Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline

By: Sumon Biswas and Hridesh Rajan

Abstract

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.

ACM Reference

Biswas, S. and Rajan, H. 2021. Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline. ESEC/FSE: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Athens, Greece (2021), 981–993.

BibTeX Reference

@inproceedings{BiswasRajan2021,
  author = {Sumon Biswas and Hridesh Rajan},
  title = {Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline},
  booktitle = {ESEC/FSE: 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Athens, Greece},
  pages = {981--993},
  year = {2021},
  publisher = {{ACM}},
  editor = {Diomidis Spinellis and Georgios Gousios and Marsha Chechik and Massimiliano Di Penta},
  doi = {10.1145/3468264.3468536},
  abstract = {
  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.},
}