We hear so much about what artificial intelligence (AI) can do, but what about what AI cannot do? What can AI not do today, and what may it not ever be able to do?
The 2022–23 Schwartz Reisman Graduate Fellows present a special one-day workshop that will explore the limitations of AI through interdisciplinary perspectives. How can a framing of constraints and limitations guide us to reassess the role of algorithms and their application in different contexts? How can this framing be a useful heuristic device to engage more ethical and responsible design?
This special event consists of a morning and afternoon session. In the invitation-only morning session, a series of small group discussions concerning key themes will cultivate ideas for our afternoon panel discussions. In our first afternoon panel, we will ask how AI’s capabilities may never surprise us. In what ways will AI never evolve in a human direction? Considering this, our second conversation will then ask: in what particular domains might we want to limit AI? If so, how? Through our discussion of what AI might never be able to do, we will interrogate what characteristics and capacities we consider to be uniquely human.
Registration
Registration to this event is free. If you have registered for Absolutely Interdisciplinary 2023 you do not need to additionally register for this event.
Venue
Desautels Hall, Rotman School of Management, University of Toronto. Classroom 1065, second floor. 105 St George St., Toronto ON M5S 3E6.
This event will be held exclusively in person.
Schedule
9:30 AM – 1:00 PM | SRI Graduate Workshop (closed session, invitation only)
1:00 PM – 2:00 PM | Roundtable 1: How might AI surprise us? How might it never surprise us?
Speakers: Syed Ishtiaque Ahmed, Reem Ayad (moderator), Cendri Hutcherson
The current era has brought forth so many unforeseen events. How might AI help us navigate these unforeseen events that we don’t yet understand. How might AI surprise us in anticipating these unforeseen events that, until now, we’ve only trusted to humans? What sort of model would allow AI to explore the future in the same way we know it can explore the past (economics, politics, climate, pandemic, etc.)?
2:00 PM – 2:30 | Break
2:30 PM – 3:30 PM | Roundtable 2: Do we want to limit AI?
Speakers: Ganaele Langlois, Nicolas Papernot, Yuxing Zhang (moderator)
Scholars and practitioners are increasingly recognizing the potential harms that may arise from the widespread use of large language models. This has led to the use and development of AI technologies whose long-term impacts are yet to be fully understood. In order to act more responsibly and mitigate potential risks, may it be necessary to limit the rapid advancement of AI technologies until we have a clearer understanding of how to effectively manage these risks? How to adopt an interdisciplinary methodological approach and a more inclusive design framework when developing AI technologies? How to evaluate the effectiveness of existing AI regulations, and what are their limits?
3:30 PM – 4:30 PM | Reception
About the speakers
Syed Ishtiaque Ahmed is an assistant professor of computer science at the University of Toronto, and the director of the Third Space research group. He is also a graduate faculty member of the School of Environment, a faculty fellow at the Schwartz Reisman Institute for Technology and Society, and a senior fellow at Massey College. He co-organizes the monthly Critical Computing Seminar at U of T, and co-steers U of T's SDG initiative. Ahmed’s research focuses on the design challenges around strengthening the voices of marginalized communities around the world. He has conducted ethnography and built technologies with many underprivileged communities in Bangladesh, India, Pakistan, Iran, Iraq, Turkey, China, Canada, and the US. Ahmed received his PhD and Masters from Cornell University, and his Bachelor from BUET in Bangladesh. He is a recipient of the International Fulbright Science and Technology Fellowship, Fulbright Centennial Fellowship, and Schwartz Reisman Institute Fellowship, among others. His research has been funded by all three branches of Canadian tri-council research (NSERC, CIHR, SSHRC), as well as NSF, NIH, Google, Microsoft, Facebook, Intel, Samsung, the World Bank, UNICEF, and UNDP, among others.
Reem Ayad is a PhD student in the University of Toronto’s Department of Psychology and a graduate fellow at the Schwartz Reisman Institute. Her research focus is moral judgment in the context of human-machine interaction, with a particular focus on virtual AI systems. Her current research seeks to understand whether nurturing feelings of “closeness” with virtual assistants influences our moral judgment of them.
Cendri Hutcherson is the director of the Toronto Decision Neuroscience Laboratory and an associate professor of Psychology at the University of Toronto, with a cross-appointment to the Rotman School of Management. She received degrees in psychology from Harvard (BA) and Stanford (PhD), and worked as a post-doctoral scholar studying neuroeconomics at the California Institute of Technology. Her research program applies computational modeling to behavior, eye tracking, EEG, and fMRI data, with the goal of understanding how we make decisions and why we sometimes make decisions we later regret.
Ganaele Langlois is associate professor in communication studies at York University, and associate director of the Infoscape Centre for the Study of Social Media. Her research interests lie in media theory and critical theory, particularly with regards to the shaping of subjectivity and agency through and with media technologies. She is the author of Meaning in the Age of Social Media (Palgrave, 2014), and co-editor of Compromised Data? From Social Media to Big Data (Bloomsbury, 2015) and a series of special issues on the Canadian alt-right for the Canadian Journal of Communication (2021–22). Langlois is a co-principal investigator on the SSHRC-funded "Beyond Verification" and Mellon-funded "Data Fluencies" projects which explore mis- and dis-information. She is currently working on a research project about textile as communication, for which she received a SSHRC Insight Development Grant and Ontario Arts Council Grant. Her research has been published in New Media and Society, Culture Machine, Communication and Critical-Cultural Studies, Television and New Media, and Fibreculture.
Nicolas Papernot is an assistant professor in the Department of Electrical and Computer Engineering and the Department of Computer Science at the University of Toronto. He is also a faculty member at the Vector Institute where he holds a Canada CIFAR AI Chair, and a faculty affiliate at the Schwartz Reisman Institute for Technology and Society. Papernot’s research interests are at the intersection of security, privacy, and machine learning. He earned his PhD in computer science and engineering at Pennsylvania State University, working with Patrick McDaniel and supported by a Google PhD Fellowship. Upon graduating, he spent a year working at Google Brain.
Mohammad Rashidujjaman Rifat is a PhD candidate in the Department of Computer Science and a Schwartz Reisman Institute graduate fellow at the University of Toronto. He is a member of the Dynamics Graphics Project (DGP) lab, Third Space research group, and is supervised by Syed Ishtiaque Ahmed. In addition to Rifat’s PhD in computer science, he is doing a doctoral specialization in South Asian Studies from the Munk School of Global Affairs and Public Policy at the University of Toronto. Rifat’s research in human-computer interaction (HCI), computer-supported cooperative works and social computing (CSCW), and information and communication technologies for development (ICTD) is at the intersection of faith and computation. Through ethnographic, computational, and design research, he studies faith-based groups and institutions to explore how religious, spiritual, and traditional ethics and politics are excluded from computing technologies, and develop theories and design socio-technical systems where plural forms of values and ethics can coexist.
Yuxing (Yolanda) Zhang is a PhD candidate at the Faculty of Information, University of Toronto and a graduate fellow at the Schwartz Reisman Institute for Technology and Society. Her research interests include critical data studies, media theory, platform studies, media infrastructure, ethics of AI, precision agriculture, space media, and knowledge politics. Her works have been published in Media, Culture & Society, Roadsides, and Canadian Journal of Communication. Her teaching focuses on power and information systems, and ethical issues in algorithmic technologies.