Feature Engineering Bookcamp

4 Bias and fairness: Modeling recidivism

This chapter covers

• Recognizing and mitigating bias in our data and ML models
• Quantifying fairness through various metrics
• Applying feature engineering techniques to remove bias from our model without sacrificing model performance

In our last chapter, we focused on building a feature engineering pipeline that would maximize our model’s performance on our dataset. This is generally the stated goal of an ML problem. Our goal in this chapter will be to not only monitor and measure model performance but also to keep track of how our model treats different groups of data because sometimes data are people.

In our case study today, data are people whose lives are on the line. Data are people who simply want ...