用于代理系统测试的关键AI系统特征分类

J. Defranco, M. Kassab, P. Laplante
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引用次数: 0

摘要

安全与信任是关键系统中最重要的两个特征。关键系统必须是高度可靠的,因为它不仅完成了任务,而且不会对公众造成伤害。问题是测试一个关键系统,特别是如果它使用人工智能(AI)。挑战在于关键AI系统(CAIS)可能会导致无法在关键错误测试期间建模的不可预测事件和条件。需要代理系统(非关键原型)来测试关键系统。我们提出了一个五维CAIS分类法和一个加权系统,将系统特征映射到测试代理,以确定要构建和测试的等效代理系统。最终,这种CAIS分类法和加权系统是开发一套用于关键错误测试的代理系统的一种方法。
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A Taxonomy of Critical AI System Characteristics for Use in Proxy System Testing
Safety and trust are two of the most important features in a critical system. A critical system is one that must be highly reliable in that it not only completes its mission but causes zero harm to the public. The problem is testing a critical system, especially if it employs artificial intelligence (AI). The challenge is critical AI systems (CAIS) may cause unpredictable events and conditions that cannot be modeled during critical error testing. Proxy systems (non-critical prototype) are needed to test the critical system. We present a five-dimensional CAIS taxonomy and a weighting system to map system characteristics to a testing proxy in order to determine equivalent proxy systems to build and test. Ultimately this CAIS taxonomy and weighting system is a way forward to develop a set of proxy systems to use for critical error testing.
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