BEGIN:VCALENDAR VERSION:2.0 PRODID:-//MCAA//Marie Curie Alumni//EN CALSCALE:GREGORIAN METHOD:PUBLISH BEGIN:VEVENT UID:event-41856@www.mariecuriealumni.eu DTSTAMP:20261021T145108Z DTSTART:20260623T193000Z DTEND:20260623T203000Z SUMMARY:Explainable AI in Financial Regulation DESCRIPTION:This webinar that will present the preliminary findings of an o ngoing Marie Skłodowska-Curie Actions (MSCA) Industrial Doctoral project under DIGITAL.Drawing on interviews with central bankers worldwide\, Pat ricia Marcella Evite will show how regulatory authorities operationalize the requirements for Explainable Artificial Intelligence (XAI) in finance . The participants report AI use cases ranging from Large Language Models (LLMs) to predictive Machine Learning (ML). For LLMs used in policy draf ting and regulatory analysis\, explainability strictly means accurate sou rce-linking and verifiable citations to prevent hallucinations. Conversel y\, predictive ML models face adoption friction: senior policymakers ofte n reject complex technical visualizations (such as SHAP plots) in favor o f intuitive\, causal narratives.Registration:&\;nbsp\;https://mariecur iealumni-eu.zoom.us/meeting/register/EwI6fAacTiOOR8mSMlLX6A#/registration Format: 30-40 min presentation followed by Q&\;amp\;A.MSCA DIGITAL pro ject: https://www.digital-finance-msca.com/\nRead more: https://www.marie curiealumni.eu/webinars/explainable-ai-financial-regulation X-ALT-DESC;FMTTYPE=text/html:This webinar that w ill present the preliminary findings of an ongoing Marie Skłodowska-Curi e Actions (MSCA) Industrial Doctoral project under DIGITAL.Drawing on int erviews with central bankers worldwide, Patricia Marcella Evite will show how regulatory authorities operationalize the requirements for Explainab le Artificial Intelligence (XAI) in finance. The participants report AI u se cases ranging from Large Language Models (LLMs) to predictive Machine Learning (ML). For LLMs used in policy drafting and regulatory analysis, explainability strictly means accurate source-linking and verifiable cita tions to prevent hallucinations. Conversely, predictive ML models face ad option friction: senior policymakers often reject complex technical visua lizations (such as SHAP plots) in favor of intuitive, causal narratives.R egistration:&nbsp;https://mariecuriealumni-eu.zoom.us/meeting/reg ister/EwI6fAacTiOOR8mSMlLX6A#/registrationFormat: 30-40 min presentation followed by Q&amp;A.MSCA DIGITAL project: https://www.digital-fin ance-msca.com/
Read more URL:https://www.mariecuriealumni.eu/webinars/explainable-ai-financial-regulation END:VEVENT END:VCALENDAR