AAAI26 Tutorial
Human-centered AI: Challenges and Opportunities


Description:

From expert based AI systems in 1970s to self-supervised systems in 2020s, the development of AI has swung from complete to no reliance on human input. While self-supervised learning has enabled fast and scalable evaluation, and led to significant advances in AI capabilities, the resulting systems are increasingly disconnected from human values and expectations. Achieving alignment between AI and human goals is thus a central challenge that is rapidly gaining renewed interest and importance as AI technologies are proliferating with a direct impact on individuals and society. The adoption success or failure of these technologies critically hinges on their ability to complement humans at either the individual, organizational and/or societal level. This tutorial will provide an overview of the challenges in achieving human-centered AI, recent attempts at achieving human-AI alignment and the opportunities that lie ahead. Key focus will be on highlighting the role of concepts in social \& decision science that AI researchers should pay attention to as they work to develop human-centered AI. The target audience includes AI developers and practitioners, as well as social science researchers who are looking to engage and contribute to AI development.

Schedule:

2-3:30 pm Part I - Motivation, State-of-art in Human Alignment, Challenges
3:30-4 pm Break
4-5:30 pm Part II - Opportunities: Cognitive AI, Human-AI Complementarity, Testbeds
5:30-6 pm Q&A and Discussion

Slides

Presenters:
Aarti Singh is the FORE Systems Professor in the Machine Learning Department at Carnegie Mellon University and Director of the NSF National AI Institute for Societal Decision Making. Her research focuses on interactive algorithms that can guide learning and decision-making, with applications to enabling social and scientific discoveries. Her work is recognized by an NSF Career Award, a US Air Force Young Investigator Award, A. Nico Habermann Faculty Chair Award, Harold A. Peterson Best Dissertation Award, and multiple paper awards. She has served on the National Academy of Sciences (NAS) committee on Applied and Theoretical Statistics, NAS Board on Mathematical Sciences and Analytics, World Economic Forum expert network, lead expert on multiple NAS and ONR/NIST study committees, General Chair and Program Chair for the International Conference on Machine Learning (ICML) 2025 and 2020, respectively, Program Chair for Artificial Intelligence and Statistics (AISTATS) 2017 conference, and Action Editor for IEEE Transactions on Information Theory and Journal of Machine Learning Research.


Cleotilde (Coty) Gonzalez is a Research Professor at the Department of Social and Decision Sciences at Carnegie Mellon University. Her research work focuses on the study of human decision making in dynamic and complex environments. She is the founding director of the Dynamic Decision Making Laboratory where researchers conduct behavioral studies on dynamic decision-making using Decision Making Games, and create technologies and cognitive computational models to support decision-making and training. She is also the research co-Director of the NSF National AI institute for Societal Decision Making. In addition, Coty is affiliated with the CyLab Security and Privacy Institute, The Human-Computer Interaction Institute, and the Software and Societal Systems Department at Carnegie Mellon University. She is a 2024 AAAS Fellow, a lifetime fellow of the Cognitive Science Society and of the Human Factors and Ergonomics Society. She is a Senior Editor for Topics in Cognitive Science, a Consulting Editor for Decision, and Associate Editor for the System Dynamics Review. She is also a member of editorial boards in multiple other journals including Cognitive Science, Psychological Review, Perspectives on Psychological Science, and others.