Sociotechnical Methods for AI Auditing
AI technologies are more widely deployed in a range of domains across society. While a wide range of evaluations and benchmarks have been developed to score how well AI systems perform relative to specific tasks and goals, it is also necessary to assess their effects in the real world, on individuals, communities and institutions. In this methods seminar, Mona Sloane and Emanuel Moss will discuss a range of methodological approaches to AI audit that center their role in social settings. Drawing on their training in the social sciences, Drs. Sloane and Moss will each discuss techniques for examining how the underlying assumptions made by AI systems about society are key to auditing their effectiveness in specific settings. They will also explore techniques for analyzing the process of AI production to reveal these underlying assumptions, and approaches to conducting this work in both academic and industry settings.
Mona Sloane BIO:
Mona Sloane, Ph.D., is an Assistant Professor of Data Science and Media Studies at the University of Virginia (UVA). As a sociologist, she studies the intersection of technology and society, specifically in the context of AI design, use, and policy. At UVA, she is a Faculty Co-Lead in the Digital Technology for Democracy Lab at the Karsh Institute of Democracy, Affiliated Faculty with the Department of Women, Gender and Sexuality, and Faculty Affiliate with the Thriving Youth in a Digital Environment (TYDE) research initiative. She also convenes the Co-Opting AI series and serves as the editor of the Co-Opting AI book series at the University of California Press as well as the Technology Editor for Public Books. Mona’s book Predicted: How AI Is Restructuring Social Life is forthcoming in the Spring of 2026 with the University of California Press and explores how predictive systems are rewiring everyday experiences. Her growing research group Sloane Lab conducts empirical research on the implications of technology for the organization of society. Its focus lies on AI as a social phenomenon that intersects with wider cultural, economic, material, and political conditions. The lab spearheads social science leadership in applied work on responsible AI, public scholarship, and technology policy. More here: monasloane.org.
Emanuel Moss BIO:
Emanuel Moss recently completed a three year stint as Senior Research Scientist at Intel Labs and is also a Lecturer at the University of Virginia School of Data Science. His research is broadly concerned with the social dimensions of AI and computing, and multidisciplinary approaches to AI evaluation. He has published widely on accountability for computational systems, AI audit and assessment, and organizational approaches to AI adoption. His ongoing work addresses the use of AI in high-precision expert domains. Moss is a recipient of the Wenner Gren Fellowship, is a member of the NIST AI Safety Institute Consortium and the Computing Community Consortium Council, and frequently collaborates with the Sloane Lab at the University of Virginia.
Hosted by Jathan Sadowski
Venue: Building F, Level 4, Room 39, Monash Caulfiled campus
Event Details
- Date:
- 15 December 2025 at 11:00 am – 12:30 pm
Description
AI technologies are more widely deployed in a range of domains across society. While a wide range of evaluations and benchmarks have been developed to score how well AI systems perform relative to specific tasks and goals, it is also necessary to assess their effects in the real world, on individuals, communities and institutions. In this methods seminar, Mona Sloane and Emanuel Moss will discuss a range of methodological approaches to AI audit that center their role in social settings. Drawing on their training in the social sciences, Drs. Sloane and Moss will each discuss techniques for examining how the underlying assumptions made by AI systems about society are key to auditing their effectiveness in specific settings. They will also explore techniques for analyzing the process of AI production to reveal these underlying assumptions, and approaches to conducting this work in both academic and industry settings.
Mona Sloane BIO:
Mona Sloane, Ph.D., is an Assistant Professor of Data Science and Media Studies at the University of Virginia (UVA). As a sociologist, she studies the intersection of technology and society, specifically in the context of AI design, use, and policy. At UVA, she is a Faculty Co-Lead in the Digital Technology for Democracy Lab at the Karsh Institute of Democracy, Affiliated Faculty with the Department of Women, Gender and Sexuality, and Faculty Affiliate with the Thriving Youth in a Digital Environment (TYDE) research initiative. She also convenes the Co-Opting AI series and serves as the editor of the Co-Opting AI book series at the University of California Press as well as the Technology Editor for Public Books. Mona’s book Predicted: How AI Is Restructuring Social Life is forthcoming in the Spring of 2026 with the University of California Press and explores how predictive systems are rewiring everyday experiences. Her growing research group Sloane Lab conducts empirical research on the implications of technology for the organization of society. Its focus lies on AI as a social phenomenon that intersects with wider cultural, economic, material, and political conditions. The lab spearheads social science leadership in applied work on responsible AI, public scholarship, and technology policy. More here: monasloane.org.
Emanuel Moss BIO:
Emanuel Moss recently completed a three year stint as Senior Research Scientist at Intel Labs and is also a Lecturer at the University of Virginia School of Data Science. His research is broadly concerned with the social dimensions of AI and computing, and multidisciplinary approaches to AI evaluation. He has published widely on accountability for computational systems, AI audit and assessment, and organizational approaches to AI adoption. His ongoing work addresses the use of AI in high-precision expert domains. Moss is a recipient of the Wenner Gren Fellowship, is a member of the NIST AI Safety Institute Consortium and the Computing Community Consortium Council, and frequently collaborates with the Sloane Lab at the University of Virginia.
Hosted by Jathan Sadowski
Venue: Building F, Level 4, Room 39, Monash Caulfiled campus