论文标题
谦虚的机器：注意放错位置不信任的费用不足
Humble Machines: Attending to the Underappreciated Costs of Misplaced Distrust
论文作者
论文摘要
奇怪的是，AI越来越多地超过人类决策者，但是大多数公众不信任AI来决定影响其生活的决定。在本文中，我们探讨了一种新颖的理论，该理论可能解释了一个原因。我们建议公众对AI的不信任是设计系统的道德后果，该系统优先考虑假阳性的成本而不是虚假负面的成本较小的成本。我们表明，我们将这种系统描述为“不信任”的系统更有可能将可信赖的个人进行误导，并对这些人和整体人类信任关系产生级联的后果。最终，我们认为公众对AI的不信任源于对被误导的潜力的充分基础。我们建议恢复对AI的信任将要求系统旨在体现“谦虚信任”的立场，在这种情况下，与假否定的不信任相关的不信任的道德成本在开发和使用过程中会适当加权。
It is curious that AI increasingly outperforms human decision makers, yet much of the public distrusts AI to make decisions affecting their lives. In this paper we explore a novel theory that may explain one reason for this. We propose that public distrust of AI is a moral consequence of designing systems that prioritize reduction of costs of false positives over less tangible costs of false negatives. We show that such systems, which we characterize as 'distrustful', are more likely to miscategorize trustworthy individuals, with cascading consequences to both those individuals and the overall human-AI trust relationship. Ultimately, we argue that public distrust of AI stems from well-founded concern about the potential of being miscategorized. We propose that restoring public trust in AI will require that systems are designed to embody a stance of 'humble trust', whereby the moral costs of the misplaced distrust associated with false negatives is weighted appropriately during development and use.
