Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions. This growing presence means that humans are now constantly interacting with machine learning-based systems, training and using models everyday. Several different techniques in computer science literature account for the human interaction with machine learning systems, but their classification is sparse and the goals varied. This survey proposes a taxonomy of Hybrid Decision Making Systems, providing both a conceptual and technical framework for understanding how current computer science literature models interaction between humans and machines.

翻译：日常生活中，我们日益依赖机器学习模型来自动化并支持高风险任务与决策。这种日益增长的普及意味着人类现在不断与基于机器学习的系统进行交互，每天训练并使用这些模型。计算机科学文献中已有多种技术阐述了人类与机器学习系统的交互，但这些技术的分类较为分散，且目标各异。本综述提出了一种混合决策系统的分类体系，为理解当前计算机科学文献如何建模人机交互提供了概念性与技术性框架。