Artificial Intelligence, Decision Making, and Fairness: Risks and Opportunities

Abstract: With the proliferation of machine learning applications across industries such as transportation, human resources, finance, surveillance, and healthcare, concerns about the fairness and equity of artificial intelligence (AI) have intensified. Recent incidents highlighting biased AI predictions have underscored the urgent need to ensure fairness in these systems. In this presentation, we will first review different sources and types of biases that can affect AI applications and approaches to remedying the effects of biases. We will later introduce Multi-Objective Ensemble Learning for Fairness (MELF). This novel approach combines ensemble learning and multi-objective decision-making to train machine learning models that achieve a balance between predictive performance and fairness metrics. MELF is adaptable across various datasets and machine learning algorithms and can be integrated with other fairness-aware training techniques. Computational experiments with various algorithms demonstrate that MELF can enhance fairness without compromising predictive accuracy.

Short Biography: Dr. Abdullah Konak is a Distinguished Professor of Information Sciences and Technology at the Pennsylvania State University, Berks. Dr. Konak also teaches graduate courses in the Master of Science in Cybersecurity Analytics and Operations program at the College of Information Sciences and Technology, Penn State World Campus. Dr. Konak’s primary research focuses on modeling, analyzing, and optimizing complex systems using computational intelligence combined with probability, statistics, data sciences, and operations research. His research also involves active learning, entrepreneurship education, and the innovation mindset. Dr. Konak published numerous academic papers on a broad range of topics, including network design, system reliability, sustainability, cybersecurity, facilities design, green logistics, production management, and predictive analytics. Dr. Konak held visiting positions at Lehigh University and Cornell University, as well as at the Chinese University of Hong Kong, where he taught engineering innovation for over a decade. He has been a principal investigator in sponsored projects from the National Science Foundation, the National Security Agency, the U.S. Department of Labor, and Venture Well. He is a member of INFORMS and ASEE.