Why is AI Not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper explores the potential for human-AI collaboration in the context of data storytelling for data workers. Data storytelling communicates insights and knowledge from data analysis. It plays a vital role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data workers need to spend tremendous effort on various tasks, including outlining and styling the story. Recently, a growing research trend has been exploring how to assist data storytelling with advanced artificial intelligence (AI). However, existing studies focus more on individual tasks in the workflow of data storytelling and do not reveal a complete picture of humans' preference for collaborating with AI. To address this gap, we conducted an interview study with 18 data workers to explore their preferences for AI collaboration in the planning, implementation, and communication stages of their workflow. We propose a framework for expected AI collaborators' roles, categorize people's expectations for the level of automation for different tasks, and delve into the reasons behind them. Our research provides insights and suggestions for the design of future AI-powered data storytelling tools.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it