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

2025-01-27

AI Summary

This study investigates the actual usage patterns, application scenarios, and multidimensional risks of generative AI among scientific and operational staff at U.S. national laboratories. Method: A mixed-methods approach was employed, integrating 66 surveys, 22 in-depth interviews, and analysis of real-world usage logs from the Argo platform. Contribution/Results: The study identifies, for the first time within a real scientific organization, a dual-mode generative AI adoption paradigm—copilot (human-AI collaboration) and workflow agent (autonomous task orchestration)—and empirically links adoption behaviors to three cross-cutting risk domains: data security, scholarly publishing integrity, and workforce impact. Although current usage remains low, it is steadily increasing; four high-frequency application scenarios are distilled. Based on these findings, the study proposes a balanced AI implementation framework for research organizations—one that simultaneously supports responsible governance and capability enhancement—providing empirical grounding for science institutions to develop context-sensitive, differentiated AI policies.

Technology Category

• Application Domains: Humanities & Computational Social Science
• Philosophy and Ethics of AI: Artificial General Intelligence
• Natural Language Processing: Generation

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

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.

Problem

• Generative AI
• Scientific Institutions
• Impact on Research and Security

Innovation

• Generative AI in Research Institutions
• Risk Identification
• Strategic Recommendations