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

By: Sumon Biswas and Hridesh Rajan

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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’2021: The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Aug. 2021).

BibTeX Reference

@inproceedings{biswas21fair,
  author = {Sumon Biswas and Hridesh Rajan},
  title = {Fair Preprocessing: Towards Understanding Compositional Fairness of Data Transformers in Machine Learning Pipeline},
  booktitle = {ESEC/FSE'2021: The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  location = {Athens, Greece},
  month = {August 23-August 28, 2021},
  year = {2021},
  entrysubtype = {conference},
  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.
  }
}