Recent technological advances, especially in the field of machine learning, provide astonishing progress on the road towards artificial general intelligence. However, tasks in current real-world business applications cannot yet be solved by machines alone. We, therefore, identify the need for developing socio-technological ensembles of humans and machines. Such systems possess the ability to accomplish complex goals by combining human and artificial intelligence to collectively achieve superior results and continuously improve by learning from each other. Thus, the need for structured design knowledge for those systems arises. Following a taxonomy development method, this article provides three main contributions: First, we present a structured overview of interdisciplinary research on the role of humans in the machine learning pipeline. Second, we envision hybrid intelligence systems and conceptualize the relevant dimensions for system design for the first time. Finally, we offer useful guidance for system developers during the implementation of such applications.
翻译：最近的技术进步,特别是机器学习领域的技术进步,在人造一般情报的道路上取得了惊人的进展。然而,目前现实世界商业应用中的任务尚不能单靠机器来解决。因此,我们确定需要发展人类和机器的社会技术群落。这些系统有能力通过将人文和人工智能结合起来,共同取得优异的成果,并通过相互学习不断改进,实现复杂的目标。因此,需要这些系统的结构设计知识。在采用分类学发展方法之后,这篇文章提供了三个主要贡献:首先,我们概述了关于人类在机器学习管道中的作用的跨学科研究。第二,我们设想了混合情报系统,并首次构想了系统设计的相关层面。最后,我们为系统开发者在实施这些应用过程中提供了有益的指导。
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