Artificial intelligence (AI) systems have become integral to the organization of social life. However, there is a growing acknowledgment that these systems can inflict harm. As a result, regulators are increasingly calling for new regulations that encompass a wide range of AI accountability mandates. Often, these mandates are interpreted solely from a technical standpoint, which can lead to significant blind spots. How can we integrate social science and technical work to establish more holistic AI accountability techniques? How does "the social" factor into this equation? What questions should we be asking, and what role can qualitative approaches play? This talk will take this cue and explore new ways of bridging social science, computer science, and data science to develop novel approaches for AI accountability.
Rhea, A., Markey, K. D’Arinzo, L., Schellmann, H., Sloane, M., Squires, P., Stoyanovich, J. (2022): ‘Resume Format, LinkedIn URLs and Other Unexpected Influences on AI Personality Prediction in Hiring: Results of an Audit’, 2022 ACM Conference on Artificial Intelligence, Ethics, and Society, July 2022, doi: https://doi.org/10.1145/
Sloane, M. (2022): ‘To make AI fair, here’s what we must learn to do’. In: Nature, Volume 605, Number 7908, May 2022, doi: http://dx.doi.org/10.
Sloane, M., Moss, E., Chowdhury, R. (2022): ‘A Silicon Valley Love Triangle: Hiring Algorithms, Pseudo-Science, and the Quest for Auditability’, In: Patterns (Cell Press), Volume 3, Number 2, February 2022, doi: https://doi.org/10.1016/
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. She also convenes the Co-Opting AI series, a public speaker series focused on all aspects of AI technology and its application, ranging from security to food, games, and more, and serves as the Technology Editor for Public Books.
Her current work includes the development of new methods for AI auditing and AI transparency, innovating AI procurement, AI in hiring and talent acquisition, AI participation and public education, new AI tools for investigative journalism, global AI policy and local governance innovation on AI, and a range of different AI topics.
At UVA, Mona runs Sloane Lab which conducts empirical research on the implications of technology for the organization of social life. 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. Mona currently is a Fellow with the NYU Institute for Public Knowledge and The GovLab, and is affiliated with the Tübingen AI Center in Germany where she recently completed a 3-year federally funded research project on the operationalization of ethics in German AI startups.
Mona holds a PhD from the London School of Economics and Political Science and has completed fellowships at the University of California at Berkeley, at the University of Cape Town, and at the Weizenbaum Institute Berlin. Before joining UVA, Mona was a Research Assistant Professor at NYU’s Tandon School of Engineering, a Senior Research Scientist at the NYU Center for Responsible AI, and the Founding Director of the *This Is Not A Drill* program, which develops public pedagogy on art, equity, technology, and the climate emergency.
Mona is a frequent public speaker and commentator, and has written for The Guardian, MIT Technology Review, The Hill, Nature, Frankfurter Allgemeine Zeitung, OneZero Medium, and other outlets. She tweets at @mona_sloane.