Human-AI Complementarity Workshop: Dynamic Alignment
Academic Workshop - September 24-25, 2026
The NSF AI Institute for Societal Decision Making (NSF AI-SDM) sponsors the participation of selected speakers and students in an annual workshop of Human-AI Complementarity for Decision Making. Human-AI Complementarity, defined as the condition in which Humans + AI working together results in better decisions than humans or AI working alone, is a broad goal pursued in several projects of the NSF AI-SDM.
In 2026, we will focus on the emerging challenge of dynamic alignment to achieve human-AI complementarity: designing AI systems that not only emulate human preferences, but also coordinate effectively with humans over trajectories of interaction. We are particularly motivated by the observation that current alignment paradigms largely optimize for static preferences and single-turn evaluations, while many deployment challenges emerge only through longitudinal interaction. This workshop therefore aims to bridge perspectives from ML and the social sciences to better understand how to design, optimize, and evaluate human-AI systems operating in dynamic environments.
The goals of the workshop are:
- To deliver state of the art instruction on desirable ideas to achieve dynamic and complementary human-AI alignment
- To generate common knowledge about pressing research challenges
- To generate new shared ideas to address these challenges in future research
📌 Special Announcement for 2026 Applicants
We are pleased to announce that AI Magazine has accepted our proposal for an upcoming Special Issue centered on our workshop's core theme: "Dynamic Human-AI Complementarity."
NSF AI-SDM will be guest-editing this special issue and will author an opening perspective piece. Potential authors for the special issue articles will be exclusively invited from this year's workshop participants.
If you are conducting research in this space, we highly encourage you to by Friday, July 17, 2026, to ensure your work is considered for this unique publication opportunity.
Work from Participants:
Participants of the workshop will represent multiple disciplines: decision science, cognitive science, computer science, machine learning, and others. The workshop will bring together academics of various levels including faculty and students working in the areas of Human and AI complementarity and Decision Making at the individual, group and societal levels.
Every participant of the workshop will need to be actively engaged in specific activities:
- As tutorial Instructor that will deliver state-of-the-art educational material
- As students that actively participate in tutorials and present their own work on interactive poster sessions
- As presenters who briefly provide key contributions to a research topic
- As discussant and AIdea generator to actively engage in the specification of proposals
Keynote Speakers
Maximilian Nickel
Research Director, Meta (FAIR)
Maximilian Nickel is a Research Scientist at Facebook AI Research in New York. Before joining FAIR, he was a postdoctoral fellow at MIT where he was with the Laboratory for Computational and Statistical Learning and the Center for Brains, Minds and Machines. In 2013, he received his PhD with summa cum laude from the Ludwig Bavarian University Munich. From 2010 to 2013 he worked as a research assistant at Siemens Corporate Technology. His research centers around geometric methods for learning and reasoning with relational knowledge representations and their applications in artificial intelligence and network science.
Ganna Pogrebna
David Trimble Chair, Queen's Business School | Lead for Behavioural Data Science, The Alan Turing Institute
Professor Ganna Pogrebna is a pioneer in Behavioural Data Science and an internationally recognized scholar working at the intersection of Artificial Intelligence, behavioral science, and decision theory. She serves as the inaugural David Trimble Chair in Leadership and Organisational Transformation at Queen's Business School (Queen's University Belfast) and leads the Behavioural Data Science strand at The Alan Turing Institute (UK). Her research focuses on the social, ethical, and behavioral dimensions of emerging technologies, analyzing how individuals and groups coordinate, negotiate risk, and make decisions in dynamic, uncertainty-driven environments.
Jennifer Neville
Professor of Computer Science and Statistics, Purdue University | Partner Research Manager, Microsoft Research
Professor Neville's research focuses on data mining and machine learning techniques for relational data. In relational domains such as social network analysis, citation analysis, epidemiology, fraud detection, and web analytics, there is often limited information about any one entity in isolation, instead it is the connections among entities that are of crucial importance to pattern discovery. Relational data mining techniques move beyond the conventional analysis of entities in isolation to analyze networks of interconnected entities, exploiting the connections among entities to improve both descriptive and predictive models. Professor Neville's research interests lie in the development and analysis of relational learning algorithms and the application of those algorithms to real-world tasks.
Robin Murphy
Professor Emerita of Computer Science and Engineering, Texas A&M University
Robin Murphy is a Professor Emerita of Computer Science & Engineering Texas A&M and the co-founder of Center for Robot-Assisted Search and Rescue (CRASAR). She has over 200 publications on artificial intelligence, human-robot interaction, and robotics including the seminal textbook “Introduction to AI Robotics and Disaster Robotics, second edition, as well as Disaster Robotics, and Robotics Through Science Fiction: AI Explained Through Six Classic Robot Short Stories. Dr. Murphy is an AAAS, ACM, and IEEE fellow for her work in disaster robotics and human-robot interaction. Her participant-observer ethnographic research at over 30 disasters has identified gaps and opportunities for the use of AI and robotics in high pressure, cognitively demanding domains.
Venue
The workshop will take place September 24-25, 2026 at the located in the downtown area. The venue address is 301 Grant St Suite 411, Pittsburgh, PA 15219.
Travel Information
How to travel to and around Pittsburgh:
- By plane via . - Pittsburgh Regional Transit offers public transit service to and from Pittsburgh International Airport via the 28X Airport Flyer. The 28X route serves Pittsburgh International Airport, Downtown Pittsburgh, and Oakland seven days a week. Riders using cash to pay their transit fares must have exact change; credit cards are not accepted on vehicles. Credit cards are accepted at the ticket vending machine in Baggage Claim inside Door #2. The ticket vending machine allows riders to buy daily, 7-day or 30-day tickets, or add stored value onto a ConnectCard or ConnecTix.
- By train via . - Amtrak's Union Station is located at 1100 Liberty Avenue, Pittsburgh, PA 15222.
- By long distance bus via . - The Greyhound Bus Terminal is located five miles from campus in downtown Pittsburgh. The station terminal is located at the intersection of 11th Street and Liberty Avenue
- Local bus via . - The PRT network offers bus, light rail, and incline services in Allegheny County. Please see their website for schedules and rider information.
This project is supported by the U.S. National Science Foundation under award number 2229881. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the U.S. National Science Foundation. More information on the National AI Research Institutes can be found at .