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

• URL: http://arxiv.org/abs/2501.16577v1
• Date: Mon, 27 Jan 2025 23:41:13 GMT
• Title: Generative AI Uses and Risks for Knowledge Workers in a Science Organization
• Authors: Kelly B. Wagman, Matthew T. Dearing, Marshini Chetty,
• Abstract summary: Generative AI could enhance scientific discovery by supporting knowledge workers in science organizations.<n>We report on a study with a US national laboratory with employees spanning Science and Operations about their use of generative AI tools.<n>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.
• Score: 4.035007094168652
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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