Summary

In this chapter, we saw that the Fairlearn toolkit is a comprehensive open source tool for assessing and improving the fairness of AI systems built by data scientists and developers. We established that it is essential for good MLOps practices to be able to validate the performance of models, explain how they work, and monitor their performance continuously in order to address these issues. As AI regulations and laws emerge, there is a need for deeper model transparency. The chapter provided an overview of the importance of fairness in AI systems. We started by discussing the concept of fairness and the various types of fairness-related harms that could occur in AI systems, then introduced the Fairlearn toolkit to help data scientists and AI practitioners promote fairness in their models. The Fairlearn toolkit includes a range of fairness metrics that could be used to assess the level of fairness in a model, and a variety of tools and techniques for mitigating fairness-related...