Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective

• URL: http://arxiv.org/abs/2503.02631v1
• Date: Tue, 04 Mar 2025 13:56:18 GMT
• Title: Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective
• Authors: Haotian Li, Yun Wang, Huamin Qu,
• Abstract summary: Large-scale generative AI techniques have the potential to enhance data storytelling with their power in visual and narration generation.<n>We compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling.<n>The benefits of these AI techniques and other implications to human-AI collaboration are also revealed.
• Score: 39.96202614397779
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human-AI collaborative tools attract attentions from the data storytelling community to lower the barrier of expertise and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration generation. After two years since these techniques were publicly available, it is important to reflect our progress of applying them and have an outlook for future opportunities. To achieve the goal, we compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling. Through comparison, we identify persistent collaboration patterns, e.g., human-creator + AI-assistant, and emerging ones, e.g., AI-creator + human-reviewer. The benefits of these AI techniques and other implications to human-AI collaboration are also revealed. We further propose future directions to hopefully ignite innovations.

Related papers

• Explainable Human-AI Interaction: A Planning Perspective [32.477369282996385]
  AI systems need to be explainable to the humans in the loop. We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication. While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
  arXiv  Detail & Related papers  (2024-05-19T22:22:21Z)
• Now, Later, and Lasting: Ten Priorities for AI Research, Policy, and Practice [63.20307830884542]
  Next several decades may well be a turning point for humanity, comparable to the industrial revolution. Launched a decade ago, the project is committed to a perpetual series of studies by multidisciplinary experts. We offer ten recommendations for action that collectively address both the short- and long-term potential impacts of AI technologies.
  arXiv  Detail & Related papers  (2024-04-06T22:18:31Z)
• Exploration with Principles for Diverse AI Supervision [88.61687950039662]
  Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. This strong reliance on human oversight poses a significant hurdle to the advancement of AI innovation. We propose a novel paradigm termed Exploratory AI (EAI) aimed at autonomously generating high-quality training data.
  arXiv  Detail & Related papers  (2023-10-13T07:03:39Z)
• AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
  generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs. This paper explores the innovative concept of harnessing these AI-generated images as new data sources. In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
  arXiv  Detail & Related papers  (2023-10-03T06:55:19Z)
• Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration [39.96202614397779]
  Recent research has explored the potential for artificial intelligence to support and augment humans in data storytelling. There lacks a systematic review to understand data storytelling tools from the perspective of human-AI collaboration. 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.
  arXiv  Detail & Related papers  (2023-09-27T15:30:50Z)
• Human in the AI loop via xAI and Active Learning for Visual Inspection [2.261815118231329]
  Industrial revolutions have disrupted manufacturing by introducing automation into production. Advances in robotics and artificial intelligence open new frontiers of human-machine collaboration. The present work first describes Industry 5.0, human-machine collaboration, and state-of-the-art regarding quality inspection.
  arXiv  Detail & Related papers  (2023-07-03T17:23:23Z)
• Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling [59.08591308749448]
  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.
  arXiv  Detail & Related papers  (2023-04-17T15:30:05Z)
• A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT [63.58711128819828]
  ChatGPT and other Generative AI (GAI) techniques belong to the category of Artificial Intelligence Generated Content (AIGC) The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
  arXiv  Detail & Related papers  (2023-03-07T20:36:13Z)
• Human in the Loop for Machine Creativity [0.0]
  We conceptualize existing and future human-in-the-loop (HITL) approaches for creative applications. We examine and speculate on long term implications for models, interfaces, and machine creativity. We envision multimodal HITL processes, where texts, visuals, sounds, and other information are coupled together, with automated analysis of humans and environments.
  arXiv  Detail & Related papers  (2021-10-07T15:42:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.