CIBC Analytics Day Recap: Understanding and Operationalizing Trustworthy AI
On May 29, CIBC hosted a fireside chat titled 'AI Research: Trustworthy AI & MLOps' as part of its internal Analytics Day series. The event featured Aditya Anne, Senior Data Scientist at CIBC; Deval Pandya, Director of AI Engineering at the Vector Institute; and Ozge Yeloglu, VP of Enterprise Advanced Analytics at CIBC, who moderated the discussion. Attended by 70 CIBC team members, the session explored the definition and importance of Trustworthy AI, emphasizing that it is essential for maintaining stakeholder confidence and driving innovation. The speakers highlighted that Trustworthy AI involves mitigating risks related to bias, privacy, safety, and explainability. They described it as a 'socio-technical' challenge, noting that ethical requirements and human values must be integrated with technical processes. Fairness was identified as the most difficult concept to address due to its complex social and technical dimensions. The discussion underscored that organizations must define their own standards for Trustworthy AI to ensure systems do not harm stakeholders, thereby preserving credibility among clients, regulators, and internal teams.
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CIBC Analytics Day Recap: Understanding and Operationalizing Trustworthy AI
On May 29, CIBC hosted a fireside chat titled 'AI Research: Trustworthy AI & MLOps' as part of its internal Analytics Day series. The event featured Aditya Anne, Senior Data Scientist at CIBC; Deval Pandya, Director of AI Engineering at the Vector Institute; and Ozge Yeloglu, VP of Enterprise Advanced Analytics at CIBC, who moderated the discussion. Attended by 70 CIBC team members, the session explored the definition and importance of Trustworthy AI, emphasizing that it is essential for maintaining stakeholder confidence and driving innovation. The speakers highlighted that Trustworthy AI involves mitigating risks related to bias, privacy, safety, and explainability. They described it as a 'socio-technical' challenge, noting that ethical requirements and human values must be integrated with technical processes. Fairness was identified as the most difficult concept to address due to its complex social and technical dimensions. The discussion underscored that organizations must define their own standards for Trustworthy AI to ensure systems do not harm stakeholders, thereby preserving credibility among clients, regulators, and internal teams.
Vector Institute for Artificial Intelligence