Machine Learning can transform transportation—improving safety, optimizing routes, and reducing delays—yet it also presents ethical concerns. From potential algorithmic bias to opaque decision-making processes, trust and fairness are at stake. In this talk,I will show how Explainable AI (XAI) can offer practical solutions to these ethical dilemmas. Instead of focusing on the technical underpinnings, we will discuss how transparency, accountability, and fairness can be enhanced in AI-driven transportation systems. Using a real-world example, I will demonstrate how XAI provides the groundwork for building ethical, trustworthy, and socially responsible AI solutions in public transportation systems.