Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline

@article{Biswas2021FairPT,
  title={Fair preprocessing: towards understanding compositional fairness of data transformers in machine learning pipeline},
  author={Sumon Biswas and Hridesh Rajan},
  journal={Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  year={2021}
}
  • Sumon Biswas, Hridesh Rajan
  • Published 2 June 2021
  • Computer Science
  • Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
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… 

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