  In recent years, many incidents have been reported where machine learning
models exhibited discrimination among people based on race, sex, age, etc.
Research has been conducted to measure and mitigate unfairness in machine
learning models. For a machine learning task, it is a common practice to build
a pipeline that includes an ordered set of data preprocessing stages followed
by a classifier. However, most of the research on fairness has considered a
single classifier based prediction task. What are the fairness impacts of the
preprocessing stages in machine learning pipeline? Furthermore, studies showed
that often the root cause of unfairness is ingrained in the data itself, rather
than the model. But no research has been conducted to measure the unfairness
caused by a specific transformation made in the data preprocessing stage. In
this paper, we introduced the causal method of fairness to reason about the
fairness impact of data preprocessing stages in ML pipeline. We leveraged
existing metrics to define the fairness measures of the stages. Then we
conducted a detailed fairness evaluation of the preprocessing stages in 37
pipelines collected from three different sources. Our results show that certain
data transformers are causing the model to exhibit unfairness. We identified a
number of fairness patterns in several categories of data transformers.
Finally, we showed how the local fairness of a preprocessing stage composes in
the global fairness of the pipeline. We used the fairness composition to choose
appropriate downstream transformer that mitigates unfairness in the machine
learning pipeline.
