One thing that's often overlooked is that bias doesn't end once the model is deployed. Real-world data distributions change over time, so even a well-balanced training dataset can drift and introduce unfair outcomes later. That's why ongoing monitoring, feedback loops, and periodic retraining are just as important as careful dataset curation. Building trustworthy AI is less about finding a "perfect" model and more about continuously validating the data it's learning from. Nice overview of a topic that's becoming increasingly important as AI systems move into production.