Thema Monroe-White

Thema (pron: Tay-mah) Monroe-White is an Associate Professor of Artificial Intelligence and Innovation Policy in the Schar School of Policy and Government and Department of Computer Science (joint) at George Mason University. Her broad interests include bias mitigation in artificial intelligence (AI), critical quantitative and computational methods, and racial equity in innovation and entrepreneurship (I&E). As an interdisciplinary scholar, her work explores the systemic biases that affect the workforce and educational journeys of racially minoritized groups within science, technology, engineering, and mathematics (STEM) fields. She is particularly concerned with understanding the pathways to achieving social and economic empowerment for minoritized groups via I&E, AI literacy, and emancipatory data science.

She has received multiple NSF awards to investigate issues of racial equity in the STEM ecosystem, including serving as a lead member of the research team for the Inclusion in Innovation Initiative (i4), a $3.5 million cooperative partnership to develop a national infrastructure for diversity and inclusion within the NSF Innovation Corps (I-Corps™) Program.  Inspired by her collaborative research on intersectional inequalities in science, she has also received funding to investigate the harms of structural racism on the scientific enterprise, and the benefits derived by the inclusion of historically marginalized groups in the scientific workforce.

Dr. White is a Fellow of the Institute in Critical Quantitative and Mixed Methodologies (ICQCM), and in 2021 she was appointed a three-year term as a special government employee data scientist on the Bureau of Labor Statistics Technical Advisory Committee (BLSTAC). She serves as a senior advisor for multiple non-profit, community, and philanthropic agencies on equitable pathways in data science education, race and gender equity in the AI workforce, and fostering diversity in STEM entrepreneurship. She is a sought-after speaker at several national and international conferences, including invited panels at the National Academies, and the White House.

Thema holds a Ph.D. in science, technology, and innovation policy from the Georgia Institute of Technology, as well as Master's and Bachelor’s degrees in Psychology from Howard University.

Areas of Research

• Artificial intelligence
• Comparative Studies
• Critical Quantitative and Computational Methods
• Data Science Education
• Economics, Development & Public Finance
• Entrepreneurship & Innovation
• Methods & Data Science
• Public Private & Nonprofit Management
• Racial Equity in STEM
• Science & Technology

Degrees

• PhD, Science, Technology, and Innovation Policy, Georgia Institute of Technology
• Master of Science, Developmental Neuropsychology, Howard University
• Bachelor of Arts, Psychology, Howard University

Select Publications

• Kozlowski, D., Monroe‐White, T., Larivière, V., & Sugimoto, C. R. (2024). The Howard‐Harvard effect: Institutional reproduction of intersectional inequalities. Journal of the Association for Information Science and Technology.
• Shieh, E., Vassel, F. M., Sugimoto, C., & Monroe-White, T. (2024). Laissez-Faire Harms: Algorithmic Biases in Generative Language Models. arXiv preprint arXiv:2404.07475.
• Monroe-White, T., & McGee, E. (2024). Toward a race-conscious entrepreneurship education. Entrepreneurship Education and Pedagogy  7(2), 161-189.
• Monroe-White, T., & Lecy, J. (2023). The Wells-Du Bois Protocol for machine learning bias: building critical quantitative foundations for third sector scholarship. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 34(1), 170-184.
• Kozlowski, D., Larivière, V., Sugimoto, C. R., & Monroe-White, T. (2022). Intersectional inequalities in science. Proceedings of the National Academy of Sciences, 119(2), e2113067119.
• Kozlowski, D., Murray, D. S., Bell, A., Hulsey, W., Larivière, V., Monroe-White, T., & Sugimoto, C. R. (2022). Avoiding bias when inferring race using name-based approaches. Plos one, 17(3), e0264270.
• Monroe-White, T.^*(2021, June). Emancipatory Data Science: A Liberatory Framework for Mitigating Data Harms and Fostering Social Transformation. In 2021 ACM SIGMIS Computers and People Research Conference, June 2021.