As artificial intelligence (AI) is increasingly pro- posed 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 Labora- tory (JPL) investigated how Aerospace’s Trusted AI Frame- work could be applied to two JPL projects that planned on lev- eraging AI for critical autonomous tasks. This combined effort led to many insights in the practical implementation of trusted AI along with considerable updates to the Trusted AI Frame- work that tailored its topic threads to space exploration. This document cohesively 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 inform autonomy researchers, flight engineers, mission and proposal reviewers, and instrument and mission principal investigators (PI’s) to drive AI-based autonomy that maximizes trust and lowers the barriers to mission adoption for both science and engineering applications.


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    Titel :

    Adapting a Trusted AI Framework to Space Mission Autonomy


    Beteiligte:
    Amini, Rashied (Autor:in) / Ono, Hiro (Autor:in) / Fesq, Lorraine (Autor:in) / Feather, Martin (Autor:in) / Goel, Ashish (Autor:in) / Doran, Gary (Autor:in) / Mandrake, Lukas (Autor:in) / Linstead, Erik (Autor:in) / Bycroft, Benjamen (Autor:in) / Kaufman, James (Autor:in)

    Erscheinungsdatum :

    05.03.2022


    Medientyp :

    Preprint


    Format :

    Keine Angabe


    Sprache :

    Englisch



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