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.
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- 777 Glades Road
- Boca Raton, Florida
- United States 33431
- Building: EE
- Room Number: 405
- Starts 20 March 2026 05:07 PM UTC
- Ends 02 April 2026 05:09 PM UTC
- No Admission Charge
Speakers
Wenbin
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.
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. His research focuses on the theoretical foundations of machine learning, with an emphasis on responsible and socially beneficial AI. He has applied his work across multiple domains, including healthcare, digital forensics, energy, transportation, and finance. Dr. Zhang has received several honors, including the NSF CRII Award, the Leibniz Fellowship, the Distinguished AC for ECML PKDD'25, the Outstanding SPC Member Award for ECAI'25, the Distinguished SPC for IJCAI’23, and recognition in the Stanford/Elsevier Top 2% Scientists List. His research has also earned best paper awards/candidates at ECML PKDD’25, FAccT’23, ICDM’23, DAMI, and ICDM’21, and he was featured in the AAAI’24 New Faculty Highlights. He actively contributes to the AI and interdisciplinary communities through leadership roles on organizing committees and editorial boards of leading venues, including as Sponsorship Chair for AAAI’26, Volunteer Chair for WSDM’24, Associate Editor for ACM Computing Surveys, and Action Editor for Data Mining and Knowledge Discovery.