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

Authors
Nagy, Bruce
Edwards, Loren
Sivapragasam, Gunendran
Subjects
Advisors
Date of Issue
2021-05-19
Date
05/19/21
Publisher
Monterey, California. Naval Postgraduate School
Language
Abstract
Development 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.
Type
Video
Presentation
Description
A video presentation with accompanying slides.
Department
Identifiers
NPS Report Number
SYM-AM-21-206
SYM-AM-21-114
Sponsors
Prepared for the Naval Postgraduate School, Monterey, CA 93943.
Naval Postgraduate School
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Approved for public release; distribution is unlimited.
Rights
This 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.
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