Supervised Algorithmic Fairness in Distribution Shifts
The 39th Annual AAAI Conference on Artificial Intelligence Tutorial
February 25th, 2025, The Pennsylvania Convention Center
Philadelphia, Pennsylvania, USA
Embedded Files
Abstract
Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains. In real-world applications, machine learning models are often trained on a specific dataset but deployed in environments where the data distribution may shift over time due to various factors. This shift can result in classifiers exhibiting poor generalization with low accuracy and making unfair predictions, which disproportionately impact certain groups identified by sensitive attributes, such as race and gender.
In this tutorial, we begin by providing a comprehensive summary of various types of distribution shifts organized into two main categories and briefly discuss the factors contributing to the emergence of unfair outcomes by supervised learning models under such shifts. Then, we conduct a thorough examination of existing methods for maintaining algorithmic fairness based on these shifts and highlight six commonly used approaches in the literature. Furthermore, we introduce frameworks for fairness-aware generalization under distribution shifts, including the latest research developments. Finally, we explore the intersections with related research fields, address significant challenges, and propose potential directions for future studies.
Materials
Tutorial Slides:
Survey Paper:
Minglai Shao, Dong Li, Chen Zhao, Xintao Wu, Yujie Lin, Qin Tian. Supervised Algorithmic Fairness in Distribution Shifts: A Survey. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI), 2024.
Paper Link: https://www.ijcai.org/proceedings/2024/0909.pdf
Schedule
Time: 4:15 pm - 6:00 pm (U.S. Eastern Time), February 25th, 2025
Location: Room 115C
Outline
Part I: Introduction to Supervised Algorithmic Fairness
Real-world cases and issues of algorithmic fairness
Definition and importance of algorithmic fairness
Overview of different types of fairness in machine learning
Introduction to distribution shifts and their impact on fairness
Part II: Learning Fairness under Distribution Shifts
General settings and formulation of learning fairness under distribution shifts
Introduction of various types of distribution shifts
Challenges posed by such shifts in maintaining fairness
Part III: Existing Approaches for Maintaining Algorithmic Fairness under Distribution Shifts
Introduction to methods for detecting and monitoring distribution shifts
Six commonly used strategies for adapting fairness to changing distributions
Introduction to past and latest related works within these six strategies
Introduction to benchmark datasets and evaluation metrics
Part IV: Future Directions, Related Research Fields, Conclusion
Challenges and potential future directions
Related research problems
Summary of key takeaways and insights from the tutorial
Part V: Q&A Session
Open floor for participants to ask questions and seek clarification
Discussion of specific challenges or scenarios raised by participants
Closing remarks and gratitude to participants for their engagement
Tutors' Bios
Dong Li is currently a Ph.D. student in the Department of Computer Science at Baylor University. Prior to this, he earned a Master’s degree from Tianjin University. His main research directions include graph mining, fairness-aware machine learning, domain generalization, etc. He has received multiple academic scholarships and national competition awards. His publications have been accepted by top international conferences such as IJCAI, CIKM, etc.
Chen Zhao is an Assistant Professor in the Department of Computer Science at Baylor University. Prior to joining Baylor, he was a senior R&D computer vision engineer at Kitware Inc. He earned his Ph.D. in Computer Science from the University of Texas at Dallas in 2021. His research focuses on machine learning, data mining, and artificial intelligence, particularly fairness-aware machine learning, novelty detection, and domain generalization. His publications have been accepted and published in premier conferences, including KDD, CVPR, IJCAI, AAAI, WWW, etc. Dr. Zhao serves as a PC member of top international conferences, such as KDD, NeurIPS, IJCAI, ICML, AAAI, ICLR, etc. He has organized and chaired multiple workshops on topics of Ethical AI, Uncertainty Quantification, Distribution Shifts, and Trustworthy AI for Healthcare at KDD (2022, 2023, 2024), AAAI (2023), and IEEE BigData (2024). He serves as the chair of the Challenge Cup of the IEEE Bigdata 2024 conference, the tutorial chair for the PAKDD 2025 and the ICDM 2025 conferences, and the workshop chair for the IEEE BigData 2025 conference.
Xintao Wu is a Professor and the Charles D. Morgan/Acxiom Endowed Graduate Research Chair in Database and leads the Social Awareness and Intelligent Learning (SAIL) Lab in the Electrical Engineering and Computer Science Department at the University of Arkansas. Dr. Wu is an associate editor or editorial board member of several journals and program committees as area chair, senior PC, and PC of top international conferences. He has served as the program co-chair of ACM EAI-KDD workshops (2022, 2023, 2024), the IEEE BigData'2020, ICLMA'2024, and PAKDD'2025. He also gave multiple tutorials on trustworthy AI at top international conferences, including ACM KDD, IEEE BigData, and SIAM SDM.
Past Tutorials:
Chen Zhao, Xintao Wu. Supervised Algorithmic Fairness in Distribution Shifts. IEEE International Conference on Big Data (BigData'24), Washington D.C., USA, December, 15th, 2024. [Website]
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