Certifiable Artificial Intelligence Through Data Fusion

• URL: http://arxiv.org/abs/2111.02001v1
• Date: Wed, 3 Nov 2021 03:34:19 GMT
• Title: Certifiable Artificial Intelligence Through Data Fusion
• Authors: Erik Blasch, Junchi Bin, Zheng Liu
• Abstract summary: This paper reviews and proposes concerns in adopting, fielding, and maintaining artificial intelligence (AI) systems. A notional use case is presented with image data fusion to support AI object recognition certifiability considering precision versus distance.
• Score: 7.103626867766158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper reviews and proposes concerns in adopting, fielding, and maintaining artificial intelligence (AI) systems. While the AI community has made rapid progress, there are challenges in certifying AI systems. Using procedures from design and operational test and evaluation, there are opportunities towards determining performance bounds to manage expectations of intended use. A notional use case is presented with image data fusion to support AI object recognition certifiability considering precision versus distance.

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