Thais, Savannah and Trotta, Roberto and Suri, Nathan and Sullivan, Emily and Poonamallee, Viyan and Na Narong, Tanaporan and Croft, Rupert and Hartman, Nicole (2026) AI for Science Needs Scientific Alignment. [Preprint]
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Abstract
This position paper argues that realizing AI’s potential for science while protecting science as a knowledge-producing institution requires alignment to science’s epistemic goals and values—a challenge that neither general AI alignment nor responsible AI frameworks adequately address. As investment in AI for science grows, troubling patterns multiply: conflicting claims about fundamental capabilities, documented contraction of research toward AI-amenable problems, and benchmark-driven development disconnected from scientific needs. We contend that science is an inherently valuable epistemic system oriented toward human understanding—not merely prediction—and that its value and reliability depend on social infrastructure that is now threatened by misaligned AI integration. We propose a new field of study, scientific alignment: ensuring AI systems optimize for epistemic norms like traceability, self-consistency, and support for human comprehension (technical alignment), while developing governance structures that sustain science’s social infrastructure (systemic alignment). We outline concrete research directions and argue that the goal is not to constrain AI, but to ensure it serves the genuine aims of scientific inquiry.
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