As artificial intelligence (AI) is increasingly proposed for new and future capabilities in space missions, the question of how to trust AI-enabled space autonomy has been explored. Recently, a collaboration between The Aerospace Corporation (Aerospace) and NASA's Jet Propulsion Laboratory (JPL) investigated how Aerospace's Trusted AI Framework could be applied to two JPL projects that planned on leveraging AI for critical autonomous tasks. This combined effort led to many insights into the practical implementation of trust-ed AI along with considerable updates to the Trusted AI Framework that tailored its topic threads to space exploration. This document summarizes the enhanced framework as tailored to space missions as well as estimation of the level of trust required as a function of mission criticality and key stakeholders. The goal of this work is to provide a set of best practices to guide autonomy researchers, flight engineers, mission and proposal reviewers, and instrument and mission principal investigators (PIs) towards AI-based autonomy that maximizes trust and lowers the barriers to mission adoption for both science and engineering applications.


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    Title :

    Adapting a Trusted AI Framework to Space Mission Autonomy


    Contributors:


    Publication date :

    2022-03-05


    Size :

    1625505 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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