Summary

In this chapter, we focused on the critical issue of bias and fairness in machine learning models. The potential negative consequences of deploying biased models, such as legal actions and fines, were emphasized. We covered various types of biases and identified stages in the deep learning life cycle where bias can emerge, including planning, data preparation, model development, and deployment.

Several metrics for detecting and evaluating bias and fairness were also introduced, including equal representation-based metrics, equal error-based metrics, distributional fairness metrics, and individual fairness metrics. This chapter provided recommendations on selecting the right metrics for specific use cases and highlighted the importance of balancing opposing views, such as WAE and WYSIWYG, when evaluating fairness. This chapter also discussed programmatic bias mitigation methods that can be applied during the pre-processing, in-processing, and post-processing stages of model...