Data Visualization and Analytics
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An integrated taxonomy is developed to delineate the objectives of these mixed-initiative visual analytics tools, how much automation they support, and the assumed roles of humans, and shows that the visualization research literature lacks consensus on the definition of mixed-initiative systems.
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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 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 .
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@article{Monadjemi2026Scoping,
title = {A Scoping Review of Mixed Initiative Visual Analytics in the Automation Renaissance},
author = {Shayan Monadjemi and Yuhan Guo and Kai Xu and Alex Endert and Anamaria Crisan},
journal = {Computer Graphics Forum},
year = {2026},
doi = {10.1111/cgf.70434},
url = {https://doi.org/10.1111/cgf.70434}
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