director of institutional analytics george washington university washington DC, District of Columbia, United States
Session Abstract: As AI/ML technologies continue to reshape higher education, institutions face both opportunities and risks in data management and privacy. This presentation, based on my 2024 article in New Directions in Higher Education, explores the growing gap between the rapid adoption of AI/ML and the development of effective privacy policies. With incidents like data breaches and surveillance on the rise, the session highlights key privacy challenges, including data ownership, consent, and ethical use of predictive models. Using two case studies from universities and insights from Europe's AI Act, we discuss how to address these risks and present best practices for incorporating privacy in AI/ML systems. The goal is to empower IR professionals to lead in policy development, ensuring transparency, accountability, and safeguarding privacy in AI-driven environments.
Keywords: AI/ML technologies, data privacy, predictive analytics, data governance, ethical use
