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In the News: Jena Zangs on AI Safety Within Academic Institutions
AI Safety Imperatives in Higher Education
Chief Data & AI Officer Jena Zangs emphasizes that as generative AI tools become embedded in academic workflows, data safety and privacy must remain top priorities. With faculty and staff increasingly using AI platforms like ChatGPT and Microsoft Copilot, there’s potential for sensitive information to be inadvertently exposed unless institutions adopt safe handling practices.
Data Governance: Control vs Public Models
Zangs highlights a key distinction in AI use within academia: enterprise AI models vs free public tools. Enterprise systems offer structured data governance — including settings for storage, processing, and access — helping universities retain control. Public AI models may process uploaded data unpredictably, raising inadvertent risks to institutional records and student information.
Practical Recommendation: Summarize Before Uploading
A cornerstone of Zangs’s advice is to summarize or de-identify sensitive documents before uploading them into AI platforms. This reduces the likelihood that personally identifiable or confidential material enters external AI systems, a simple but effective step toward minimizing data exposure.
Embedding Human Judgment into AI Culture
Zangs frames AI as a tool that augments, not replaces, human expertise. The University of St. Thomas’s approach encourages faculty and staff to preserve the human voice in decision-making, ensuring AI insights are interpreted and applied through critical human judgment rather than automated conclusions.
Zangs frames AI as a tool that augments, not replaces, human expertise. The University of St. Thomas’s approach encourages faculty and staff to preserve the human voice in decision-making, ensuring AI insights are interpreted and applied through critical human judgment rather than automated conclusions.
Towards Broader Institutional AI Governance
While tactical practices like data summarization are important, Zangs’s comments reflect a broader push toward formal governance frameworks in academia. This includes procurement guidelines, secure enterprise contracts, training on ethical AI use, and auditability — helping universities manage risk while responsibly integrating AI into education, administration, and research.
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