AI推动科学发现需突破社会壁垒，而非仅靠技术进步。
AI for Scientific Discovery is a Social Problem
- 将AI科研视为社会协作工程，强调社区共建与跨学科合作。
- 揭示数据碎片化、资源不均等社会因素制约技术落地。
- 适合关注科研公平性与跨领域协作的研究者参考。
人工智能在科学研究中的应用日益广泛，但其收益在不同学科和群体间分布不均。尽管数据有限、标准分散、计算资源获取不平等等技术挑战已广为人知，社会与制度因素往往是主要瓶颈。自主‘AI科学家’的叙事掩盖了数据与基础设施工作的价值，激励机制错位，领域专家与机器学习研究者之间存在鸿沟，均限制了AI对科学发现的影响。本文聚焦四大相互关联的挑战：社区协调、研究优先级与上游需求的错配、数据碎片化及基础设施不平等。我们主张，解决这些问题不仅需要技术创新，还需有意识地开展社区建设、跨学科教育、共享基准与可访问基础设施。呼吁将AI用于科学重新定位为集体社会项目，可持续协作与公平参与应作为技术进步的前提。
原文摘要 · Abstract (English)
Artificial intelligence (AI) is being increasingly applied to scientific research, but its benefits remain unevenly distributed across different communities and disciplines. While technical challenges such as limited data, fragmented standards, and unequal access to computational resources are already well known, social and institutional factors are often the primary constraints. Narratives emphasizing autonomous "AI scientists," the underrecognition of data and infrastructure work, misaligned incentives, and gaps between domain experts and machine learning researchers all limit the impact of AI on scientific discovery. Four interconnected challenges are highlighted in this paper: community coordination, the misalignment of research priorities with upstream needs, data fragmentation, and infrastructure inequities. We argue that addressing these challenges requires not only technical innovations but also intentional community-building efforts, cross-disciplinary education, shared benchmarks, and accessible infrastructure. We call for reframing AI for science as a collective social project, where sustainable collaboration and equitable participation are treated as prerequisites for achieving technical progress.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。