A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance
🤖 AI Summary
This paper addresses the ambiguous human–AI collaboration relationships and insufficient exploration of interaction paradigms in hybrid active visual analytics systems. Adopting a qualitative approach integrating bibliometric analysis and thematic coding, it synthesizes two decades of literature to construct a novel three-dimensional classification framework—spanning collaborative objectives, automation levels, and human roles—and proposes the first consensus-based operational definition of hybrid active visual analytics. The study identifies critical limitations: conceptual inconsistency, narrow interaction patterns, and imbalanced research distribution across domains and time periods. It further provides the first systematic characterization of evolutionary trajectories of real-world practices across eras and application scenarios. The findings establish a theoretical foundation, design guidelines, and a developmental roadmap for advancing human–AI collaborative visual analytics, thereby filling a foundational gap in modeling collaboration paradigms within hybrid active systems.
Application Category
📝 Abstract
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 re-calibrates 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.
Problem
Research questions and friction points this paper is trying to address.
Investigating how automation recalibrates human-technology dynamics in data analysis
Developing taxonomy to describe mixed-initiative visual analytics systems and human roles
Addressing lack of consensus on mixed-initiative system definitions in visualization research
Innovation
Methods, ideas, or system contributions that make the work stand out.
Conducted scoping review of mixed-initiative systems
Developed integrated taxonomy for human-automation relationship
Integrated existing frameworks with new qualitative codes
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