Generative AI Uses and Risks for Knowledge Workers in a Science Organization
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913667970236416 |
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| author | Wagman, Kelly B. Dearing, Matthew T. Chetty, Marshini |
| author_facet | Wagman, Kelly B. Dearing, Matthew T. Chetty, Marshini |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_16577 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI Uses and Risks for Knowledge Workers in a Science Organization Wagman, Kelly B. Dearing, Matthew T. Chetty, Marshini Human-Computer Interaction Artificial Intelligence 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. |
| title | Generative AI Uses and Risks for Knowledge Workers in a Science Organization |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2501.16577 |