Dallas (SMU), January 14, 2025 – SMU DataArts is pleased to announce it has been approved by the National Endowment for the Arts (NEA) for a Research Grants in the Arts award of $50,000. This grant will support a study using artist grant data from the Greater Pittsburgh Arts Council to identify factors that may promote or inhibit equitable grantmaking.

In total, the NEA will award 18 Research Grants in the Arts for a total of $1,045,000 in funding to support a broad range of research studies that investigate the value and/or impact of the arts, either as individual components of the U.S. arts ecosystem or as they interact with each other and/or with other domains of American life.

NEA Director of Research and Analysis Sunil Iyengar said, “The research undertaken by these NEA grant recipients, including SMU DataArts, covers a compelling array of fields and topics. The studies will contribute to a formidable body of research that is strengthening public knowledge about the arts’ benefits to our lives and communities.”

“Understanding how bias and inequities may influence grantmaking processes is essential to creating more equitable opportunities for artists and cultural organizations,” said Daniel Fonner, principal investigator for the project. “This research uses artificial intelligence as an evaluation tool, helping us uncover patterns in decision-making that can inform more transparent and equitable funding practices. Our collaboration with the Greater Pittsburgh Arts Council will not only deepen our understanding of local grant programs but also lay the groundwork for tools that can be applied across the broader arts funding ecosystem. We are grateful to the NEA for supporting this important work.”

The research will harness the power and scalability of machine learning to support the evaluation of grantmaking programs with a focus on equitable outcomes in panel review processes. The project will focus on data provided by our partners at the Greater Pittsburg Arts Council from an artist funding program. The research will seek to answer the following questions:

• What are the key factors in grant application materials leading to application success?  

• What grant panelist characteristics, if any, may contribute to biased outcomes when evaluating artist grant applications?  

• Can measures be developed from the data to support the evaluation of other machine learning datasets?

This study will build upon principal investigator Daniel Fonner’s previous work  and collaborations with the Computer Science Department at SMU’s Lyle School of Engineering to employ machine learning as an evaluation tool for grantmaking. As a precursor to this NEA-funded project, SMU DataArts received funding from the IBM Center for the Business of Government to produce a forthcoming report for public administrators about responsible uses of AI for evaluation.