Critical AI@Rutgers, the initiative through which the new Critical AI Journal has been conceived, is currently partnering with the Australian National University for two exciting AY2021-2022 workshops. For information, click here!
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Visiting Arts Professor, NYU Tisch
Mimi Onuoha is a Nigerian-American artist creating work about a world made to fit the form of data. By foregrounding absence and removal, her multimedia practice uses print, code, installation and video to make sense of the power dynamics that result in disenfranchised communities’ different relationships to systems that are digital, cultural, historical, and ecological. Onuoha has spoken and exhibited internationally and has been in in residence at Studio XX (Canada), Data & Society Research Institute (USA), the Royal College of Art (UK), Eyebeam Center for Arts & Technology (USA), and Arthouse Foundation (Nigeria, upcoming). She lives and works in Brooklyn.
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March 5, 2021, 12pm EST
Thinkpiece Panel II
(An interdisciplinary conversation with three leading AI specialists.)
Associate Professor of Science, Technology, and Society at MIT. Cultural anthropologist, sociologist, and Mellon Postdoctoral Fellow at Dartmouth College
Title: “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.
Applied Mathematics and Philosophy, Harvard; Berkman Klein Center for Internet & Society
Title: “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.
Associate Professor of Information Studies and African American Studies at UCLA, Author of Algorithms of Oppression: How Search Engines Reinforce Racism
Title: “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.
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February 26, 2021, 12pm EST
Technology and Human Rights Fellow, Harvard Kennedy School Carr Center for Human Rights Policy, Fellow, Harvard Berkman Klein Center for Internet & Society
Moderator: Mukti Mangharam, Associate Professor of English, Rutgers
Sabelo Mhlambi is a computer scientist and researcher whose work focuses on the ethical implications of technology in the developing world, particularly in Sub-Saharan Africa, along with the creation of tools to make Artificial Intelligence more accessible and inclusive to underrepresented communities. His research centers on examining the risks and opportunities of AI in the developing world, and in the use of indigenous ethical models as a framework for creating a more humane and equitable internet. His current technical projects include the creation of Natural Language Processing models for African languages, alternative design of web-platforms for decentralizing data and an open-source library for offline networks.
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February 19, 2021, 11am EST
Thinkpiece Panel I
(An interdisciplinary conversation with three leading AI specialists.)
Moderator: Ellen P. Goodman, Rutgers College of Law
Venable Professor of Law at U of Baltimore, director of the Saul Ewing Civil Advocacy Clinic
Title: “Poverty Lawgorithms: The Economic Injustices of Automated Decision-Making”
As a result of automated decision-making systems, low-income people can find themselves excluded from mainstream opportunities, such as jobs, education, and housing; targeted for predatory services and products; and surveilled by systems in their neighborhoods, workplaces, and schools. These dynamics impede people’s economic security and potential for social mobility, and yet the law provides scant recourse. Thus, we must consider how to challenge opaque and unfair algorithmic systems as part of an economic justice agenda.
Michele Gilman is the Venable Professor of Law at the University of Baltimore School of Law. Professor Gilman teaches in the Civil Advocacy Clinic, where she supervises students representing low-income individuals in a wide range of litigation and law reform matters. She also teaches evidence and federal administrative law. Professor Gilman writes extensively about privacy, poverty, and social welfare issues, and her articles have appeared in journals including the California Law Review, the Vanderbilt Law Review, and the Washington University Law Review. In 2019-2020, she was a Faculty Fellow at Data & Society, where she researched the intersection of privacy law, data-centric technologies, and low-income communities.
Professor in the School for the Future of Innovation in Society and School of Computing, Informatics and Decision Systems Engineering at Arizona State
Title: “Misdirected Dreams? Trusting in AI: the hopes, the needs, and the challenges.”
“Humans in the loop”, “humans out of the loop”, and even “humans on the loop”, technology is edging ever closer to interfacing with the human or even brazenly replacing the human function. As we seek dreams of automation through artificial intelligence, the question centers on whether we are engaged in a process of deep techno-utopian distraction, or we are in fact on the right path to addressing our critical global needs. What are the challenges? How will we ensure a sustainable future?
Katina Michael is a professor at Arizona State University, holding a joint appointment in the School for the Future of Innovation in Society and School of Computing, Informatics and Decisions Systems Engineering. She is also the director of the Society Policy Engineering Collective (SPEC) and the Founding Editor-in-Chief of the IEEE Transactions on Technology and Society. Katina is a senior member of the IEEE and a Public Interest Technology advocate who studies the social implications of technology.
Associate Professor of Business Ethics and Xerox Junior Chair, Carnegie Mellon University
Title: “Flawed Like Us and the Starry Moral Law: A Critical Perspective to Artificial Intelligence.”
AI is an imitation game. “What is a good AI system?” Is the same question as “What is a good human being?” In this talk, engaging with Ian McEwan’s Machine Like Me, I invite the audience to rethink what it means to be human in the age of AI.
Tae Wan Kim is Associate Professor of Business Ethics and Xerox Junior Faculty Chair at Carnegie Mellon’s Tepper School of Business. Kim is a faculty member of the Block Center for Technology and Society at Heinz College, and CyLab at Carnegie Mellon’s School of Computer Science. Prior to joining Tepper’s faculty in 2012, Kim did his PHD in the Department of Legal Studies and Business Ethics at The Wharton School, University of Pennsylvania. Kim is on the editorial boards of Business Ethics Quarterly, Journal of Business Ethics, and Business & Society Review.
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February 12, 2021, 12pm EST
Co-founder and co-director, AI Now Institute, Minderoo Research Professor at New York University, Founder of Google’s Open Research Group
Keynote: “AI and Social Control”
This talk examines the limits of AI technologies, and their insistence on enforcing normative categories that necessarily exclude “that which doesn’t fit.” It then goes on to review the political economy driving the AI industry, AI’s recent history and capacity for social control, and how movements for justice must go beyond corporate-sponsored “ethics” and a fascination with technical mechanisms and adopt more militant tactics that contend with concentrated power, and the capacity of AI to exacerbate inequality and facilitate minority rule.
Facilitator: David Pennock (Director, DIMACS)
Meredith Whittaker’s research and advocacy focuses on the social implications of artificial intelligence and the tech industry responsible for it. Prior to NYU, she worked at Google for over a decade, where she led product and engineering teams, and co-founded M-Lab, a globally distributed network measurement platform that now provides the world’s largest source of open data on internet performance. As a long-time tech worker, she helped lead labor organizing at Google, driven by the belief that worker power and collective action are necessary to ensure meaningful tech accountability in the context of concentrated industrial power. She has advised the White House, the FCC, the City of New York, the European Parliament, and many other governments and civil society organizations on artificial intelligence, internet policy, measurement, privacy, and security.