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DTSTAMP:20260520T231005Z
DESCRIPTION:Click for Latest Location Information: http://dgiq-edw2026.data
 versity.net/sessionPop.cfm?confid=165&proposalid=16452\nThe adoption of age
 ntic AI demands a rethinking of foundational data and information quality p
 ractices. This panel brings together academic and industry speakers who hav
 e jointly advanced three complementary frameworks shaping the future of tru
 stworthy AI. First, Explainability-Driven Data Quality (EDDQ) uses machine-
 learning explainability weights to automate end-to-end data quality decisio
 ns—from cataloging and key-element identification to change managemen
 t and issue resolution—linking data quality directly to model behavio
 r. Second, Zero Trust Data Quality (ZTDQ) applies zero-trust principles to 
 dynamic, autonomous AI systems, asserting that data quality must always be 
 verified, never assumed, as systems and data evolve. Third, emerging &ldquo
 ;Semantics 2.0” approaches leverage new generalized semantic data eng
 ineering tools to enable scalable, context-aware meaning representation, in
 teroperability, and user-centric data governance across domains. Together, 
 these perspectives outline a unified, lead practice agenda that advances bo
 th data and information quality and semantics for the next era of intellige
 nt, adaptive systems.\n
DTSTART:20260505T154500
SUMMARY:Panel: Next-Generation Data Quality and Semantics for Agentic AI Sy
 stems
DTEND:20260505T162959
LOCATION: See Description
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