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.

1. Towards a Framework for
AI-Assisted Data Storytelling
Angelica Lo Duca
IIT-CNR, Italy
angelica.loduca@iit.cnr.it

2. Data Storytelling
Generative AI
AI-Assisted
Data Storytelling
Ethical Issues

3. Data Storytelling
Generative AI
AI-Assisted
Data Storytelling
Ethical Issues

4. Data Storytelling is building data-driven stories.

5. Data Storytelling is building data-driven stories.

6. Data Storytelling is building data-driven stories.

7. You always tell a
story to an audience
General Public Professionals Executives

8. Data Storytelling
Generative AI
AI-Assisted
Data Storytelling
Ethical Issues

9. Artificial
Intelligence
Generative
Creates new content based on what it has learned from
existing content.
The result of the learning process from existing content is a
statistical model (generative model), which is used to
generate new content.
Text is generated without any
communication intent. It simply
generates a text that is statistically
more probable given the training set.
Stochastic Parrot
Emily M. Bender, et. al.. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜 https://doi.org/10.1145/3442188.3445922

10. C
o
d
e
Speech Im
age
Text
V
i
d
e
o
The GenAI Landscape

11. C
o
d
e
Speech Im
age
Text
V
i
d
e
o
The GenAI Landscape

12. Data Storytelling
Generative AI
AI-Assisted
Data Storytelling
Ethical Issues

13. LET GEN-AI
GENERATE THE
FULL STORY
- Generates
already-seen stories
- Plagiarism
- You do not have
the control over the
story
USE GEN-AI FOR
IDEAS
- Ok, but in theory,
you should already
know which story
to tell with your
data!
USE GEN-AI AS
AN ASSISTANT
- Generates texts to
include in stories
- Generates images
to include in stories

14. LET GEN-AI
GENERATE THE
FULL STORY
- Generates
already-seen stories
- Plagiarism
- You do not have
the control over the
story
USE GEN-AI FOR
IDEAS
- Ok, but in theory,
you should already
know which story
to tell with your
data!
USE GEN-AI AS
AN ASSISTANT
- Generates texts to
include in stories
- Generates images
to include in stories

15. GenAI as an
Assistant for
Data Storytelling
Textual Background
Image Generation
Title
Commentaries
Annotations
Descriptions
Text Generation
Visual Background

16. Here, we focus on data stories wrapped
up in a single data visualization chart

17. GPT-3.5
DALL-E
AI-DaSt
Textual Annotator
Graphic Annotator
Upload
SVG basic
chart
Main Editor
Download
SVG data story

18. Style
Details
Description (max 250 char)
OK CANCEL
Number of Images
Graphic Annotator
Icon
Black and White
Four
Praying Hands

21. Text Type
Audience Type
Description (max 250 char)
OK CANCEL
Number of Produced Outputs
Textual Annotator
Number of Output Characters
Title
Technical audience
3
200
Pay attention to
material and moral
news because they
have a high
percentage of fake
news.

24. Data Storytelling
Generative AI
AI-Assisted
Data Storytelling
Ethical Issues

25. Manipulated
Data Stories
Biased Data
Stories

26. Manipulated
Data Stories
Biased Data
Stories

27. GenAI is good at predicting good text, but it is also
good at predicting bad text.
Malicious data storytellers may build manipulated
data stories looking very realistic to influence the
audience to do something
Manipulation of
users

28. Manipulated
Data Stories
Biased Data
Stories

29. A technical error produced by a model during the
prediction phase
Malicious data storytellers may build discriminatory
data stories, either intentionally or unintentionally
Bias Relies on human beliefs, stereotypes, or forms of
discriminations

30. As data storytellers, we have the
responsibility to always control the
content produced by GenAI

31. We also must build ethical data stories,
in the sense that
we always must respect data

32. Thanks for attention!
Questions???
https://www.linkedin.com/in/angelicaloduca/
angelica.loduca@iit.cnr.it