论文标题
人类和ML在决策中的分类法，以调查人类ML互补性
A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity
论文作者
论文摘要
混合人类ML系统越来越多地在各种领域做出结果决定。通常会引入这些系统，即期望合并后的人类ML系统将实现互补的性能，即与任何决策代理人隔离相比，组合决策系统将是一种改进。但是，经验结果已经混杂在一起，现有的研究很少阐明预期互补绩效的来源和机制。我们在这项工作中的目标是提供概念工具，以促进研究人员推理并就人为ML互补性进行交流。利用人类心理学，机器学习和人类计算机互动中的先前文献，我们提出了一种分类法，描述了人类和基于ML的决策可能会有所不同的不同方式。通过这样做，我们从概念上绘制了潜在的机制，通过这些机制将人类和ML决策结合起来可能会产生互补的绩效，为研究社区开发一种语言，以推理任何决策领域中混合系统的设计。为了说明如何使用我们的分类法来研究互补性，我们提供了一个数学聚合框架，以检查互补性的有利条件。通过合成模拟，我们演示了如何使用该框架来探索分类法的特定方面，并阐明了结合人类ML判断的最佳机制
Hybrid human-ML systems increasingly make consequential decisions in a wide range of domains. These systems are often introduced with the expectation that the combined human-ML system will achieve complementary performance, that is, the combined decision-making system will be an improvement compared with either decision-making agent in isolation. However, empirical results have been mixed, and existing research rarely articulates the sources and mechanisms by which complementary performance is expected to arise. Our goal in this work is to provide conceptual tools to advance the way researchers reason and communicate about human-ML complementarity. Drawing upon prior literature in human psychology, machine learning, and human-computer interaction, we propose a taxonomy characterizing distinct ways in which human and ML-based decision-making can differ. In doing so, we conceptually map potential mechanisms by which combining human and ML decision-making may yield complementary performance, developing a language for the research community to reason about design of hybrid systems in any decision-making domain. To illustrate how our taxonomy can be used to investigate complementarity, we provide a mathematical aggregation framework to examine enabling conditions for complementarity. Through synthetic simulations, we demonstrate how this framework can be used to explore specific aspects of our taxonomy and shed light on the optimal mechanisms for combining human-ML judgments
