We can measure data and model fairness at different points in the machine learning pipeline. In this chapter, we look at the pre-processing, in-processing, and post-processing categories of bias mitigation algorithms.
Most Bias Starts with Your Data
AIF360’s bias mitigation algorithms are categorized based on where in the machine learning pipeline they are deployed, as illustrated in Figure 2-1. As a general guideline, you can use its pre-processing algorithms if you can modify the training data. You can use in-processing algorithms if you can change the learning procedure for a machine learning model. If you need to treat the learned model as a black box and cannot modify the training data or learning algorithm, you will need to use the post-processing algorithms.
Figure 2-1. Where can you intervene in the pipeline?
Pre-Processing Algorithms
Pre-processing is the optimal time to mitigate bias given that most bias is intrinsic to the data. With pre-processing algorithms, you attempt to reduce bias by manipulating the training data before training the algorithm. Although this is conceptually simple, there are two key issues to consider. First, data can be biased in complex ways, so it is difficult for an algorithm to translate one dataset to a new dataset which is both accurate and unbiased. Second, there can be legal issues involved: ...
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