BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20251102T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260307T235842Z UID:7055A0D6-7E74-4011-84AE-9A96C133FFE6 DTSTART;TZID=America/New_York:20260307T160000 DTEND;TZID=America/New_York:20260307T170000 DESCRIPTION:As Artificial Intelligence systems rapidly transition from pass ive predictive tools to fully autonomous decision-making agents\, ensuring transparency\, trust\, and accountability has become a critical priority. This event focuses on Explainability-Driven Autonomous AI Agents—intell igent systems capable of operating independently across multiple domains w hile providing interpretable and trustworthy decision insights.\n\nOrganiz ed under the broader vision of the Institute of Electrical and Electronics Engineers\, this session brings together researchers\, industry leaders\, and practitioners working at the intersection of autonomous systems\, exp lainable AI (XAI)\, edge intelligence\, and safety-critical applications.\ n\nThe event will explore:\n\n-\nFoundations of explainability in autonomo us AI agents\n\n-\nArchitectures for multi-domain intelligent systems\n\n- \nTrust\, robustness\, and verification in AI-driven autonomy\n\n-\nHuman- AI collaboration and interpretable decision pipelines\n\n-\nApplications a cross healthcare\, smart infrastructure\, defense\, consumer electronics\, and distributed intelligence\n\n-\nEthical\, regulatory\, and societal im plications of autonomous AI\n\nSpecial emphasis will be placed on circuit- and system-level innovations that enable deployable\, real-time\, and res ource-efficient explainable AI frameworks. Through invited talks\, panel d iscussions\, and technical presentations\, participants will gain insights into emerging methodologies that bridge algorithmic intelligence with har dware-aware design and system reliability.\n\nThis event aims to foster in terdisciplinary collaboration within the IEEE community\, encouraging the development of next-generation autonomous AI agents that are not only inte lligent—but also transparent\, accountable\, and societally aligned.\n\n Speaker(s): Reshma\, \n\nVirtual: https://events.vtools.ieee.org/m/543717 LOCATION:Virtual: https://events.vtools.ieee.org/m/543717 ORGANIZER:mguduri@ltu.edu SEQUENCE:35 SUMMARY:Explainability-Driven Autonomous AI agents for multi-Domain Applica tions URL;VALUE=URI:https://events.vtools.ieee.org/m/543717 X-ALT-DESC:Description: <br /><p data-start="123" data-end="542">As Artific ial Intelligence systems rapidly transition from passive predictive tools to fully autonomous decision-making agents\, ensuring transparency\, trust \, and accountability has become a critical priority. This event focuses o n <em data-start="354" data-end="398">Explainability-Driven Autonomous AI Agents</em>&mdash\;intelligent systems capable of operating independently across multiple domains while providing interpretable and trustworthy deci sion insights.</p>\n<p data-start="544" data-end="828">Organized under the broader vision of the <span class="hover:entity-accent entity-underline i nline cursor-pointer align-baseline"><span class="whitespace-normal">Insti tute of Electrical and Electronics Engineers</span></span>\, this session brings together researchers\, industry leaders\, and practitioners working at the intersection of autonomous systems\, explainable AI (XAI)\, edge i ntelligence\, and safety-critical applications.</p>\n<p data-start="830" d ata-end="853">The event will explore:</p>\n<ul data-start="855" data-end=" 1279">\n<li data-start="855" data-end="912">\n<p data-start="857" data-end ="912">Foundations of explainability in autonomous AI agents</p>\n</li>\n< li data-start="913" data-end="967">\n<p data-start="915" data-end="967">Ar chitectures for multi-domain intelligent systems</p>\n</li>\n<li data-star t="968" data-end="1029">\n<p data-start="970" data-end="1029">Trust\, robu stness\, and verification in AI-driven autonomy</p>\n</li>\n<li data-start ="1030" data-end="1093">\n<p data-start="1032" data-end="1093">Human-AI co llaboration and interpretable decision pipelines</p>\n</li>\n<li data-star t="1094" data-end="1211">\n<p data-start="1096" data-end="1211">Applicatio ns across healthcare\, smart infrastructure\, defense\, consumer electroni cs\, and distributed intelligence</p>\n</li>\n<li data-start="1212" data-e nd="1279">\n<p data-start="1214" data-end="1279">Ethical\, regulatory\, an d societal implications of autonomous AI</p>\n</li>\n</ul>\n<p data-start= "1281" data-end="1657">Special emphasis will be placed on circuit- and sys tem-level innovations that enable deployable\, real-time\, and resource-ef ficient explainable AI frameworks. Through invited talks\, panel discussio ns\, and technical presentations\, participants will gain insights into em erging methodologies that bridge algorithmic intelligence with hardware-aw are design and system reliability.</p>\n<p data-start="1659" data-end="190 0" data-is-last-node="" data-is-only-node="">This event aims to foster int erdisciplinary collaboration within the IEEE community\, encouraging the d evelopment of next-generation autonomous AI agents that are not only intel ligent&mdash\;but also transparent\, accountable\, and societally aligned. </p> END:VEVENT END:VCALENDAR