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.

翻译：人工智能（AI）正日益广泛地应用于科学研究，但其益处在不同社群和学科间的分布仍不均衡。尽管数据有限、标准碎片化、计算资源获取不平等这类技术挑战已广为人知，但社会和制度性因素往往是主要的制约条件。强调自主“AI科学家”的叙事、对数据与基础设施工作的认可不足、激励机制的错位，以及领域专家与机器学习研究者之间的隔阂，都限制了AI对科学发现的影响力。本文重点阐述了四个相互关联的挑战：社群协调、研究重点与上游需求错位、数据碎片化以及基础设施不均衡。我们认为，应对这些挑战不仅需要技术创新，还需要有意识的社群建设努力、跨学科教育、共享基准测试以及可访问的基础设施。我们呼吁将“AI for Science”重新定位为一个集体性的社会项目，将可持续的协作与公平的参与视为实现技术进步的先决条件。