The continual release of increasingly sophisticated AI models, trained on extensive datasets, presents ongoing challenges, including issues related to transparency, robustness, and fairness. This presentation introduces an approach to systematically identify and elucidate covert biases within these models. By employing a strategic data organization strategy, we enhance model transparency and ethical considerations, all without the requirement for supplementary data.
Dr. Feizi is an Associate Professor in the Computer Science Department at the University of Maryland, College Park. He holds a Ph.D. in EECS with a minor degree in mathematics from MIT. Prior to his appointment at UMD, he was a post-doctoral research scholar at Stanford University. Dr. Feizi’s research focuses on developing reliable and trustworthy Artificial Intelligence and Machine Learning. He has published over 100 peer-reviewed papers and given more than 50 invited talks. He has received multiple awards for his work including the ONR's Young Investigator Award, the NSF CAREER award, the ARO's Early Career program Award, two best paper awards, the Ernst Guillemin thesis award, a teaching award, and more than fifteen research awards from national agencies such as NSF and industry such as Meta, IBM, Amazon, Qualcomm and Capital One.