Mitigating bias
We can mitigate bias at three different levels with methods that operate at these individual levels:
- Preprocessing: These are interventions to detect and remove bias from the training data before training the model. Methods that leverage pre-processing have the advantage that they tackle bias at the source. On the other hand, any undetected bias could still be amplified by the model.
- In-processing: These methods mitigate bias during the model training and are, therefore, highly dependent on the model and tend to not be model-agnostic like the pre-processing and post-processing methods. They also require hyperparameter tuning to calibrate fairness metrics.
- Post-processing: These methods mitigate bias during model inference. In Chapter 6, Anchors and Counterfactual Explanations, we touched on the subject of using the What-If tool to choose the right thresholds (see Figure 6.13 in that chapter), and we manually adjusted them to achieve parity with...
