Who I Am

I am a Research Scientist at Google Research, working in the Responsible AI and Human-Centered Technology research group. Prior to this, I was a postdoc at Microsoft Research with the FATE (Fairness, Accountability, Transparency, and Ethics in AI) research group, and I completed my PhD in 2020 at the Human-Computer Interaction Institute at Carnegie Mellon University, where I was a Siebel Scholar and a fellow in the PIER Program in Interdisciplinary Education Research. Before that, I completed an M.S. in the Digital Media program at Georgia Tech and was a teacher at a public school in Maryland.

What I Do

My research draws on theories and methods from HCI to develop tools and processes to support more responsible AI design and development. I am particularly interested in how these approaches take shape in natural language technologies, in applications in educational AI systems, and in public sector AI/ML systems more generally.

Recent News

A paper on how cross-functional teams collaborate to address fairness in AI systems was accepted to the ACM conference on Fairness, Accountability, and Transparency in Sociotechnical Systems (FAccT) 2023 conference! Led by Wesley Deng and co-authored with Nur Yildirim, Monica Chang, Motahhare Eslami, and Ken Holstein.
A paper on how User Experience design and evaluation practices are changing to develop more responsible AI systems was accepted to the ACM conference on Human Factors in Computing Systems (CHI) 2023 conference! Led by Qiaosi Wang and co-authored with Shivani Kapani, Shaun Kane, Mike Terry, and Lauren Wilcox.
A paper on how ethical AI development toolkits envision and support the work of ethics in AI development was accepted to the ACM conference on Computer-Supported Cooperative Work (CSCW)! With Richmond Wong and Nick Merrill.
Co-organized a workshop at the intersection of Human-Computer Interaction and Natural Language Processing at the NAACL conference in Seattle. Co-organized with Su Lin Blodgett, Hal Daumé III, Ani Nenkova, Brendan O'Connor, Hanna Wallach, and Qian Yang.
A book chapter on structural injustice in educational AI will be published in a forthcoming book on ethics in AI for education, published by Routledge. Co-authored with Su Lin Blodgett, Elijay Mayfield, and Ezekiel Dixon-Román.

Selected Publications

Please see my Google Scholar page for the most up-to-date publications.

Madaio, M., Egede, L., Subramonyam, H., Wortman Vaughan, J., & Wallach, H. (2022). Assessing the Fairness of AI Systems: AI Practitioners’ Processes, Challenges, and Needs for Support. Proc. ACM Hum.-Comput. Interact. 6, CSCW1, Article 52 (April 2022), 26 pages. https://doi.org/10.1145/3512899. [pdf]

Madaio, M., Stark, L., Wortman Vaughan, J., Wallach, H. (2020). Prompting Conversations about Fairness in AI Design with Checklists. Presented at the Ethics in Design Workshop at the 2020 CSCW Conference on Computer Supported Cooperative Work and Social Computing. [pdf]

Madaio, M., Stark, L., Wortman Vaughan, J., Wallach, H. (2020). Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI. In the Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-14). [pdf]
Best Paper Award

Madaio, M., Stark, L., Wortman Vaughan, J., Wallach, H. (2020). The Need for Top-Level Performance Indicators to Support Fairness in AI. Presented at the Workshop on Fair and Responsible AI at the 2020 CHI Conference on Human Factors in Computing Systems. [pdf]

Black, E., Williams, J., Madaio, M., Donti, P.L. (2020). A Call for Universities to Develop Requirements for Community Engagement in AI Research. Presented at the Workshop on Fair and Responsible AI at the 2020 CHI Conference on Human Factors in Computing Systems. [pdf]

Madaio, M., Yarzebinski, E., Kamath, V., Akpe, H., Blahoua Seri, A., Tanoh, F., Jasinska, K. & Ogan, A. (2020). Collective Support and Independent Learning with a Voice-Based Literacy Technology in Rural Communities. In the Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-14). [pdf]

Chatterjee, R., Madaio, M., Ogan, A. (2020). Predicting Gaps in Usage in a Phone-Based Literacy Intervention System. In the Proceedings of the In International Conference on Artificial Intelligence in Education (pp. 92-105). Springer, Cham. [pdf]
Best Student Paper Award


Mayfield, E., Madaio, M., Prabhumoye, S., Gerritsen, D., McLaughlin, B., Dixon-Roman, E., Black, E. (2019). Equity Beyond Bias in Language Technologies for Education. In 14th Workshop on Innovative Use of NLP for Building Educational Applications, at ACL 2019. [pdf]

Madaio, M., Martin, S.E. (2019). Who owns the Smart City? Towards an Ethical Framework for Civic AI. In Scherling, Laura, and Andrew DeRosa, eds. Ethics in Design and Communication: Critical Perspectives. Bloomsbury Publishing, 2020. [book] [pre-print of the chapter available on request].

Madaio, M., Kamath, V., Yarzebinski, E., Zasacky, S., Tanoh, F., Hannon-Cropp, J., Cassell, J., Jasinska, K. & Ogan, A. (2019). "You Give a Little of Yourself": Family Support for Children’s Use of an IVR Literacy System. In the Proceedings of the 2019 ACM SIGCAS Conference on Computing and Sustainable Societies (ACM COMPASS). [pdf]
Best Paper Award

Madaio, M., Tanoh, F., Blahoua Seri, A., Jasinska, K. & Ogan, A. (2019). "Everyone Brings Their Grain of Salt": Designing for Low-Literate Parental Engagement with a Mobile Literacy Technology in Côte d'Ivoire. Accepted to the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI). [pdf]


Lee, J., Lin, Y., and Madaio, M. (2018). A Longitudinal Evaluation of a Deployed Fire Risk Model. In the AI for Social Good Workshop at the Neural Information Processing System Conference. (NeurIPS 2018). [pdf]

Singh Walia, B., Hu, Q., Chen, J., Chen, F., Lee, J., Kuo, N., Narang, P., Batts, J., Arnold, G., and Madaio, M. (2018). A dynamic pipeline for spatio-temporal fire risk prediction. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (KDD). [pdf]


Madaio, M., Chen, S.T, Haimson, O.L., Zhang, W., Cheng, X., Hinds-Aldrich, M., Chau, D.H., and Dilkina, B. (2016). Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM. 2016, pp. 185–194. [pdf]
Best Student Paper, Runner-Up