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DTSTAMP:20260520T223239Z
DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2026.data
 versity.net/sessionPop.cfm?confid=165&proposalid=16567\nPut a GPT on the pa
 nel—literally—and let it go first. Then watch two veteran pract
 itioners critique, calibrate, and challenge its decisions in real time. Thi
 s high-stakes, high-energy session runs through four governance flashpoints
 :\n\n	A regulated decision made under deadline pressure\n
 A fraud model paying the cost of false positives\n
 A synthetic-data shortcut that might backfire\n
 An automation pitch that skips risk review\n\nAfter each LLM response, the 
 experts weigh in—and the room votes: Ship / Ship with Guardrails / Bl
 ock. Together, we’ll reveal the proof points that matter most: end-to
 -end data lineage, error-cost math, model drift + rollback triggers, explai
 nability under audit, and where to place humans in the loop.\n\nYou’l
 l walk away with a practical rubric: when to trust the model, when to inter
 vene, and what evidence flips risk into readiness. Come for the debate, lea
 ve with a Monday-ready playbook to govern real-world AI at scale.\n
DTSTART:20260505T164500
SUMMARY:Panel: AI Governance Strategy: LLM vs. Human-in-the-Loop
DTEND:20260505T172959
LOCATION: See Description
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