In this podcast, we’ve covered AI Explainability, which is all about making AI decision-making understandable, ensuring transparency, trust, and fairness. As AI becomes more common, knowing how it arrives at conclusions is crucial for spotting errors, reducing biases, and meeting regulatory needs. We can achieve this through techniques like LIME and SHAP, which help in understanding the predictions of AI models. AI Explainability ultimately leads to better decisions, reduces risks, and builds confidence in AI systems. To learn more about AI Explainability, listen to the full podcast!