Distinguished Webinar Speaker: Dr. Francesca Rossi
We are honored to feature Dr. Francesca Rossi, IBM Fellow and IBM Global Leader for Responsible AI and AI Governance at IBM Research — a leading voice in trustworthy AI, AI ethics, constraint reasoning, preferences, and responsible decision-making systems.
Dr. Francesca Rossi — IBM Fellow & IBM Global Leader for Responsible AI and AI Governance, IBM Research
Upcoming Talk
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Biography
Dr. Francesca Rossi is an IBM Fellow and the IBM Global Leader for Responsible AI and AI Governance, based at the T.J. Watson IBM Research Lab in New York. Before joining IBM, she was a professor of computer science at the University of Padova, Italy, for 20 years.
Her research interests span artificial intelligence, constraint reasoning, preferences, multi-agent systems, computational social choice, collective decision making, neuro-symbolic AI, and AI value alignment. She is equally focused on the ethical development and governance of AI systems, particularly for decision-support contexts.
She co-chairs IBM's AI Ethics Board, co-leads the Responsible AI working group of the Global Partnership on AI, and has been a member of the European Commission High Level Expert Group on AI. She has published over 220 scientific articles and co-authored multiple books and edited volumes.
Key Contributions
- Leadership in responsible AI, AI ethics, and AI governance at IBM Research.
- Research in constraint reasoning, preferences, multi-agent systems, and computational social choice.
- Pioneering work on ethical AI development and trustworthy decision-support systems.
- Co-chairs IBM's AI Ethics Board and the Global Partnership on AI Responsible AI working group.
- Former member, European Commission High Level Expert Group on AI.
Recognition
- IBM Fellow — IBM's highest technical distinction
- IBM Global Leader for Responsible AI and AI Governance
- Fellow of AAAI (Association for the Advancement of Artificial Intelligence)
- Fellow of EurAI (European Association for Artificial Intelligence)
- Past President of AAAI and IJCAI
- Editor-in-Chief, Journal of AI Research
Past Distinguished Webinars
Archive of previous ACM TRUST Distinguished Webinar speakers and talks
Prof. Leslie G. Valiant — 2010 A.M. Turing Award Laureate, Harvard University
Inaugural Talk
Enhanced and Efficient Reasoning in Large Language Models
In current Large Language Models the production of smoothly flowing prose is understandable in terms of the principles of machine learning. However, there is no comparable principled basis to justify trust in the content of the text produced. The widely recognized phenomenon of hallucinations is just one manifestation of this situation. While instances may be detected and corrected where alternative methods of verification exist, the pervasiveness of hallucinations where those precautions are not taken leads one to wonder about the trustworthiness of outputs in general. It appears to be conventional wisdom that addressing this issue by adding more principled reasoning to large language models is not computationally feasible and hence not an appropriate goal for technology.
Here we describe some new results that indicate, to the contrary, that one can enhance current language models with more principled reasoning, even while retaining much of the current software and hardware base. The method can be interpreted as providing more of a world model, where the individual objects and relations of the world are represented as first-class entities, and not treated indirectly and impressionistically as is current practice. On this model learned or programmed rules can be chained with certain soundness guarantees.
Biography
Prof. Leslie G. Valiant is a pioneering computer scientist whose work has had lasting impact on theoretical computer science, machine learning, and large-scale computation. He currently serves as the T. Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University.
He received the 2010 A.M. Turing Award for transformative contributions to the theory of computation, including the theory of probably approximately correct (PAC) learning, the complexity of enumeration and algebraic computation, and the theory of parallel and distributed computing.
His research introduced influential ideas that helped define modern computational learning theory and expanded the foundations of algorithmic and systems thinking. His scholarship continues to inspire researchers working across AI, theory, and trustworthy computing.
Key Contributions
- Introduced the PAC (Probably Approximately Correct) learning framework — foundational to computational learning theory.
- Made landmark contributions to counting complexity, including #P-completeness.
- Advanced parallel and distributed computation through the Bulk Synchronous Parallel (BSP) model.
- Produced influential research spanning theory of computation, learning, and intelligence.
Recognition
- 2010 A.M. Turing Award Laureate
- Knuth Prize recipient
- EATCS Award recipient
- Fellow, Royal Society
- Member, U.S. National Academy of Sciences