人工智能法规中不确定性的沟通

Aditya Sai Phutane
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引用次数: 0

摘要

人工智能(AI)监管不确定性的学术研究主要集中在减轻不确定性的理论、策略和实践上。然而,对于联邦机构如何将科学的不确定性传达给包括公众和受监管行业在内的所有利益相关者,人们知之甚少。这很重要,原因有三:第一,它突出了问题的哪些方面是可量化的;第二,它展示了机构如何解释不容易量化的问题的不确定性;第三,它显示了知识渊博的机构如何看待公众受众与手头问题的关系,以及他们对这种沟通的期望。通过分析四类科学不确定性的人工智能法规,本研究发现所有权、安全性和透明度领域的不确定性很难量化,因此各机构使用个性化的例子来解释不确定性。此外,各机构还寻求公众的意见,以收集更多的数据,并就涉及道德的问题达成共识。这些发现与解决不确定性和监管决策的文献一致。它们有助于提高我们对当前有效传播科学以解释风险和不确定性的实践的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Communication of Uncertainty in AI Regulations
Scholarship of uncertainty in artificial intelligence (AI) regulation has focused on theories, strategies, and practices to mitigate uncertainty. However, there is little understanding of how federal agencies communicate scientific uncertainties to all stakeholders including the public and regulated industries. This is important for three reasons: one, it highlights what aspects of the issue are quantifiable; two, it displays how agencies explain uncertainties about the issues that are not easily quantified; and three, it shows how knowledgeable agencies perceive the public audience in relation to the issue at hand and what they expect from such communication. By analyzing AI regulations across four categories of scientific uncertainties, this study found that uncertainty in areas of ownership, safety, and transparency are hard to quantify and hence agencies use personalized examples to explain uncertainties. In addition, agencies seek public input to gather additional data and derive consensus on issues that have moral implications. These findings are consistent with the literature on tackling uncertainty and regulatory decision-making. They can help advance our understanding of current practices of communicating science effectively to explain risks and uncertainties.
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