Functional Hazard Analysis and Subsystem Hazard Analysis of Artificial Intelligence/Machine Learning Functions Within a Sandbox Program

dc.contributor.authorNagy, Bruce
dc.contributor.authorEdwards, Loren
dc.contributor.authorSivapragasam, Gunendran
dc.contributor.corporateAcquisition Research Program (ARP)
dc.contributor.otherAcquisition Research Program
dc.date05/19/21
dc.date.accessioned2021-11-02T05:14:19Z
dc.date.available2021-11-02T05:14:19Z
dc.date.issued2021-05-19
dc.descriptionA video presentation with accompanying slides.en_US
dc.description.abstractDevelopment of advanced Artificial Intelligence (AI)/Machine Learning (ML) system-enabled weapons and combat systems for deployment in the U.S. Navy has become a reality. This is also true for the other armed forces, as well as in homeland security and even the Coast Guard. From the Navy standpoint, the Naval Ordnance Safety and Security Activity (NOSSA) is attempting to get ahead of the acquisition cycle by focusing on the development of policies, guidelines, tools, and techniques to assess mishap risk in Safety Significant Functions (SSF) that are identified. NOSSA's efforts have the potential of influencing the acquisition community, including in requirements, development, and test and evaluation engineering. This paper makes recommendations for the Functional Hazard Analysis (FHA) and Subsystem Hazard Analysis (SSHA) analysis templates and focuses on ways to decrease autonomy within system operations and increase its correlated Software Control Category (SCC). The questions and discussions devised from this research aim to form guidance and offer best practices to address AI/ML system safety issues.en_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.en_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.en_US
dc.description.sponsorshipPrepared for the Naval Postgraduate School, Monterey, CA 93943.en_US
dc.description.sponsorshipNaval Postgraduate Schoolen_US
dc.identifier.npsreportSYM-AM-21-206
dc.identifier.npsreportSYM-AM-21-114
dc.identifier.other58
dc.identifier.urihttps://hdl.handle.net/10945/68223
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.relation.ispartofseriesVideos
dc.relation.ispartofseriesAcquisition Research Symposium
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.titleFunctional Hazard Analysis and Subsystem Hazard Analysis of Artificial Intelligence/Machine Learning Functions Within a Sandbox Programen_US
dc.typeVideoen_US
dc.typePresentationen_US
dspace.entity.typePublication
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