Dear All,
You are cordially invited to the Critical Computing seminar tomorrow. Please find the details below.
Best Regards, Ishtiaque
== Syed Ishtiaque Ahmed Assistant Professor Department of Computer Science The University of Toronto Bahen Centre for Information Technology, Room 5262 Saint George Street, Toronto, ON M5S 2E4, Canada Ph: +1 647 220 3482 web: https://www.ishtiaque.net/ My Availability: Google Calendar Link https://calendar.google.com/calendar/embed?src=ishtiaque.uoft%40gmail.com&ctz=America%2FToronto ==
---------- Forwarded message --------- From: Priyank Chandra priyank.chandra@utoronto.ca Date: Tue, Nov 16, 2021 at 11:15 AM Subject: Critical Computing Seminar (Nov 17): "The Myopia of Model Centrism" To: ISCHOOL-FAC-REG-L@LISTSERV.UTORONTO.CA < ISCHOOL-FAC-REG-L@listserv.utoronto.ca> Cc: Ishtiaque Ahmed ishtiaque@cs.toronto.edu, Robert Soden < robert.soden@utoronto.ca>, Shion Guha shion.guha@utoronto.ca, Mohammad Rashidujjaman Rifat rifat@cs.toronto.edu, Christoph Becker < christoph.becker@utoronto.ca>, Adrian Petterson < a.petterson@mail.utoronto.ca>
Dear All,
We are happy to announce that we are restarting our Critical Computing seminar series. This a monthly online seminar where each month we invite scholars to discuss topics in critical computing on the third Wednesday of each month. The objective of the seminar is to create a broader understanding of computing from different ethical, social, and cultural perspectives. You will find more information about this seminar series and upcoming speakers by following this link: https://www.dgp.toronto.edu/critical-computing-seminar/index.html
This month (November), Nithya Sambasivan from Google Research will give a talk on “The Myopia of Model Centricism” on *Wednesday, Nov 17, 11am to 12:15pm EST*.
We invite you all to join the seminar. Please check the following link for more details about the seminar and how to register for the seminar: https://www.dgp.toronto.edu/critical-computing-seminar/Nithya%20Sambasivan.h...
A flyer is also attached to this email, and I have appended the seminar details at the bottom of this email. Please feel free to forward this invitation to anyone interested (within and outside UofT).
We look forward to seeing you all at the seminar.
Best Regards,
Priyank Chandra (On behalf of the Organizers)
Assistant Professor
Faculty of Information
University of Toronto
*Seminar Details: *
*Nithya Sambasivan*, Research Scientist, Google Research
*Time: November 17, 11 AM to 12:15 PM EST*
*Registration link:* here https://forms.gle/jdrnf7wYW55pd9Ro9
*Brief Bio:* Nithya Sambasivan is a Research Scientist at PAIR, Google Research and leads the human-computer interaction (HCI) group at the India lab. Her current research focuses on designing responsible AI systems by focusing on the humans of the AI/ML pipeline, specifically in the non-West. Her research is seminal to Google's products and strategy for emerging markets, while also winning numerous best paper awards and nominations at top-tier computing conferences. Nithya has a PhD. in Information and Computer Sciences from UC Irvine.
*About the talk:* AI models seek to intervene in increasingly higher stakes domains, such as cancer detection and microloan allocation. What is the view of the world that guides AI development in high risk areas, and how does this view regard the complexity of the real world? In this talk, I will present results from my multi-year inquiry into how fundamentals of AI systems---data, expertise, and fairness---are viewed in AI development. I pay particular attention to developer practices in AI systems intended for low-resource communities, especially in the Global South, where people are enrolled as labourers or untapped DAUs. Despite the inordinate role played by these fundamentals on model outcomes, data work is under-valued; domain experts are reduced to data-entry operators; and fairness and accountability assumptions do not scale past the West. Instead, model development is glamourised, and model performance is viewed as the indicator of success. The overt emphasis on models, at the cost of ignoring these fundamentals, leads to brittle and reductive interventions that ultimately displace functional and complex real-world systems in low-resource contexts. I put forth practical implications for AI research and practice to shift away from model centrism to enabling human ecosystems; in effect, building safer and more robust systems for all.
*Relevant papers:*
- Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., Aroyo, L. "Everyone wants to do the model work, not the data work": Data Cascades in High-stakes AI CHI 2021. https://dl.acm.org/doi/10.1145/3411764.3445518 - Sambasivan, N., Arnesen, E., Hutchinson, B., Doshi, T., Prabhakaran, V. Re-imagining Algorithmic Fairness in India and Beyond. FaccT 2021. https://dl.acm.org/doi/10.1145/3442188.3445896
*Site:* https://nithyasambasivan.com/