforgiving environment of deep space. Therefore, trusted AI for these systems is required for successful adoption of the autonomy software. To capture the needs for trust, interviews with a variety of JPL personnel responsible for developing autonomy solutions were conducted and are summarized here. Additionally, the application of the framework is presented as a means to lower the barrier for AI deployment. The intent of this document is to encourage researchers, engineers, and program managers to adopt new strategies when considering whether to leverage AI in autonomous systems.