互惠协作：GLAM领域融合的经验如何增强跨学科人工智能研究
Reciprocal Collaboration: how lessons from convergence in GLAMs can enhance interdisciplinary AI research
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- University College Dublin（都柏林大学学院）
- Dublin City University（都柏林城市大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对跨学科AI协作中领域方沦为信息提供者的单向问题，借鉴GLAM融合历史，提出五项关键实践，构建互惠协作框架以促进双向理解与共同影响。
中文摘要 AI 辅助
研究资助机构日益认识到不同研究领域之间协作的必要性。这一需求的重要驱动力是当前人工智能（AI）及相关技术的革命。人们对AI在不同领域的潜在影响越来越感兴趣，包括其所使用的方法论，以及由此带来的新知识、新途径和生产力的提升。然而，与此同时，人们对AI技术的基本原理以及跨学科和/或交叉学科研究的开展方式也日益担忧。由此产生的协作往往最终成为单向通道，领域合作方仅充当信息提供者。例如，艺术、人文与社会科学（AHSS）合作方的贡献可能仅限于提供关于伦理的见解，而技术合作方可能仅提供构建基于AI的应用解决方案的服务。针对这一问题，我们提出一种互惠的协作方法，即双方都寻求理解、合作并识别共同重要的影响。在本文中，我们探讨了文化遗产机构（GLAM）、艺术、人文与社会科学（AHSS）研究以及技术主导的AI研究之间的关系，特别是当前AI技术进步的影响。借鉴GLAM研究中融合的历史，我们提出了五项关键实践，以形成一个框架，促进跨越这一鸿沟的更深入理解。
英文摘要
The need for collaboration between diverse fields of research is increasingly recognised as important by research funding agencies. A significant driver of this need is the current revolution in artificial intelligence (AI) and related technologies. There is a growing interest in the potential impact of AI in different fields including the methodologies they use and the resulting advances in new knowledge, new access and enhanced productivity. However, there is also a corresponding increase in concern about the fundamentals of AI technologies and the way in which trans and/or interdisciplinary research is approached. The resulting collaboration too often ends up as a one-way street where the domain partner acts only as an information provider. For example, the contribution of the AHSS partner might be limited to providing insight about ethics and/or the technology partner may only provide a service to build applied AI-based solutions. In response to this problem, we propose a reciprocal approach to collaboration where both partners seek to understand, cooperate and identify jointly significant impacts. In this paper we explore this relationship between cultural heritage institutions (GLAMs), Arts, Humanities & Social Sciences (AHSS) research and technology-led AI research, especially the impact of current technological advances in AI. Drawing from the history of convergence in GLAM studies, we propose five key practices to form a framework for greater understanding across this divide.
