The Sloan Foundation continually explores the intersection of research and technology to identify emerging focus areas where recent innovation, changing contexts, or scarce funding open up potential opportunities for new programs. Exploratory grantmaking is intended to bring community needs and priorities into sharper focus and allow us to determine whether there is a clear strategy and potential impact for the Foundation in a specific area. Supported activities may include workshops and other expert convenings, early software development and prototyping, landscape analyses, development of protocols and standards, initial research on and engagement with potential user communities, and demonstration or other proof-of-concept projects.
Open and cheap hardware has the potential to revolutionize the creation and deployment of sensors and other scientific instruments, expanding access and lowering barriers to innovation in data-driven research methods. Grants in this focus area seek to explore the potential for Foundation support to have an impact on the development of best practices, data standards, and emerging new practitioner communities in open hardware.
Trust in Algorithmic Knowledge
The complexity and opacity of AI-driven research methods has raised new questions about the degree to which their results can or should be trusted. Issues examined in this focus area include identifying and mitigating algorithmic bias, the role of training and benchmarking datasets in AI development, how Machine Learning techniques enhance or degrade rigor and reproducibility, and the ways that algorithmic recommendation systems influence trust in knowledge. Grants focus on exploring these issues with an eye toward understanding the potential for Foundation impact.
Interested grantseekers should email a letter of inquiry of no more than two pages to email@example.com.