Publication: Visualization and Automation in Data Science: Exploring the Paradox of Humans-in-the-Loop
Visualization and Automation in Data Science: Exploring the Paradox of Humans-in-the-Loop
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Rogers, J., Anastacio, M., Bernard, J., Chakhchoukh, M., Faust, R., Kerren, A., Koch, S., Kotthoff, L., Turkay, C., & Wall, E. (2024). Visualization and Automation in Data Science: Exploring the Paradox of Humans-in-the-Loop. Proceedings of the IEEE Visualization in Data Science, 1–5. https://doi.org/10.1109/VDS63897.2024.00005
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This position paper explores the interplay between automation and human involvement in data science. It synthesizes perspectives from Automated Data Science (AutoDS) and Interactive Data Visualization (VIS), which traditionally represent opposing ends of the human-machine spectrum. While AutoDS aims to enhance efficiency by reducing human tasks, VIS emphasizes the importance of nuanced understanding, innovation, and context provided by human involvement. This paper examines these dichotomies through an online survey and advocates for
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Rogers, J., Anastacio, M., Bernard, J., Chakhchoukh, M., Faust, R., Kerren, A., Koch, S., Kotthoff, L., Turkay, C., & Wall, E. (2024). Visualization and Automation in Data Science: Exploring the Paradox of Humans-in-the-Loop. Proceedings of the IEEE Visualization in Data Science, 1–5. https://doi.org/10.1109/VDS63897.2024.00005
