In the rapidly evolving field of Artificial Intelligence (AI), the importance of understanding model decisions is becoming increasingly vital. This talk explores why explanations are crucial for both technical and ethical reasons. We begin by examining the necessity of explainability in AI systems, particularly in mitigating unexpected model behavior, biases and addressing ethical concerns. The discussion then transitions into Explainable AI (XAI), highlighting the differences between interpretability and explainability, and showcasing methods for enhancing model transparency. A real-world examples will demonstrate how these concepts can be practically employed to improve model performance. The talk concludes with reflections on the challenges and future directions in XAI.

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