IEEE VIS 2024 Content: 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

Jen Rogers - Tufts University, Boston, United States

Mehdi Chakhchoukh - Université Paris-Saclay, CNRS, INRIA, Orsay, France

Marie Anastacio - Leiden Universiteit, Leiden, Netherlands

Rebecca Faust - Tulane University, New Orleans, United States

Cagatay Turkay - University of Warwick, Coventry, United Kingdom

Lars Kotthoff - University of Wyoming, Laramie, United States

Steffen Koch - University of Stuttgart, Stuttgart, Germany

Andreas Kerren - Linköping University, Norrköping, Sweden

Jürgen Bernard - University of Zurich, Zurich, Switzerland

Room: Bayshore I

2024-10-13T16:45:00ZGMT-0600Change your timezone on the schedule page
2024-10-13T16:45:00Z
Exemplar figure, described by caption below
The tug-of-war between automation and human involvement in data science: As automation technology advances, the balance between human intuition and machine efficiency becomes increasingly critical. Accessibility Description: An illustration of a tug-of-war between a robot on one side and three human figures on the other. The robot, representing automation, pulls one end of a rope while the human figures, symbolizing human involvement, pull from the opposite side. The image conveys the tension between automated processes and human input in data science.
Fast forward
Abstract

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 a balanced approach that harmonizes the efficiency of automation with the irreplaceable insights of human expertise. Ultimately, we address the essential question of not just what we can automate, but what we should automate, seeking strategies that prioritize technological advancement alongside the fundamental need for human oversight.