论文标题
怀疑AI预测：影响驱动的第二意见建议
Doubting AI Predictions: Influence-Driven Second Opinion Recommendation
论文作者
论文摘要
有效的人类协作需要一种系统设计，该系统设计为人类提供有意义的方法来理解并批判性地评估算法建议。在本文中，我们提出了一种通过建立共同的组织实践来增强人类合作的方法：确定可能提供互补意见的专家。当训练机器学习算法以预测人类生成的评估时，专家的丰富观点经常在单片算法的建议中丢失。拟议的方法旨在通过（1）确定某些专家是否可能不同意算法评估以及（如果是这样），（2）建议专家向专家索取第二意见，来利用（1）确定某些专家是否可能不同意算法。
Effective human-AI collaboration requires a system design that provides humans with meaningful ways to make sense of and critically evaluate algorithmic recommendations. In this paper, we propose a way to augment human-AI collaboration by building on a common organizational practice: identifying experts who are likely to provide complementary opinions. When machine learning algorithms are trained to predict human-generated assessments, experts' rich multitude of perspectives is frequently lost in monolithic algorithmic recommendations. The proposed approach aims to leverage productive disagreement by (1) identifying whether some experts are likely to disagree with an algorithmic assessment and, if so, (2) recommend an expert to request a second opinion from.
