[论文解读] Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling

Haotian Li, Yun Wang+2 more|arXiv (Cornell University)|Apr 17, 2023

Scientific Computing and Data Management被引用 9

一句话总结

本研究访谈了18名数据工作者，以绘制他们在数据讲述工作流中希望如何、在哪里以及为何与AI协作的地图，提出四种AI角色和混合代理-自动化模式。

ABSTRACT

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.

研究动机与目标

• 描述真实世界数据讲述工作流中的阶段和任务。
• 识别数据工作者在规划、执行和沟通阶段希望以何种方式与AI协作以及在何处协作。
• 使用代理-自动化框架分析对AI角色的偏好。
• 总结在数据讲述中采用AI的利弊并提供设计指导。

提出的方法

• 对来自学术界和产业界的18名数据工作者进行了半结构化访谈。
• 使用思维导图来揭示每个参与者的数据讲述工作流。
• 应用迭代编码从访谈记录中推导协作模式和AI角色。

Figure 1 : This figure summarizes participants’ opinions about where and how they would like to collaborate with AI. (a) demonstrates AI collaborators’ roles against AI automation and human agency. (b) shows tasks in the existing workflows of our interviewees and (c) illustrates the expected AI coll

实验结果

研究问题

• RQ1 在人类希望与AI协作的数据讲述工作流中，在哪里进行协作？
• RQ2 在不同的讲述任务中，人类希望如何与AI协作？
• RQ3 为什么数据工作者在数据讲述中偏好或抵制与AI协作？
• RQ4 哪些AI角色和协作模式最适合数据讲述工作流？

主要发现

• 数据讲述工作流包含三个阶段（规划、执行、沟通）和八个任务。
• 参与者偏好四种AI角色——创建者、优化者、审阅者和助手——在代理-自动化光谱上进行映射。
• 协作模式集中在规划和执行阶段，AI经常作为收集数据事实的助手，以及作为故事片段与风格的创建者或优化者。
• 人们重视AI的可获取性，以减轻重复性任务的工作量，但也担忧AI对语境的理解和维持人类控制。
• 人们偏好跨阶段的混合模式，追求在AI自动化下实现较高的人类自主性（代理加自动化）。
• 大多数参与者不愿意完全取代人类讲述，倾向于保留观众理解和语境差异的AI支持。

Figure 2 : This figure summarizes (1) the interviewees’ opinions about why and why not AI collaborators are preferred and (2) our suggestions for future AI-powered data storytelling tools.