Balancing Explainability and Privacy in AI

Join Zachary Hanif, VP of Data and AI at Twilio, as he discusses the fundamental differences between building AI systems in regulated financial services versus communication platforms, drawing from his experience at Capital One to implement rigorous model governance frameworks that reduce maintenance costs while accelerating development timelines. Hanif addresses the critical balance between explainable AI and high-performing black box models, emphasizing that organizations must identify where their use cases fall on the explainability spectrum rather than applying blanket requirements. He explores privacy-by-design principles for real-time AI systems, the challenge of moving from proof-of-concept to production (with 80% of AI pilots failing), and provides a practical framework for successful AI implementation that includes clear objective criteria, close collaboration between technical teams and domain experts, and properly tempered expectations for experimental development timelines.