1) Current AI systems lack transparency and explainability, which reduces people's trust in applications like autonomous vehicles, financial management tools, and medical diagnoses. 2) For AI to be trustworthy, its decisions must be explained, fair, and free of bias. However, machine learning models are based on data patterns rather than formal logic, making explanations challenging. 3) Developing explainable AI requires techniques for understanding how models work, removing unfair biases, improving robustness, and making decisions transparent and traceable.