Artificial intelligence and machine learning technologies have been used in high-stakes decision-making systems like lending decision, employment screening, and criminal justice sentencing. A new challenge arising with these AI systems is avoiding the unfairness introduced by the systems that lead to discriminatory decisions for protected classes.
This project aims to improve fairness-aware AI technologies by addressing the algorithmic limitations of existing methods. In particular, the research team led by Prof. Tianbao Yang will improve the training procedures of fairness-constrained AI models to make the model adaptive to different contexts, applicable to different applications, and subject to emerging fairness constraints. The success of this project will yield a transferable approach to improve fairness in various aspects of society via eliminating the disparate impacts and enhancing the fairness of AI systems in the hands of the decision makers. Together with AI practitioners, the investigators will integrate the techniques in this project into real-world systems, e.g., education analytics.
Below is abstracted from LSU News Release
"Artificial intelligence (AI) and machine learning (ML) technologies play an increasing role in our society today, including in high-stakes decision-making systems like lending decisions, employment screenings, and criminal justice sentencing.
However, one growing challenge with AI and ML systems is avoiding the unfairness they might introduce that can lead to discriminatory decisions. Finding a solution to that problem is the aim of a project by LSU Computer Science Associate Professor Mingxuan Sun and University of Iowa Computer Science Associate Professor Tianbao Yang and University of Iowa Associate Professor of Business Analytics Qihang Lin."
Funded thanks to FAI: Advancing Optimization for Threshold-Agnostic Fair AI Systems through NSF-Amazon Fair AI program. Lead PI (Yang); co-PIs (Sun and Lin)
Covered 4/5/22 by Corridor Business Journal: "Amazon, NSF give grant to UI researchers to make algorithms less discriminatory"