Towards a Framework for AI-Assisted Data Storytelling

Angelica Lo Duca

2023

Abstract

Data storytelling is building stories supported by data to engage the audience and inspire them to make decisions. Applying data storytelling to data visualization means adding a narrative that better explains the visual and engages the audience. Generative AI can help transform data visuals into data stories. This paper proposes AI-DaSt (AI-based Data Storytelling), a framework that helps build data stories based on generative AI. The framework focuses on visual charts and incorporates two main generative AI models provided by the OpenAI APIs: text generation and image generation. We use GPT-3.5 for the chart title, commentary and notes, and image generation for images to include in the chart. We also describe the potential ethical issues and possible countermeasures related to using Generative AI in data storytelling. Finally, we focus on a practical use case, which shows how to transform a data visualization chart into a data story using the implemented framework.

Paper Citation

in Harvard Style

Lo Duca A. (2023). Towards a Framework for AI-Assisted Data Storytelling. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-672-9, SciTePress, pages 512-519. DOI: 10.5220/0012251800003584

in Bibtex Style

@conference{webist23,
author={Angelica Lo Duca},
title={Towards a Framework for AI-Assisted Data Storytelling},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2023},
pages={512-519},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012251800003584},
isbn={978-989-758-672-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Towards a Framework for AI-Assisted Data Storytelling
SN - 978-989-758-672-9
AU - Lo Duca A.
PY - 2023
SP - 512
EP - 519
DO - 10.5220/0012251800003584
PB - SciTePress