Join us for a dynamic two-day event that merges the hands-on coding experience of hackathons with cutting-edge research presentations. Discover the latest advancements in LLM research and its integration into the programming landscape. Learn about the current progress of LLMs and design the future of AI systems with principles of responsibility, inclusivity, and accessibility. The Hackathon and Symposium are part of the DSI Emergent Data Science Program, Toward a Fair and Inclusive Future of Work with ChatGPT is focused on the responsible development and ethical implementation of generative AI, specifically examining the impact of ChatGPT on diverse communities. By delving into the societal implications of using ChatGPT, the Program aims to equip researchers and users with a deeper understanding of the potential social impact and ethical considerations associated with these technologies. This program is co-sponsored by the Schwartz Reisman Institute for Technology and Society (SRI). October 4: Responsible LLM Hackathon Participants will work in teams to design, develop, and implement cutting-edge solutions using state-of-the-art models, competing for prestigious awards. The hackathon is open to all University of Toronto students, offering a platform to engage deeply with the latest advancements in AI and contribute to shaping a future where technology is both powerful and responsible. Don’t miss this chance to be part of a vibrant community of innovators. |
| October 4, 2024 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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8:00–9:00am |
Registration and Breakfast |
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9:00–9:30am |
Welcome and Opening Prof. Shurui Zhou, Prof. Ishtiaque Ahmed, Prof. Shion Guha, Prof. Anastasia Kuzminykh |
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9:30am–12:00pm |
Hackathon
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12:00 - 1:00 pm |
Lunch |
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1:00 - 6:00 pm |
Hackathon Participants have the opportunity to continue their work and submit all deliverables by 12:00 PM on Oct 5 |
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5:00 - 6:00 pm |
Dinner |
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October 5: Responsible Human-LLM Collaboration Symposium
Join us for a compelling session where leading researchers will present their latest advancements in the development, applications, and ethical considerations of LLMs. Our goal is to foster a community dedicated to establishing robust guidelines, policies, and safeguards for the ethical use of LLMs. Attendees will gain valuable insights into the future trajectory of LLMs and their societal impact.
This Symposium is the second installment in our series. To explore the discussions from our previous workshop, “Fairness – ChatGPT Workshop,” visit: Fairness – ChatGPT Workshop.
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9:00-9:20 am |
Breakfast |
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9:20-9:30 am |
Opening Remarks Prof. Shurui Zhou, Prof. Ishtiaque Ahmed, Prof. Shion Guha, Prof. Anastasia Kuzminykh |
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9:30-9:55 am |
Beyond Accuracy: Designing AI for complex relational interactions Prof. Matt Ratto, Faculty of Information, University of Toronto |
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9:55-10:20 am |
Data Driven Story Telling in the Age of AI Prof. Swati Mishra, Department of Computing and Software, McMaster University |
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10:20-10:45 am |
Trustworthy LLM with and for Software Engineering Prof. Gias Uddin, Lassonde School of Engineering, Electrical Engineering and Computer Science, York University |
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10:45-11:00 am |
Break |
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11:00-11:25 am |
AI Under the Microscope: From Classroom Performance to Societal Biases Prof. Yasir Zaki, New York University Abu Dhabi |
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11:25-11:50 am |
Towards Trustworthy AI: Can LLMs Achieve Causal Reasoning and Cooperation? Prof. Zhijing Jin, Department of Computer Science, Faculty of Arts & Science, University of Toronto |
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12:00-1:00 pm |
Lunch |
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1:00-1:25 pm |
Addressing Ethical Challenges in LLM-Driven Chart Reasoning Prof. Enamul Hoque Prince, Director, School of Information Technology, York University |
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1:25-2:25 pm |
Working Groups Each Working Group, led by at least one faculty member, to discuss: 1. Develop a slide deck that introduces the concept of responsible LLM usage, which can be utilized for teaching purposes after the workshop. It would be intriguing to observe the various perspectives on “responsibility” considered by different groups and the citations they incorporate. 2. Explore strategies for enhancing the curriculum to integrate LLMs across diverse fields (e.g., Computer Science and others). |
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2:30:-3:00 pm |
Working Group Summaries |
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3:10-3:30 pm |
Hackathon Presentations |
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3:30-3:40 pm |
Closing Remarks |
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Hackathon
October 4, 2024
9:00 a.m. to 5:00 p.m.
10th Floor, West Winter Garden,
108 College Street, Toronto
REGISTRATION FULL!
Symposium
October 5, 2024
9:00 a.m. to 3:45 p.m.
10th floor
700 University Avenue, Toronto
Zhijing Jin
Max Planck Institute & ETH
Incoming Assistant Professor, Department of Computer Science, Faculty of Arts & Science, University of Toronto
Dr. Jin’s research focuses on how AI systems can achieve causal reasoning, thus making judgments based on sound logic and less bias, which can be applied to Responsible AI and AI for Science. Her technical work focuses on causal inference methods for NLP, specifically to address robustness, interpretability, and causal/logical reasoning of LLMs. She is completing her PhD at the Max Planck Institute and ETH, and previously worked with Prof Mona Diab at Meta Responsible AI. Dr. Jin will join the University of Toronto in summer 2025. and Society (IDSS).
Swati Mishra
Assistant Professor
Computer Science and Software Engineering, McMaster University
Prof. Mishra’s research focuses on designing tools that improve ML system’s usability, reliability, and interactivity with stakeholders. Her lab currently focuses on applying cognitive modeling techniques to understand end-user behavior and leveraging it to build reliable AI systems for decision-making, sensemaking, and storytelling. She is interested in leveraging Machine Teaching, an inverse problem of Machine Learning, to improve teacher efficiency in applications for healthcare and computational journalism. Before joining McMaster, she received her Ph.D. in Information Science from Cornell University, where her research was funded by a multi-year Data Science Fellowship from Bloomberg AI. She also received an M.Sc. in Computer Science from Cornell University and an M.Sc. in Human-Computer Interaction from Indiana University. She has worked in the AI industry for 9 years, building and leading state-of-the-art AI products.
Enamul Hoque Prince
Associate Professor and Director
School of Information Technology, York University
Prof. Prince’s research addresses the challenges of the information overload problem using an interdisciplinary lens, combining information visualization and human-computer interaction with natural language processing. More specifically, he focuses on devising novel visual analytics techniques to explore large datasets such as online conversations as well as understanding the utility and potential trade-offs of such techniques from the real user’s perspectives. Dr Prince completed his Ph.D. in Computer Science from the University of British Columbia and was postdoctoral fellow in Computer Science at Stanford University. He is an area chair for the ACL Conference and serves as the program committee member for the IEEE Vis.
Matt Ratto
Professor and Associate Dean Research
Faculty of Information, University of Toronto
Prof. Ratto’s studies and practices ‘critical making’, work that combines humanities insights and engineering practices and has published extensively on this concept. He publishes across a wide range of disciplines including recent work on hope and interventional digital projects (CSCW 2023), generative AI and mental health (JMIR 2023), and additive manufacturing and prosthetics (CJPO 2020; JPO 2020).
Yasir Zaki
Assistant professor
Computer Science; Global Network, Courant Institute of Mathematical Sciences, NYU, Abu Dhabi
Prof. Zaki’s research interests include communication networks, wireless networks, cellular communication, congestion control protocols, and enhancing web in developing regions. He received his MSc and PhD degrees in Communication and Information Technology from the University of Bremen respectively . He has over 50 publications in the area of cellular communications and wireless network, his main research areas include future mobile communication, transport congestion control protocols, and improving the Internet/network performance in developing countries.
Gias Uddin
Assistant Professor
Department of Electrical Engineering and Computer Science, York University
Prof. Uddin’s research focuses on designing intelligent tools to assess AI trustworthiness and improve the productivity of developers and data engineers. His work has been published in top software engineering journals and conferences, including IEEE TSE, ICSE, and ASE, and has garnered international media attention from outlets like BBC News. His research is supported by several external grants, including IBM, NSERC, and Alberta Innovates.
