Stanford Impact Labs Invests in Research Teams Advancing Health, Labor, Democracy, and Technology Solutions

Guided by the belief that universities are uniquely positioned to generate knowledge that can help solve some of the world’s toughest social challenges, Stanford Impact Labs (SIL) invests in solutions-focused social science to help bridge the gap between scientific insight and real-world impact. Our funding model is specifically designed to support Stanford-led research teams working closely with external partners and stakeholders as they move ideas from early exploration to putting insights to use for society. Our Stage 2 funding helps position teams to rigorously test solutions in real-world settings so as to generate evidence not only about whether an approach works, but how it can be adapted and scaled to improve people’s lives. 

Each year, the small number of ambitious research projects selected for Stage 2 funding are led by teams who demonstrate a clear understanding of the problem landscape, a compelling solution that is ready to be tested, and evidence of strong partnerships with government, nonprofit, or industry partners positioned to meaningfully contribute to, or act on, the findings. 

We are pleased to introduce our 2025 Stage 2 investments, all of which reflect SIL's commitment to catalyzing field-leading social science research that is collaborative, evidence-driven, and designed for real-world impact.

Detecting Human Trafficking and Deforestation in Supply Chains

Modern slavery affects an estimated 50 million people globally. Despite growing regulation and public pressure for supply chain transparency, severe exploitation is prevalent in global supply chains. A lack of comprehensive supply chain data linking abuses to specific producers limits the effectiveness of these regulations. Without transparency and microdata, compliance often relies on self-reporting from first-tier partners, while financial incentives for exploitation persist. 

In Latin America, Brazil represents both the region’s largest economy and population, making human trafficking a significant concern. To address this challenge, this team of researchers and government partners led by Grant Miller, Henry J. Kaiser, Jr. Professor of Health Policy at the Stanford School of Medicine, will develop a new data-driven technical tool that integrates data from siloed publicly available administrative and legal records to help regulators, investors, and firms make evidence-based assessments to take targeted action against illegal behavior. 

Elevating Women’s Voices in Policymaking in India

Despite widespread policies aimed at gender equality, women remain poorly represented in politics and policy in India. To address this challenge, this team of researchers and practitioner partners led by Soledad Artiz Prillaman, Assistant Professor of Political Science at Stanford’s School of Humanities and Sciences, leveraged prior Stage 1 funding to create and pilot three interventions that link top-down and bottom-up institutions as a means of catalyzing systemic change. 

With Stage 2 funding, the team will conduct a randomized controlled trial of the interventions—an agency- and capacity-building training program for elected women representatives (EWRs), a peer-support network for EWRs, and the facilitation of women-only citizen-representative political forums—across two states. 

Empowering Diverse Digital Citizens

In today’s rapidly evolving information ecosystem, people face significant challenges discerning reliable and trustworthy information. Social media has amplified these risks, with increased exposure to scams, fraud, low quality information, and false news content. This trend disproportionately harms vulnerable groups, including older adults, adolescents, and diverse communities, who are often targeted but have fewer skills and resources to protect themselves. Generative AI further intensifies these challenges, as it can now create highly persuasive text, images, and voices at scale and deploy them through microtargeting.

With prior Stage 2 funding, this team of researchers and industry and nonprofit partners led by Jeffrey Hancock, Harry and Norman Chandler Professor of Communication at Stanford’s School of Humanities and Social Sciences, designed and tested digital media literacy interventions to help people make better-informed decisions. With current Stage 2 funding, the team will expand its digital literacy interventions to develop, evaluate, and scale validated AI literacy interventions to mitigate AI harms and the unequal distribution of benefits across society. 

Strengthening Maternal and Infant Care through GenAI Mobile Messaging

Every year, 1.3 million children under the age of five die in South Asia, often from preventable causes. Maternal mortality in the region is also high. Many of these deaths could be averted through improved health practices, and yet under-resourced health systems can be challenged to communicate vital information to patients effectively.

This team, led by Charles Prober, Professor of Pediatrics (Infectious Disease) at the Stanford School of Medicine and founding executive director of the Stanford Center for Health Education, has tested and is working to expand the capacity and reach of a tool developed and deployed by partner Noora Health to address this crisis. The mobile Care Companion Program (mCCP) provides perinatal care guidance through mobile phone messaging to new mothers and families. With Stage 2 funding, the team will study the impact of integrating generative AI into mCCP on response time, user experiences, health-related behaviors, and health outcomes.

Stage 2: Test Solutions provides flexible funding for Stanford-led research teams who are working in partnership and focused on developing and testing solutions in real-world settings. For more detailed information about eligibility, selection criteria, application requirements, and budget guidelines, please review our Stage 2: Test Solutions Request for Proposals (RFP). SIL is currently accepting 2026 Stage 2 applications through October 20, 2025.