AI Audits

AI Audits is Research3’s system for evaluating, verifying, and stress-testing scientific claims, summaries, and conclusions against the underlying research literature.

As AI becomes increasingly embedded in scientific workflows, the risk is no longer just missing information—it is confidently presented but weakly supported conclusions. AI Audits is designed to address this problem by making evidence, assumptions, and uncertainty explicit.

Rather than generating new claims, AI Audits analyzes existing outputs—whether produced by AI systems, research reports, summaries, or human-written analyses—and audits them against identifiable scientific sources within Research3’s corpus of 100M+ papers.

AI Audits enables users to:

• Verify whether claims are supported by cited research

• Identify missing, weak, or contradictory evidence

• Detect overgeneralization, cherry-picking, or unsupported extrapolation

• Trace conclusions back to original studies and methodologies

• Surface conflicting findings across the literature

The system evaluates how well conclusions align with the strength, scope, and limitations of the underlying evidence. It highlights where findings are robust, where results depend on specific assumptions, and where scientific consensus does not exist.

AI Audits is especially valuable in high-stakes research contexts, including policy analysis, healthcare, climate science, AI safety, and interdisciplinary work where misinterpretation of evidence can have real-world consequences. By grounding analysis in verifiable literature, it helps reduce the spread of overstated or misleading scientific claims.