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

AI capabilities are advancing fast. How might AI be used to dramatically improve impact-focused social science? Through this fast grants program, we seek to support novel uses of frontier AI tools that dramatically accelerate the speed, quality, or relevance of social science research.

This fast grants program is intended to generate proof points for how AI can help shape social science disciplines, expanding the questions researchers can tackle and the methods they use to do so. By supporting these innovations, designed for others to learn from and build on, we hope to enable meaningfully more ambitious and socially impactful research that expands the public value of social science. 

Purpose

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. Proposed work should generate insights, tools, products, workflows or institutional models that could be used by other researchers.

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. (e.g. reproducibility & replicability, metascience, peer review, etc)

We’re also open to ideas that fall outside these examples, but otherwise dramatically improve impact-focused social science. 

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 it is 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 policies) might need to adapt in the age of AI. 

Stanford Impact Labs has existing funding opportunities 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 four criteria:

1. Novelty

Does the project explore a genuinely new or underdeveloped way that frontier AI could improve impact-focused social science?

Reviewers will consider:

• 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?

Our primary consideration is novelty in the use of AI to improve social science. For proposals that apply AI to a specific research question, reviewers will also consider the novelty of the research within its field (disciplinary novelty), but as a secondary factor.

2. Impact on Social Science

Will the project improve impact-focused social science beyond the applicant’s own work?

Reviewers will consider:

• Does the proposal generate proof points for how AI could meaningfully improve the speed, quality, relevance, or other aspects of impact-focused social science? 
• Could other researchers adopt or adapt the project’s outputs? (e.g., methods, tools, data, insights, workflows, or some mix of these)? 
• Is there a clear and credible plan to drive adoption and demand from other researchers?

3. Impact on Society

Does the project increase the likelihood that social science will benefit society at scale?

Reviewers will consider: 

• Would other researchers’ use of the project’s outputs plausibly lead to better real-world decisions by stakeholders beyond academia?

We welcome proposals that have a direct path to societal benefit (i.e. a plausible case for impact from the researchers’ own work), as long as there is also opportunity for other researchers to use the project’s outputs to improve impact-focused social science more generally as well. 

4. 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. Applications from teams should describe key members’ roles. 

What Strong Proposals Might Do

Strong proposals may:

• treat current AI capabilities as a floor, not a ceiling, and articulate how the approach scales or becomes more valuable as AI capabilities improve
• generate insights, tools, products or workflows that can be used not only by researchers (the focus of this RFP), but also by policymakers or practitioners
• engage decision-makers, practitioners, or other non-academic partners who could put research to use
• examine the risks and limitations of AI in social science in addition to the benefits
• advance the frontiers of what responsible use in AI-enabled social science looks like
• join forces with other teams working on similar challenges across different disciplines (in our first cycle, we saw how many teams aimed to address similar challenges)

These are potential markers of a strong proposal but should be treated as optional. We welcome proposals that display other strengths as well. 

Awardee Expectations

Awardees will be expected to:

• 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.

For more information, view our Frequently Asked Questions.