Hong Lu, Lamberson P J, Page Scott E
Department of Finance, Loyola University Chicago, Chicago, IL 606002, USA.
Department of Communication, University of California at Los Angeles (UCLA), Los Angeles, CA 90095, USA.
J Social Comput. 2021 Jun;2(2):89-102. doi: 10.23919/jsc.2021.0009.
An increasing proportion of decisions, design choices, and predictions are being made by hybrid groups consisting of humans and artificial intelligence (AI). In this paper, we provide analytic foundations that explain the potential benefits of hybrid groups on predictive tasks, the primary use of AI. Our analysis relies on interpretive and generative signal frameworks as well as a distinction between the big data used by AI and the thick, often narrative data used by humans. We derive several conditions on accuracy and correlation necessary for humans to remain in the loop. We conclude that human adaptability along with the potential for atypical cases that mislead AI will likely mean that humans always add value on predictive tasks.
越来越多的决策、设计选择和预测是由人类和人工智能（AI）组成的混合团队做出的。在本文中，我们提供了分析基础，以解释混合团队在预测任务（AI的主要用途）上的潜在优势。我们的分析依赖于解释性和生成性信号框架，以及AI使用的大数据与人类使用的丰富且通常带有叙述性的数据之间的区别。我们得出了人类要留在循环中的准确性和相关性的几个必要条件。我们得出结论，人类的适应性以及可能误导AI的非典型案例的存在，可能意味着人类在预测任务中总是能增加价值。
