Summary

After reading this chapter, you should understand how bias can be detected visually and with metrics, both in data and models, then mitigated through preprocessing, in-processing, and post-processing methods. We also learned about causal inference by estimating heterogeneous treatment effects, making fair policy decisions with them, and testing their robustness. In the next chapter, we also discuss bias but learn how to tune models to meet several objectives, including fairness.