CAREER: Advancing Fair Data Mining via New Robust and Explainable Algorithms and Human-Centered Approaches

Purdue University, West Lafayette IN

Investigators

Abstract

Predictive discrimination is widespread in artificial intelligence (AI) applications that affect human life. Automated decisions can replicate, exaggerate social inequities, and even implement and legitimize new forms of discrimination. The fairness and equity of data mining and machine learning models are becoming a growing concern in many communities, but the constraints of sensitive information in data and the complexity of models bring critical challenges to building fair AI frameworks. This project focuses on undertaking fundamental research activities to advance fairness in data mining and machine learning, and to enable efficient human-machine interaction in human-centered and wellness-focused real-world problems. This project will result in algorithms and software that facilitate broader research of fair AI technologies in high-stake application areas, such as improving healthcare diversity. The project's impacts are easing humans' effort to build, adopt, and interact with fair models. Furthermore, this project will encourage underrepresented students into cutting edge computational research and contribute to graduate and undergraduate education in multidisciplinary areas. The research objective of this project is to create fair and explainable AI and human-in-the-loop control paradigm: designing a family of fair, explainable, and robust data mining algorithms with high expressive ability, faithful explanations, and rigorous theoretical foundations. From a data equity perspective, the investigator will design effective algorithms to achieve fair predictions while being able to protect sensitive information. From an algorithm perspective, the investigator will design novel explainable and robust models with rigorous theoretical guarantees on generalization ability and Pareto efficiency. From a human-machine interaction perspective, the project will promote human-in-the-loop interventions and integrate human feedback to repair incorrect or biased predictions. This research effort combines rigorous theoretical analysis with emerging application problems, and is applicable to addressing the grand challenges that society faces in building responsible data science. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.