Generative AI Uses and Risks for Knowledge Workers in a Science Organization

要旨

Generative AI could enhance scientific discovery by supporting knowledge workers in science organizations. However, the real-world applications and perceived concerns of generative AI use in these organizations are uncertain. In this paper, we report on a collaborative study with a US national laboratory with employees spanning Science and Operations about their use of generative AI tools. We surveyed 66 employees, interviewed a subset (N=22), and measured early adoption of an internal generative AI interface called Argo lab-wide. We have four findings: (1) Argo usage data shows small but increasing use by Science and Operations employees; Common current and envisioned use cases for generative AI in this context conceptually fall into either a (2) copilot or (3) workflow agent modality; and (4) Concerns include sensitive data security, academic publishing, and job impacts. Based on our findings, we make recommendations for generative AI use in science and other organizations.

著者
Kelly B.. Wagman
University of Chicago, Chicago, Illinois, United States
Matthew T. Dearing
Argonne National Laboratory, Lemont, Illinois, United States
Marshini Chetty
University of Chicago, Chicago, Illinois, United States
DOI

10.1145/3706598.3713827

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713827

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Working with AI (or not)

Annex Hall F205
5 件の発表
2025-04-30 18:00:00
2025-04-30 19:30:00
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