Example Concept: Organizations must conduct regular bias audits to identify and mitigate potential biases in AI models. This includes using fairness metrics to evaluate model outputs, employing explainability tools to understand decision-making processes, and documenting findings in model cards to ensure transparency and accountability. By following these practices, organizations can reduce the risk of biased outcomes and enhance trust in their AI systems.