Critical AI studies is an interdisciplinary ‘field in formation’ (Raley and Rhee, 2023), which aims to better understand, critique and provide alternatives to the current regimes of Artificial Intelligence (AI) development and implementations – from dataset production to model development, evaluation and deployment as well as social, political and institutional contexts that shape them.
While Critical AI Studies is an interdisciplinary field it has a strong footing in the critical methodologies of humanities, social sciences and arts. With an overarching aim to foreground how humanities scholars can engage critically with AI, this emerging field provides the illustrative backdrop in this new seminar series, which aims to facilitate and stimulate conversations around the field of Critical AI Studies and inspire future research.
The seminars are online, open to everyone and take place on a bi-monthly basis. For each seminar one or two prominent invited speaker(s) are invited to give a talk that engages theoretically or empirically with AI and address one or more of four overarching themes: (1) Ethics and Accountability, (2) Politics of Data (3) Politics of Machine Learning and (4) Methods on ML Research.
The seminar series is organised by Anna Schjøtt Hansen, Dieuwertje Luitse and Tobias Blanke who are part of the Critical AI Research Group. It is supported by the University of Amsterdam Research Priority Area’s Human(e) AI and Global Digital Cultures and the Amsterdam School for Cultural Analysis and hosted by Creative Amsterdam (CREATE).
You can find the individual events and how to sign up by clicking on the headlines below.
Invited talk by Lauren M. E. Goodlad (Rutgers University) and Rita Raley (University of California, Santa Barbara).
Lauren M. E. Goodlad is a distinguished Professor of English at Rutgers University. She is the chair of a new interdisciplinary initiative on Critical Artificial Intelligence and as Editor-in-Chief and Co-founder of the new interdisciplinary journal Critical AI. Rita Raley is Professor of English at UC Santa Barbara and part of working group on Critical Machine Learning Studies and have recently co-edited a special issue on ‘Critical AI: A Field in Formation’. As a result, both Lauren and Rita are highly influential voices in the emerging field of critical AI and in this first seminar they will provide an overview of field of Critical AI Studies and discuss where it might be heading in the future.
Invited talk by Mercedes Bunz (Kings College London).
Mercedes Bunz is a Professor of Digital Culture and Society and the Deputy Head of the Department of Digital Humanities at King’s College London. She is the co-founder of the Creative AI lab and have provided numerous papers that carefully analyses AI, latest ‘Error is No Exception: On the Alien Intelligence of Machine Learning’. In this seminar keynote she will discuss her concept ‘The Calculation of Meaning’, which provides a critical entry point to discuss the risky practice applying AI without rigorous lab testing.
Invited talk by Benjamin Jacobsen (University of York)
Benjamin Jacobsen is a lecturer in Sociology at University of York. In addition, Benjamin is an editorial board member on the journal The Sociological Review and a Visiting Fellow in the Algorithmic Societies Project at Durham University. His research broadly explores the power and politics of machine-learning algorithms in transforming culture and society. In this session, Jacobsen will critically explore ‘The Politics of Synthetic Data’ by shedding light on how the use of synthetically generated data for training machine-learning algorithms is changing the ways we envision these technologies, as well as how this emerging phenomenon is quickly transforming the possibilities of machine learning in contemporary society.
Invited talk by Milagros Miceli (Technische Universität Berlin, Weizenbaum-Institut) and Julian Posada (Yale University)
Milagros Miceli is a sociologist and computer scientist leading the Data, Algorithmic Systems, and Ethics research group at the Weizenbaum-Institut for the Networked Society in Berlin. Her research explores the production of ground-truth data for machine learning, with a specific focus on data labor, and the power relations that condition dataset production processes. In addition, Miceli is a researcher at Distributed AI Research Institute where she explores ways of engaging with communities of data workers in AI research and development.
Julian Posada is an Assistant Professor of American Studies at Yale University and a member of the Yale Law School’s Information Society Project and the Yale Institute for Foundations of Data Science. His research investigates the human labor for data production in the artificial intelligence industry, emphasizing the experiences of workers in Latin America who are employed by digital platforms to produce data for machine learning and validate algorithmic outputs.
In this seminar conversation, Miceli and Posada will join forces to discuss how current power imbalances and experiences in data work contribute to the (re)production of social inequalities by machine learning, and the importance of enhancing labor conditions for data workers in the process of improving data quality for machine learning to mitigate these issues.
Invited talk by Nick Seaver (Tufts University)
Nick Seaver is an Assistant Professor in the department of Anthropology at Tufts University and the Director of the program Science, Technology and Society. Generally his work is centred on the question of how people who make technology deal with cultural materials. He has provided seminal work on how to conduct ethnographic research of algorithms and co-authored the Critical Algorithm Studies reading list. In his keynote he will discuss his recent book ‘Computing Taste: Algorithms and the Makers of Music Recommendation’, which draws on several years of ethnographic research and interviews with US-based developers of algorithmic music recommender systems.