The approach

You have decided to take a three-fold approach, as follows:

• Placing guardrails with feature engineering: Leveraging lessons learned in Chapter 7, as well as the domain knowledge we already have about priors and age, in particular, we will engineer some features.
• Tuning models for interpretability: Once the data is ready, we will tune many models with different class weighting and overfitting prevention techniques. These methods will ensure that the models not only generalize better but are easier to interpret.
• Implementing model constraints: Last but not least, we will implement monotonic and interaction constraints on the best models to make sure that they don't stray from trusted and fair interactions.

In the last two sections, we will make sure the models perform accurately and fairly. We will also compare recidivism risk distributions between the data and the model to ensure that they align.