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Hazard Contribution Modes of Machine Learning ComponentsAmongst the essential steps to be taken towards developing and deploying safe systems with embedded learning-enabled components (LECs) i.e., software components that use ma- chine learning (ML)—are to analyze and understand the con- tribution of the constituent LECs to safety, and to assure that those contributions have been appropriately managed. This paper addresses both steps by, first, introducing the notion of hazard contribution modes (HCMs) a categorization of the ways in which the ML elements of LECs can contribute to hazardous system states; and, second, describing how argumentation patterns can capture the reasoning that can be used to assure HCM mitigation. Our framework is generic in the sense that the categories of HCMs developed i) can admit different learning schemes, i.e., supervised, unsupervised, and reinforcement learning, and ii) are not dependent on the type of system in which the LECs are embedded, i.e., both cyber and cyber-physical systems. One of the goals of this work is to serve a starting point for systematizing L analysis towards eventually automating it in a tool.
Document ID
20200001851
Document Type
Conference Paper
Authors
Smith, Colin (Oak Ridge National Lab. TN, United States)
Denney, Ewen (KBRwyle Moffett Field, CA, United States)
Pai, Ganeshmadhav J. (KBRwyle Moffett Field, CA, United States)
Date Acquired
March 20, 2020
Publication Date
February 7, 2020
Subject Category
Mathematical and Computer Sciences (General)
Report/Patent Number
ARC-E-DAA-TN76123
Meeting Information
AAAI Conference on Artificial Intelligence(New York, NY)
Funding Number(s)
CONTRACT_GRANT: NNA14AA60C
Distribution Limits
Public
Copyright
Public Use Permitted.

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