BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260406T184045Z UID:4883CF64-2870-4322-93BF-DC0B35BF87FF DTSTART;TZID=America/New_York:20260402T110000 DTEND;TZID=America/New_York:20260403T120000 DESCRIPTION:As AI systems increasingly mediate decisions in high-stakes dom ains such as healthcare\, education\, and criminal justice\, ensuring fair ness is essential for preventing harm and maintaining public trust. When l eft unaddressed\, algorithmic bias can amplify existing societal inequalit ies\, automate discrimination at scale\, and erode the legitimacy of decis ions made by AI. Embedding fairness is therefore not optional\; it is a pr erequisite for building systems that are ethical\, accountable\, and align ed with human values and societal expectations. In this talk\, I will revi sit the foundations of AI fairness\, examine the challenges of operational izing fairness in real-world deployments\, and highlight some of our recen t 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 exampl es drawn from applications in healthcare\, housing\, and adolescent mental health.\n\nSpeaker Biography: Dr. Wenbin Zhang is an Assistant Professor in the Knight Foundation School of Computing and Information Sciences at F lorida International University and an Associate Member of the Te Ipu o Te Mahara Artificial Intelligence Institute.\n\nSpeaker(s): Wenbin \n\nRoom: 405\, Bldg: EE\, 777 Glades Road\, Boca Raton\, Florida\, United States\, 33431\, Virtual: https://events.vtools.ieee.org/m/549812 LOCATION:Room: 405\, Bldg: EE\, 777 Glades Road\, Boca Raton\, Florida\, Un ited States\, 33431\, Virtual: https://events.vtools.ieee.org/m/549812 ORGANIZER:sreeranjanirajendran@gmail.com SEQUENCE:10 SUMMARY:AI Fairness in Practice: Paradigm\, Challenges\, and Prospects for Societal Alignment URL;VALUE=URI:https://events.vtools.ieee.org/m/549812 X-ALT-DESC:Description: <br /><p><span data-olk-copy-source="MessageBody">A s AI systems increasingly mediate decisions in high-stakes domains such as healthcare\, education\, and criminal justice\, ensuring fairness is esse ntial for preventing harm and maintaining public trust. When left unaddres sed\, algorithmic bias can amplify existing societal inequalities\, automa te discrimination at scale\, and erode the legitimacy of decisions made by AI. Embedding fairness is therefore not optional\; it is a prerequisite f or building systems that are ethical\, accountable\, and aligned with huma n values and societal expectations. In this talk\, I will revisit the foun dations of AI fairness\, examine the challenges of operationalizing fairne ss in real-world deployments\, and highlight some of our recent work acros s areas such as data stream mining\, survival analysis\, graph learning\, and generative models. I will also outline the intersection of fairness wi th areas such as privacy\, security\, software systems\, GeoAI\, federated learning\, and large language models\, with motivating examples drawn fro m applications in healthcare\, housing\, and adolescent mental health.</sp an></p>\n<p>&nbsp\;</p>\n<p><span data-olk-copy-source="MessageBody">Speak er <strong>Biography:&nbsp\;</strong>Dr. Wenbin Zhang is an Assistant Prof essor in the Knight Foundation School of Computing and Information Science s at Florida International University and an Associate Member of the Te Ip u o Te Mahara Artificial Intelligence Institute.&nbsp\;</span></p> END:VEVENT END:VCALENDAR