# Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling arXiv: https://arxiv.org/abs/2304.08366 ยท HTML: https://mlsanity.com/paper/2304.08366/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 33 of its 112 references that are in the map. - First posted on arXiv 17 Apr 2023. ## Builds on (most cited first, 30 of 33) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021) - [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020) - [Investigating Explainability of Generative AI for Code through Scenario-based Design](https://mlsanity.com/paper/2202.04903/) (2022) - [Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems](https://mlsanity.com/paper/2001.06509/) (2020) - [Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens](https://mlsanity.com/paper/2102.00593/) (2021) - [AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization](https://mlsanity.com/paper/2102.01330/) (2021) - [Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements](https://mlsanity.com/paper/1907.09091/) (2019) - [Calliope: Automatic Visual Data Story Generation from a Spreadsheet](https://mlsanity.com/paper/2010.09975/) (2020) - [How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study](https://mlsanity.com/paper/2101.06098/) (2021) - [On the Design of AI-powered Code Assistants for Notebooks](https://mlsanity.com/paper/2301.11178/) (2023) - [AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates](https://mlsanity.com/paper/1912.06723/) (2019) - [ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning](https://mlsanity.com/paper/1902.05009/) (2019) - [Whither AutoML? Understanding the Role of Automation in Machine Learning Workflows](https://mlsanity.com/paper/2101.04834/) (2021) - [Towards Natural Language-Based Visualization Authoring](https://mlsanity.com/paper/2208.10947/) (2022) - [Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop](https://mlsanity.com/paper/2101.04296/) (2021) - [How Much Automation Does a Data Scientist Want?](https://mlsanity.com/paper/2101.03970/) (2021) - [VizLinter: A Linter and Fixer Framework for Data Visualization](https://mlsanity.com/paper/2108.10299/) (2021) - [How do you Converse with an Analytical Chatbot? Revisiting Gricean Maxims for Designing Analytical Conversational Behavior](https://mlsanity.com/paper/2203.08420/) (2022) - [InfoColorizer: Interactive Recommendation of Color Palettes for Infographics](https://mlsanity.com/paper/2102.02041/) (2021) - [Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work](https://mlsanity.com/paper/2203.11085/) (2022) - [Retrieve-Then-Adapt: Example-based Automatic Generation for Proportion-related Infographics](https://mlsanity.com/paper/2008.01177/) (2020) - [Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML](https://mlsanity.com/paper/2104.04375/) (2021) - [Goals, Process, and Challenges of Exploratory Data Analysis: An Interview Study](https://mlsanity.com/paper/1911.00568/) (2019) - [Erato: Cooperative Data Story Editing via Fact Interpolation](https://mlsanity.com/paper/2209.02529/) (2022) - [fAIlureNotes: Supporting Designers in Understanding the Limits of AI Models for Computer Vision Tasks](https://mlsanity.com/paper/2302.11703/) (2023) - [Falx: Synthesis-Powered Visualization Authoring](https://mlsanity.com/paper/2102.01024/) (2021) - [Notable: On-the-fly Assistant for Data Storytelling in Computational Notebooks](https://mlsanity.com/paper/2303.04059/) (2023) - [Interweaving Multimodal Interaction with Flexible Unit Visualizations for Data Exploration](https://mlsanity.com/paper/2004.10428/) (2020) - [Augmented Chironomia for Presenting Data to Remote Audiences](https://mlsanity.com/paper/2208.04451/) (2022) ## Built on by (most cited first, 7 of 7) - [DataTales: Investigating the use of Large Language Models for Authoring Data-Driven Articles](https://mlsanity.com/paper/2308.04076/) (2023) - [From Data to Story: Towards Automatic Animated Data Video Creation with LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2408.03876/) (2024) - [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023) - [What's Next? Exploring Utilization, Challenges, and Future Directions of AI-Generated Image Tools in Graphic Design](https://mlsanity.com/paper/2406.13436/) (2024) - [ReSpark: Leveraging Previous Data Reports as References to Generate New Reports with LLMs](https://mlsanity.com/paper/2502.02329/) (2025) - [FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementation](https://mlsanity.com/paper/2502.17909/) (2025) - [Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective](https://mlsanity.com/paper/2503.02631/) (2025) ## Abstract (arXiv) Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data workers spend tremendous efforts 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 may focus on individual tasks in the workflow of data storytelling and do not reveal a complete picture of humans' preference for collaborating with AI. To better understand real-world needs, we interviewed eighteen data workers from both industry and academia to learn where and how they would like to collaborate with AI. Surprisingly, though the participants showed excitement about collaborating with AI, many of them also expressed reluctance and pointed out nuanced reasons. Based on their responses, we first characterize stages and tasks in the practical data storytelling workflows and the desired roles of AI. Then the preferred collaboration patterns in different tasks are identified. Next, we summarize the interviewees' reasons why and why not they would like to collaborate with AI. Finally, we provide suggestions for human-AI collaborative data storytelling to hopefully shed light on future related research.