Verification Is Not Knowledge: Science needs more than answers
Jessica Hullman argues that verifiable AI outputs alone do not constitute scientific knowledge without human understanding and accountability.
The essay critiques the value-free ideal that has shaped science since the Cold War era, warning that prioritizing correct outputs over human insight risks undermining science's social contract and critical thinking. Examples include AI-generated cancer treatments passed through trials but lacking human explanation, and papers where authors cannot explain claims. The author argues this framing mistakes verified results for knowledge and threatens the capacities that sustain scientific progress.
- Science requires human understanding and accountability for claims
- AI outputs can pass trials without human context or explanation
- The value-free ideal distorts how we judge evidence and progress
- Pushback against AI in science is not anti-AI or anti-science
- Organizations
- Northwestern University
From the original article
14 min read at blog.cosmos-institute.org
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.”
By ersinesen · blog.cosmos-institute.org
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