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An interdisciplinary conversation with three leading AI specialists

Dwaipayan Banerjee, Associate Professor of Science, Technology, and Society at MIT. Cultural anthropologist, sociologist, and Mellon Postdoctoral Fellow at Dartmouth College.

"Decolonizing Computing: An Aesthetic and Demonic Energy."

Dwaipayan Banerjee is an Associate Professor of Science, Technology, and Society (STS) at MIT. He earned his doctorate in cultural anthropology at NYU and has been a Mellon Postdoctoral Fellow at Dartmouth College. He also holds an M. Phil and an MA in sociology from the Delhi School of Economics. His research is guided by a central theme: how do various kinds of social inequity shape medical, scientific and technological practices? In turn, how do scientific and medical practice ease or sharpen such inequities? In doing so, Banerjee’s ongoing research pushes science and technology studies into the global south. He develops postcolonial and subaltern orientations in the scholarship on science, medicine and technology.

Optional Reading: "The Aesthetics of Decolonization"

 Lily Hu, Applied Mathematics and Philosophy, Harvard; Berkman Klein Center for Internet & Society

"What is 'objective' and what is 'political' about data and algorithms?"

Debate about whether predictions issued by data-based algorithm systems come out of a process that is "objective" or "political" set forth a dichotomy between the empirical and the normative that is false. I will focus, instead, on how theoretical, empirical, and political considerations interact in the creation and use of such systems.

Lily Hu is a PhD candidate in Applied Mathematics and Philosophy at Harvard University. She works in philosophy of (social) science and political and social philosophy. Her current project is on causal inference methodology in the social sciences and focuses on how various statistical frameworks treat and measure the “causal effect” of social categories such as race, and ultimately, how such methods are seen to back normative claims about racial discrimination and inequalities broadly. Previously, she has worked on topics in machine learning theory and algorithmic fairness.

Optional Reading: "What is 'Race' in Algorithmic Discrimination on the Basis of Race?"

Safiya Noble, Associate Professor of Information Studies and African American Studies at UCLA, Author of Algorithms of Oppression: How Search Engines Reinforce Racism

"New Paradigms of Justice: How Knowledge Curators Can Respond to the Information Crisis"

Data discrimination is a real social problem, exacerbated by the monopoly status and private interests of a small number of internet companies. This talk offers provocations for imagining and creating new paradigms of justice in the technology sector, helmed by information professionals like librarians, museum curators and knowledge managers.

Safiya Noble is an Associate Professor at UCLA in the Departments of Information Studies and African American Studies. She is the author of a best-selling book on racist and sexist algorithmic bias in commercial search engines, entitled Algorithms of Oppression: How Search Engines Reinforce Racism (NYU Press). Dr. Noble is the co-editor of two edited volumes: The Intersectional Internet: Race, Sex, Culture and Class Online and Emotions, Technology & Design. She currently serves as an Associate Editor for the Journal of Critical Library and Information Studies, and is the co-editor of the Commentary & Criticism section of the Journal of Feminist Media Studies. She is a member of several academic journal and advisory boards, including Taboo: The Journal of Culture and Education.

Optional Reading: "Algorithms of Oppression" 

Moderator: Britt Paris (School of Communications & Information)

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Critical AI is a new interdisciplinary initiative that brings together AI research and humanities thinking. Find complete details for the Winter 2021 series and  Follow @CriticalAI on Twitter.