Stanford Impact Labs - Social Science, Supercharged: Fast Grants for AI-Enabled Research

We invite proposals from Stanford faculty, postdocs, and PhD students for projects that use frontier AI tools to dramatically improve impact-focused social science research. We are intentionally keeping this call broad. Illustrative examples might include:

• New data: using AI to unlock, structure, or analyze new forms of data relevant to social sciences
• New methods: using AI to improve how social scientists generate, test, or synthesize knowledge
• New research architectures: rethinking how social science research is organized and executed, including the use of agentic systems to manage all or part of research workflows (e.g. AI as co-scientist)
• Improved institutions: using AI to improve the institutions and processes through which social science is produced, evaluated, reviewed, replicated, translated, or used.

We’re also open to ideas that fall outside these examples, but otherwise dramatically improve the public value of social sciences. AI is advancing faster than most research timelines. We are particularly interested in work that accounts for a world where AI is significantly more capable than today. 

Out of scope
This fast grants program is not focused on the social science of AI in Society, such as studying the impacts of AI adoption, how AI is reshaping labor markets, or how societal institutions (schools, health systems or public policy) might need to adapt in the age of AI. 

Stanford Impact Labs has existing funding windows that offer larger awards for this type of impact-focused research. About 45% of our existing portfolio is research at the intersection of AI and society. Faculty interested in studying those questions are encouraged to apply through SIL’s regular funding cycles. 

Review criteria
Proposals will be evaluated primarily on three criteria:

1. Novelty: Does the project explore a genuinely new or underdeveloped way that frontier AI could accelerate impact-focused social science?

   • Is this approach genuinely new, or does it represent a modest extension of existing methods? We're looking for significant improvements, not modest gains.

   • Does it take clear advantage of recent advances in frontier AI?

2. Impact: Does the project increase the likelihood that social science will benefit society at scale?

   • Does the proposal generate proof points for how AI could meaningfully improve the speed, quality, relevance or otherwise meaningfully improve the public value of social science research? 

   • Could the approach, if successful, be adopted or adapted by other researchers beyond this project?

   • Does the proposal lay out a credible plan for how this proof-of-concept could lead to a bigger impact down the road? 

3. Team: Does the individual or team have the expertise and capacity to execute what they are proposing?

   • A single applicant can satisfy this criterion if their background covers what the project requires. Interdisciplinary teams should describe each member's role. 

What strong proposals might do

• generate insights, tools, products, workflows or institutional models that could be adopted by other researchers or organizations
• treat current AI capabilities as a floor, not a ceiling, and articulate how the approach scales or becomes more valuable as AI capabilities improve
• examine benefits alongside risks and limitations
• advance the frontiers of what responsible use in AI-enabled social science looks like
• engage decision-makers, practitioners, or other non-academic partners who could put research to use

Not every proposal needs to do all of this. 

Awardee expectations

• Complete the proposed project within the award period
• Share key lessons, outputs, or proof points from the work publicly 
• Present their project at a demo day.

Questions?
Read our Frequently Asked Questions which we will update periodically. You can find a PDF version of our RFP here.