Executive summary: GiveWell reports that using AI to red team its global health research has surfaced some worthwhile critiques—especially by filling literature gaps—but remains limited by low relevance rates, unreliable quantitative claims, and the need for substantial human filtering, and the team invites others to test alternative AI critique methods.
Key points:
GiveWell piloted a two-stage AI red teaming process—AI literature synthesis followed by AI critique of internal analysis—across six grantmaking areas.
The approach generated several critiques worth investigating, such as reinfection risks in syphilis programs, natural recovery bias in malnutrition treatment, and strain mismatch in malaria vaccines.
The prompting strategy emphasized generating many candidate critiques, checking for novelty against the report, using structured categories, and including prompts aimed at less obvious perspectives.
The authors found AI most useful for identifying relevant academic literature they had not yet incorporated, but least useful for interventions already extensively reviewed.
AI-generated quantitative impact estimates were often unsupported, and roughly 85% of critiques were filtered out as irrelevant or based on misunderstandings.
GiveWell chose not to pursue more complex workflows or custom tooling, judging that expected gains would likely be marginal relative to added friction, while remaining open to contrary evidence from others.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, andcontact us if you have feedback.
Executive summary: GiveWell reports that using AI to red team its global health research has surfaced some worthwhile critiques—especially by filling literature gaps—but remains limited by low relevance rates, unreliable quantitative claims, and the need for substantial human filtering, and the team invites others to test alternative AI critique methods.
Key points:
GiveWell piloted a two-stage AI red teaming process—AI literature synthesis followed by AI critique of internal analysis—across six grantmaking areas.
The approach generated several critiques worth investigating, such as reinfection risks in syphilis programs, natural recovery bias in malnutrition treatment, and strain mismatch in malaria vaccines.
The prompting strategy emphasized generating many candidate critiques, checking for novelty against the report, using structured categories, and including prompts aimed at less obvious perspectives.
The authors found AI most useful for identifying relevant academic literature they had not yet incorporated, but least useful for interventions already extensively reviewed.
AI-generated quantitative impact estimates were often unsupported, and roughly 85% of critiques were filtered out as irrelevant or based on misunderstandings.
GiveWell chose not to pursue more complex workflows or custom tooling, judging that expected gains would likely be marginal relative to added friction, while remaining open to contrary evidence from others.
This comment was auto-generated by the EA Forum Team. Feel free to point out issues with this summary by replying to the comment, and contact us if you have feedback.