Unifying Principles and Metrics for Safe and Assistive AI
Unifying Principles and Metrics for Safe and Assistive AI
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DOI：
10.1609/aaai.v35i17.17769
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Siddharth Srivastava
中科院分区：
文献类型：
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作者：
Siddharth Srivastava
由于担心人工智能系统的可控性以及人工智能对未来工作的影响，人工智能应用的普及和成功受到了影响。这些担忧反映了一个核心问题的两个方面：人类将如何与人工智能系统合作？对人工智能安全的研究侧重于设计人工智能系统，使人类能够安全地指导和控制人工智能系统，而对人工智能和未来工作的研究侧重于人工智能对可能无法做到这一点的人类的影响。这篇Blue Sky Ideas论文提出了一套统一的声明性原则，可以在多个维度上对任意人工智能系统进行更统一的评估，以确定它们适合特定类别的人类操作员使用的程度。它利用最近的人工智能研究和该领域的独特优势，为解决上述问题的人工智能系统开发以人为中心的原则。
The prevalence and success of AI applications have been tempered by concerns about the controllability of AI systems about AI's impact on the future of work. These concerns reflect two aspects of a central question: how would humans work with AI systems? While research on AI safety focuses on designing AI systems that allow humans to safely instruct and control AI systems, research on AI and the future of work focuses on the impact of AI on humans who may be unable to do so. This Blue Sky Ideas paper proposes a unifying set of declarative principles that enable a more uniform evaluation of arbitrary AI systems along multiple dimensions of the extent to which they are suitable for use by specific classes of human operators. It leverages recent AI research and the unique strengths of the field to develop human-centric principles for AI systems that address the concerns noted above.