이 에피소드에 관해
In this episode, we dive into one of the most critical topics in modern AI—Explainable AI (XAI). As AI systems increasingly influence healthcare, finance, hiring, and even law enforcement, understanding how they make decisions isn’t just nice to have—it’s essential.
We break down XAI in simple terms, explore the difference between black-box and white-box models, and discuss why transparency and trust are becoming non-negotiable in AI adoption.
🔍 Topics covered:
Why explainability matters more than ever
Post-hoc tools like LIME and SHAP
The delicate balance between accuracy and interpretability
Real-world use cases from medical diagnostics to credit scoring and autonomous vehicles
Challenges in making deep models understandable
How XAI could shape the future of responsible AI
Whether you're technical or not, this conversation will help you grasp why explainable AI is key to building systems people can trust.
Tune in and join the conversation on the future of transparent AI.
