AI for Scientific Discovery is a Social Problem
This addresses the problem of uneven distribution of AI benefits in science for researchers and institutions, but it is incremental as it builds on existing critiques of social factors in technology.
The paper argues that the main barriers to AI accelerating scientific discovery are social and institutional, such as misaligned incentives and community dysfunction, rather than just technical obstacles, and calls for reframing it as a collective social project.
Artificial intelligence promises to accelerate scientific discovery, yet its benefits remain unevenly distributed. While technical obstacles such as scarce data, fragmented standards, and unequal access to computation are significant, we argue that the primary barriers are social and institutional. Narratives that defer progress to speculative "AI scientists," the undervaluing of data and infrastructure contributions, misaligned incentives, and gaps between domain experts and machine learning researchers all constrain impact. We highlight four interconnected challenges: community dysfunction, research priorities misaligned with upstream needs, data fragmentation, and infrastructure inequities. We argue that their roots lie in cultural and organizational practices. Addressing them requires not only technical innovation but also intentional community-building, 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 technical progress.