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DESCRIPTION:Click for Latest Location Information: http://dgiq-aigov2026.da
 taversity.net/sessionPop.cfm?confid=166&proposalid=16857\nExplainable AI (X
 AI) involves explaining algorithmic decisions and the data driving those de
 cisions in a human-centric way and in non-technical terms to end-users. Exp
 lainability at scale is required for businesses to achieve successful adopt
 ion of AI in their critical systems and for promoting accountability, credi
 bility, and trust. The introduction of Agentic AI, where autonomous AI syst
 ems work with LLMs, further adds complexity and even more relevance for XAI
 , with multiple agent-to-agent interactions, along with human-AI interactio
 ns.\n\nThis presentation will focus on taking a human-centered approach to 
 data literacy and building trust in AI products and services for improving 
 their adoption. It will provide the audience with insights into XAI, a
 long with an understanding of requirements, tools, and techniques for imple
 menting XAI. Finally, it will provide an evaluation framework to enable suc
 cessful AI adoption. The following agenda topics will be covered: \n\n
 Requirements for building successful human-centric and semantics-based XAI,
  along with challenges\n	Ante hoc and post hoc XAI techniques\n
 XAI tools and their usage\n
 Evaluation of XAI, including human-in-the-loop validation.\n
 Examples in a few industries with a deep dive into one use case\n\n
DTSTART:20261117T154500
SUMMARY:Using Explainable AI (XAI) so Humans Can Understand Algorithmic Dec
 isions
DTEND:20261117T162959
LOCATION: See Description
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