This podcast outlines a structured approach to recognizing, measuring, and mitigating bias in artificial intelligence systems. The NIST publication distinguishes between statistical bias (systematic error in model outputs) and societal bias (inequities embedded in data and social systems), emphasizing that AI systems can both reflect and amplify existing disparities. It proposes a lifecycle-based framework that spans data collection, model development, evaluation, deployment, and monitoring, encouraging organizations to adopt transparency, documentation, testing, and stakeholder engagement practices. The report highlights the importance of clear definitions, contextual understanding of fairness, and measurable performance criteria, while acknowledging trade-offs among fairness metrics. Ultimately, SP 1270 aims to support the development of techni ...