IEEE Women in Engineering Webinar on Trustworthy AI Systems

Join IEEE Women in Engineering for an insightful webinar on building trustworthy AI systems—a critical challenge as AI moves from experimentation to real-world, high-stakes applications.
This session brings together two experienced speakers who will share practical perspectives on why many AI systems fail to gain trust and how we can design systems that are reliable, safe, and transparent by design.
Through real-world case studies and research-backed frameworks, you’ll explore how to build AI systems that not only perform well but also know their limits, communicate uncertainty, and prevent critical failures. Topics will include production challenges in AI deployment, reliability and explainability, and safety mechanisms for high-impact environments such as healthcare.
Whether you’re a student, engineer, or industry professional, this webinar will equip you with actionable insights, practical frameworks, and key questions to evaluate and build trustworthy AI systems.
Speaker(s): Anusha Kovi, Latha Iyer
Agenda:
Session 1 (45 minutes)
Talk: From Demo to Durable: How to Ship AI Systems People Actually Trust
Speaker: Anusha, Business Intelligence Engineer, Amazon
– Presentation (30 minutes)
– Why most AI pilots fail in production
– Real-world case study of an AI system failure
– A practical five-layer framework: policy, retrieval, reasoning, verification, and explanation
– Key questions and red flags when building trustworthy AI systems
– Q&A (15 minutes)
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Session 2 (45 minutes)
Talk: When AI Says “I Don’t Know”: Building Safety Alerts for High-Stakes Systems
Speaker: Latha Iyer, Applied AI Researcher
– Presentation (30 minutes)
– Risks of overconfident AI in critical environments
– Introduction to Adaptive Confidence Circuit Breaker (ACCB)
– Real-world validation using healthcare data
– Designing AI systems that recognize and communicate uncertainty
– Q&A (15 minutes)
Virtual: https://events.vtools.ieee.org/m/557290