AI Fairness in Practice: Paradigm, Challenges, and Prospects for Societal Alignment

As AI systems increasingly mediate decisions in high-stakes domains such as healthcare, education, and criminal justice, ensuring fairness is essential for preventing harm and maintaining public trust. When left unaddressed, algorithmic bias can amplify existing societal inequalities, automate discrimination at scale, and erode the legitimacy of decisions made by AI. Embedding fairness is therefore not optional; it is a prerequisite for building systems that are ethical, accountable, and aligned with human values and societal expectations. In this talk, I will revisit the foundations of AI fairness, examine the challenges of operationalizing fairness in real-world deployments, and highlight some of our recent work across areas such as data stream mining, survival analysis, graph learning, and generative models. I will also outline the intersection of fairness with areas such as privacy, security, software systems, GeoAI, federated learning, and large language models, with motivating examples drawn from applications in healthcare, housing, and adolescent mental health.
Speaker Biography: Dr. Wenbin Zhang is an Assistant Professor in the Knight Foundation School of Computing and Information Sciences at Florida International University and an Associate Member of the Te Ipu o Te Mahara Artificial Intelligence Institute.
Speaker(s): Wenbin
Room: 405, Bldg: EE, 777 Glades Road, Boca Raton, Florida, United States, 33431, Virtual: https://events.vtools.ieee.org/m/549812