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DESCRIPTION:Click for Latest Location Information: http://dgiq2024east.data
 versity.net/sessionPop.cfm?confid=162&proposalid=15583\nRegis A. James, PhD
 , has spent well over a decade building scalable, end-to-end AI systems in 
 domains ranging from molecular diagnostics of genetic disease patients&nbsp
 ;to electroretinogram hallmark extraction from mouse models to company
 -wide employee pairing for institutional knowledge transfer (mentor matchin
 g). In doing so, the contexts in which he has collaborated have presented n
 umerous opportunities to develop and implement circumspect practices for ef
 fective due diligence towards reducing the risk and, therefore, maximizing 
 the explainability of ultimate decisions. To demonstrate these princip
 les, Regis will discuss how these considerations were applied in MAGNETRON 
 AI, a recent patent-pending autonomous AI system that he built with colleag
 ues, and highlight how these principles can be re-applied within other inst
 itutional contexts. \n\nTopics covered:\n\n
 Appropriately isolating sensitive data via governance controls\n
 Ensuring AI literacy among collaborators to maximize the predictability of 
 AI system behavior\n
 Maximizing data quality to enable comprehensiveness, reproducibility, and h
 igh-throughput processing within AI systems\n
 Considering the consequences of autonomy granted to AI systems during the a
 lgorithmic development process\n
 The necessity of incorporating explainability into AI systems\n
 Achieving risk-mitigation consensus with colleagues\n\n
DTSTART:20241211T120000
SUMMARY:Leveraging AI Literacy, Governance, and Data Quality Principles to 
 Minimize Risk
DTEND:20241211T124459
LOCATION: See Description
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