In Episode 35 of '100 Days of Data,' Jonas and Amy explore the essential role of explainable AI (XAI) in building trust and transparency in AI-driven systems. From real-world scenarios in finance and healthcare to technical strategies like intrinsic vs. post-hoc explainability, they dive into how organizations can make AI decisions understandable and accountable. The discussion covers key concepts—interpretability, transparency, and accountability—and highlights the risks of deploying 'black box' models without clear explanations. With vivid analogies and field-tested stories, this episode emphasizes why making AI explainable isn't just a technical goal—it's a business, ethical, and regulatory imperative.