In the previous chapter, we looked at AI governance, a few AI regulations and policies such as the EU AI Act, Joe Biden’s US executive order, and a couple of reports from the UN and UNESCO. We also introduced the SAFE-HAI framework which is critical to achieving responsibly built AI algorithms that adheres to the newly introduced AI policies. In this chapter, we’ll review “accuracy,” a key part of the SAFE-HAI framework and a crucial responsible AI principle. We’ll briefly discuss its definition, key questions to consider regarding accuracy and performance, ways to measure accuracy, a scoring rubric for accuracy, a few benchmarks from the research community, and ways to resolve poor accuracy of ML models and applications.