Storytelling is an ancient and precious human ability that has been
rejuvenated in the digital age. Over the last decade, there has been a notable
surge in the recognition and application of data storytelling, both in academia
and industry. Recently, the rapid development of generative AI has brought new
opportunities and challenges to this field, sparking numerous new questions.
These questions may not necessarily be quickly transformed into papers, but we
believe it is necessary to promptly discuss them to help the community better
clarify important issues and research agendas for the future. We thus invite
you to join our workshop (Gen4DS) to discuss questions such as: How can
generative AI facilitate the creation of data stories? How might generative AI
alter the workflow of data storytellers? What are the pitfalls and risks of
incorporating AI in storytelling? We have designed both paper presentations and
interactive activities (including hands-on creation, group discussion pods, and
debates on controversial issues) for the workshop. We hope that participants
will learn about the latest advances and pioneering work in data storytelling,
engage in critical conversations with each other, and have an enjoyable,
unforgettable, and meaningful experience at the event.

数字化时代的人类通过数据讲故事，生成 AI 带来了新的机遇和挑战，该研究旨在探讨生成 AI 在数据讲故事中的作用、工作流程的变化以及整合 AI 的风险和陷阱。

Gen4DS: 生成型人工智能时代的数据叙事研讨会

Gen4DS: Workshop on Data Storytelling in an Era of Generative AI

Data storytelling is powerful for communicating data insights, but it
requires diverse skills and considerable effort from human creators. Recent
research has widely explored the potential for artificial intelligence (AI) to
support and augment humans in data storytelling. However, there lacks a
systematic review to understand data storytelling tools from the perspective of
human-AI collaboration, which hinders researchers from reflecting on the
existing collaborative tool designs that promote humans' and AI's advantages
and mitigate their shortcomings. This paper investigated existing tools with a
framework from two perspectives: the stages in the storytelling workflow where
a tool serves, including analysis, planning, implementation, and communication,
and the roles of humans and AI in each stage, such as creators, assistants,
optimizers, and reviewers. Through our analysis, we recognize the common
collaboration patterns in existing tools, summarize lessons learned from these
patterns, and further illustrate research opportunities for human-AI
collaboration in data storytelling.

数据叙事是一个强大的沟通数据洞察力的工具，但需要人类创建者具备多种技能和相当的努力。最近的研究广泛探讨了人工智能（AI）在支持和增强数据叙事中与人类的合作潜力，然而目前缺乏一个系统性的回顾来理解数据叙事工具从人工智能与人类合作的角度，这阻碍了研究人员对现有合作工具设计的反思，以促进人类和 AI 的优势并缓解它们的不足。本文通过一个框架从两个角度对现有工具进行调查：工具在叙事工作流程中的阶段，包括分析、规划、实施和沟通，以及每个阶段中人类和 AI 的角色，如创建者、助手、优化者和评审者。通过我们的分析，我们识别出现有工具中的常见合作模式，总结了从这些模式中得到的教训，并进一步说明了人工智能与人类在数据叙事中合作的研究机会。

迄今为止我们在哪里？从人工智能合作的角度理解数据叙事工具

Where Are We So Far? Understanding Data Storytelling Tools from the  Perspective of Human-AI Collaboration

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.

研究了 18 位数据工作者对人工智能在数据叙事中协作的需求和偏好，发现他们对与人工智能协作持有兴奋态度，但也表达了一些疑虑和原因。研究还总结了数据叙事工作流中的任务阶段和期望人工智能的角色，并提出了建议，以促进未来相关研究。