Recent debates highlight the risk of treating scientific inquiry as merely an automated pipeline for generating verified proofs and treatments. In one scenario highlighted by Terry Tao, an uncomprehended AI cancer therapy might pass trials simply by gaming the trial's hidden flaws. Meanwhile, computer scientist Tom Dietterich pointed to a surge in arXiv papers whose own authors cannot explain their claims, leaving nobody accountable if errors arise. Prioritizing correctness over understanding traces back to a Cold War-era ideal that mistakenly separates scientific discovery from human peer exchange and critical evaluation.
Key points
Human understanding is necessary to integrate scientific discoveries into existing knowle…: Human understanding is necessary to integrate scientific discoveries into existing knowledge and adequately train the next generation of researchers.
Output-driven research leaves scientific fields without accountability when automated fin…: Output-driven research leaves scientific fields without accountability when automated findings turn out to be flawed or fail unexpectedly.
Reducing science to verified answers risks dismissing vital peer exchange and human evalu…: Reducing science to verified answers risks dismissing vital peer exchange and human evaluation as unnecessary obstacles to progress.
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This is a guest essay by Jessica Hullman, Ginni Rometty Professor of Computer Science and Policy Fellow at Northwestern University.
In his essay on the role that rigorous proofs play in mathematical advances, Bill Thurston writes that the measure of success in math research is “whether what we do enables people to understand and think more clearly and effectively about mathematics.” Many recent discussions of AI for science hinge on a tension between understanding and answers. Is science about humans achieving insight, or the archiving of verifiable solutions?
One side prioritizes outputs. Progress is implied to be evident in verifiable proofs of longstanding conjectures and cures for diseases, whether humans were involved in their making or not. The other side argues that human understanding is paramount, because it drives integration of new findings into the canon, because it shapes and inspires the next generation, and so on. But the first side, those who emphasize correct outputs, dismisses this as gatekeeping or nostalgia.
When Terry Tao posed a thought experiment in a recent talk, asking how comfortable people would be taking…
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