Pressure-Testing Our Research as GiveWell Grows

This is a new episode of the GiveWell Conversations podcast. You can listen to the episode or read a summary of the conversation below.

GiveWell works to continually improve our research so we can make better grant decisions and direct donations where we believe they can do the most good. GiveWell’s Cross-Cutting research subteam is an important part of that effort—it investigates the impact of the programs we fund, works to resolve our largest uncertainties, and aims to improve our research. As GiveWell’s grantmaking grows, the Cross-Cutting subteam is strengthening our data collection and analysis to support that growth. 

In this episode, GiveWell co-founder and CEO Elie Hassenfeld speaks with Program Director Alex Cohen, who leads the Cross-Cutting subteam, about how better data can improve our decision-making, our work to gather stronger evidence, and the ways we’ve tested AI as a tool to increase our research capacity and quality.

Elie and Alex discuss:

• Triangulating multiple data sources to check impact: Directing donations to programs we believe will do the most good requires understanding the impact of the programs we support. Organizations provide us with monitoring data, but we know there are many ways that, despite the best intentions, this data can be biased or incomplete. To address this, we are investing in additional sources and types of data to improve the accuracy of our estimates. For example, in Burkina Faso we’re funding dried-blood tests to better understand whether caregivers are completing the second and third at-home doses of seasonal malaria chemoprevention (SMC), which provides preventive malaria medication. We think this will improve our understanding of how accurate self-reported data is and whether funding to boost coverage of second and third doses of the medication could cost-effectively help people.
• Setting quality standards for survey data: GiveWell often collects survey data at the end of a program to better understand its impact and answer our open questions, but survey data is prone to error. For example, GiveWell researchers shadowed surveyors in Uganda and Nigeria who worked long days under time pressure, creating conditions where hard-to-access households were more likely to be skipped. Because those households may also be the ones programs are most likely to miss, this could lead to overestimating coverage. We’re working to reduce these errors by investing in more independent data checks, and we’ve developed survey guidelines that include practices like GPS tracking and verifying whether a random sample of households was surveyed.
• Using AI to critique our work: GiveWell is experimenting with AI tools to increase our research capacity and improve its quality. Our cost-effectiveness models are an important part of our funding decisions, and we carefully vet them to reduce the chance of mistakes. We’ve developed and trained an AI model that, with human oversight, can catch errors that previously required tedious staff review, opening up some capacity for those reviewers to support the team in other ways. We're also experimenting with using AI to critique and identify weaknesses in our research, though we’ve had more limited success with this strategy.  

By investing in more and better data and experimenting with new tools, we’re working to expand our research capacity while strengthening the rigor of our work as our grantmaking scales. 

Growing our research team is central to these goals, and GiveWell is actively hiring researchers and program officers. New members of our research team typically start on the Cross-Cutting subteam as part of their onboarding and training. If someone you know might be a good fit, please share our open roles!

Visit our All Grants Fund page to learn more about how you can support this work, and listen or subscribe to our podcast for our latest updates.

This episode was recorded on August 21, 2026, and represents our best understanding at that time.