Every major tech company is building AI to assess research impact. Google Scholar already ranks papers algorithmically. Semantic Scholar uses machine learning to predict which studies will be influential. Microsoft Academic tried before it shut down. The infrastructure for automated scientific evaluation exists today.
The question is not whether AI will evaluate science. It is whether that evaluation will be transparent, auditable, and aligned with what science actually needs.
Here is the risk: proprietary algorithms deciding which research gets visibility, funding, and credit. A neural network trained on citation patterns might optimize for what gets cited rather than what gets used. An AI system built by a for-profit company might prioritize metrics that serve corporate interests rather than scientific progress.
We have seen this movie before. Social media algorithms optimized for engagement and got polarization. Search algorithms optimized for clicks and got clickbait. If we build opaque AI systems that optimize for the wrong signals in science, we will get research optimized for algorithmic approval rather than genuine discovery.
The alternative: evaluation systems where every input is visible, every weight is auditable, every decision can be challenged. Systems where the community can see why a contribution was valued, verify the logic, and correct errors. Systems that amplify human judgment rather than replacing it with a black box.
This is the difference between AI as oracle and AI as tool. An oracle gives you answers you cannot question. A tool gives you capabilities you can direct. Science needs tools, not oracles.
The technical challenge is significant. Scientific impact is multidimensional and domain-specific. A breakthrough in theoretical physics looks different from a methodological advance in epidemiology. Building evaluation systems that capture this diversity without becoming unwieldy requires careful design and continuous community feedback.
But the technical challenge is solvable. The harder problem is governance: who controls the evaluation criteria? Who audits the system? Who decides what counts as valuable contribution?
A meritocratic approach to science funding means building AI evaluation as transparent infrastructure, not proprietary gatekeeping. The algorithms should be open-source. The training data should be public. The evaluation criteria should be community-governed. The outputs should be explainable.
The discussion: Would you trust an AI system to evaluate your research contributions if you could see exactly how it worked? What would need to be true for that trust to be justified? And who should control the evaluation criteria—the scientists, the funders, or the platform builders?
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