What happens when an algorithm makes a decision that affects your life—but can’t explain why?
In this episode of AI Ignited, host Elif Baser sits down with Ilkem Kaya to unpack Human-Centered and Explainable AI (XAI). From healthcare and finance to law and urban planning, they explore why transparency and interpretability are becoming non-negotiable in high-stakes systems. Drawing on insights from leading research centers like NIST, Stanford HAI, and MIT CSAIL, the conversation breaks down how explainability builds trust, exposes bias, and enables accountability—and where it can still fall short. This episode challenges the myth that accuracy is enough and asks a critical question: if AI shapes our futures, shouldn’t it be able to explain itself?
