Keynote Speakers
AI ethics, safety, and governance in human-AI collaboration
Abstract: AI is going to bring huge benefits in terms of scientific progress, human wellbeing, economic value, and the possibility of finding solutions to major social and environmental problems. Supported by AI, we will be able to make more grounded decisions and to focus on the main values and goals of a decision process rather than on routine and repetitive tasks. However, such a powerful technology also raises some concerns, related for example to the black-box nature of some AI approaches, the possible discriminatory decisions that AI algorithms may recommend, the spread of misinformation, and the accountability and responsibility when an AI system is involved in an undesirable outcome. Also, since many successful AI techniques rely on huge amounts of data, it is important to know how data are handled by AI systems and by those who produce them. These concerns are among the obstacles that hold AI back or that cause worry for current AI users, adopters, and policy makers. Without answers to these questions, many will not trust AI, and therefore will not fully adopt it nor get its positive impact. In this talk I will present the main issues around AI ethics and safety, how they relate to the AI capabilities, some of the proposed technical and non-technical solutions, as well as practical actions and regulations being defined for AI development, deployment, and use.
Bio: Francesca Rossi is an IBM Fellow and the IBM AI Ethics Global Leader. She is based at the T.J. Watson IBM Research Lab, New York, USA, where she leads research projects and she co-chairs the IBM AI Ethics board. Her research interests focus on artificial intelligence, with special focus on constraint reasoning, preferences, multi-agent systems, computational social choice, neuro-symbolic AI, cognitive architectures, and value alignment. On these topics, she has published over 220 scientific articles in journals and conference proceedings, and as book chapters. She is a fellow of both the worldwide association of AI (AAAI) and the European one (EurAI). She has been president of IJCAI (International Joint Conference on AI) and is the current president of AAAI. She is a member of the board of the Partnership on AI and she co-chairs the Responsible AI working group of the Global Partnership on AI. She also co-chairs the OECD Expert Group on AI Futures and she has been a member of the European Commission High Level Expert Group on AI.
The Window to the Soul (and Mind): Providing Recommendations Based on Monitoring Visual, Vital and Neurological Signals of Users
Abstract: Traditional recommender systems have relied heavily on ratings and user-item interactions. However, advancements in wearable, sensor and other tracking technologies enable the collection of diverse physiological data—including heart rhythms (via EKG and pulse monitoring), brainwave activities (via electroencephalograms (EEG)), electrical activities in muscles (via electromyography (EMG)), skin conductance (via Electrodermal Activities (EDA) or Galvanic Skin Responses (GSR)), and the eye-tracking monitoring (via near-infrared eye-trackers, such as Tobii, or using webcams). This rich tapestry of signals can be leveraged in recommender systems by providing more suitable and nuanced recommendations.
In this talk I will explore how leveraging these vital, neurological and visual data streams can enhance recommendation quality, discuss successes and potential obstacles for developing such recommender systems and review the ways to overcome these challenges. Moreover, I will address the critical ethical and privacy considerations that arise from using sensitive biometric information. As a result, the attendees will gain some insights into the future of personalized recommendations driven by physiological analytics and the responsibilities that come with harnessing these powerful data sources.
Bio: Alexander Tuzhilin is Leonard N. Stern Professor in the Department of Technology, Operations and Statistics at the Stern School of Business, NYU. His research interests include AI, personalization, recommender systems and machine learning, and he published widely on these and related topics. Professor Tuzhilin has served on the organizing committees of numerous conferences, including as the Program and also the General Chair of the IEEE International Conference on Data Mining (ICDM), and as the Program Chair, the Conference Chair and the Chair of the Steering Committee of the ACM Conference on Recommender Systems (RecSys). He served on the editorial boards of several journals, including as the Editor-in-Chief of the ACM Transactions on Management Information Systems. His past doctoral students have joined leading universities, including the Wharton School at the University of Pennsylvania, Georgia Institute of Technology, University of Minnesota and other schools. He is a recipient of the INFORMS Information Systems Society Design Science Award and is a Distinguished Fellow of the ISS IFORMS Society. He received Ph.D. in Computer Science from the Courant Institute of Mathematical Sciences, NYU.
Lessons from the Trenches: Industry Perspectives on User Studies and Modeling at Scale
Abstract: Given that personalization drives 20-30% of revenue in leading tech firms, it is necessary to understand user behaviors and preferences at scale. This talk focuses on the critical interplay of user research and user modeling within large-scale industry applications to advance personalization and adaptive systems. It distinguishes between user research, centered on understanding the why behind user behavior, and user modeling, which aims to predict behavior at scale. Leading technology firms such as Google, Meta, Netflix, and Roblox provide examples of how qualitative insights can be integrated into the design and training of sophisticated and scaled user models. Significant challenges such as bias, privacy, and explainability, as well as emerging trends such as real-time adaptation, cross-platform modeling, longitudinal analysis, and the impact of generative AI, call for interdisciplinary collaboration between academia and industry. Ultimately, the aim is to inspire and drive future research and collaboration within the UMAP community, ensuring the development of ethical and scalable user modeling and adaptive systems.
Bio: Chen Zheng is Senior Director of Product and Head of Discovery at Roblox, responsible for Discovery products (such as Home, Search, User Lifecycle Management, LiveOps) that match billions of users with the best actions. Previously, she has worked on AI/ML-driven products such as search, recommender systems, content creation, ranking-as-a-service, data labeling & warehouse, enterprise/studio tools, community safety, and fraud across different industries at Netflix, Meta (Instagram and Facebook Marketplace), Google, and Wish, collectively for more than 15 years. She is also an educator on Product Management and AI, the Co-founder/Board Chair of Product Pub, a Guest Lecturer, and an Advisor/Investor for nonprofits and startups. She graduated from Stanford in 2009 with a Ph.D. in Physics (special focus on Astrophysics).