Artificial agents are increasingly integrated into data analysis workflows, carrying out tasks that were primarily done by humans. Our research explores how the introduction of automation recalibrates the dynamic between humans and automating technology. To explore this question, we conducted a scoping review encompassing twenty years of mixed-initiative visual analytic systems. To describe and contrast the relationship between humans and automation, we developed an integrated taxonomy to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans. Here, we describe our qualitative approach of integrating existing theoretical frameworks with new codes we developed. Our analysis shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems and explores a limited potential of the collaborative interaction landscape between people and automation. Our research provides a scaffold to advance the discussion of human-AI collaboration during visual data analysis. Our integrated taxonomy is available in the form of a web application on https://smonadjemi.github.io/miva.

翻译：人工智能体正日益融入数据分析工作流程，承担着原本主要由人类完成的任务。本研究探讨自动化介入如何重新校准人类与自动化技术之间的动态关系。为探究该问题，我们开展了一项涵盖二十年混合主动式可视化分析系统的范围综述。为描述和对比人类与自动化之间的关系，我们构建了一个综合分类体系，用以界定这些混合主动式可视化分析工具的目标、其支持的自动化程度以及人类所承担的假定角色。本文阐述了我们融合现有理论框架与新开发编码的定性方法。分析表明，可视化研究文献对混合主动式系统的定义缺乏共识，并且对人类与自动化协作互动格局的探索仍显局限。我们的研究为推进视觉数据分析中的人机协作讨论提供了框架支撑。该综合分类体系以网页应用形式发布于https://smonadjemi.github.io/miva。