Pith. sign in

REVIEW 1 cited by

Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2304.08366 v2 pith:5QWY6IJH submitted 2023-04-17 cs.HC cs.AI

classification cs.HCcs.AI
keywords datastorytellingtasksworkerscollaborationcollaboratecollaboratinghowever
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original 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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Reflecting on Design Paradigms of Animated Data Video Tools

    cs.HC 2025-02 conditional novelty 6.0 of 10

    This CHI paper proposes a two-dimensional framework for classifying 46 data video authoring tools and summarizes design paradigms across components and human-AI roles.

Pith tools