Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness

By: Sumon Biswas and Hridesh Rajan

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Abstract

Machine learning models are increasingly being used in important decision-making software such as approving bank loans, recommending criminal sentencing, hiring employees, and so on. It is important to ensure the fairness of these models so that no discrimination is made between different groups in a protected attribute (e.g., race, sex, age) while decision making. Algorithms have been developed to measure unfairness and mitigate them to a certain extent. In this paper, we have focused on the empirical evaluation of fairness and mitigations on real-world machine learning models. We have created a benchmark of 40 top-rated models from Kaggle used for 5 different tasks, and then using a comprehensive set of fairness metrics evaluated their fairness. Then, we have applied 7 mitigation techniques on these models and analyzed the fairness, mitigation results, and impacts on performance. We have found that some model optimization techniques result in inducing unfairness in the models. On the other hand, although there are some fairness control mechanisms in machine learning libraries, they are not documented. The mitigation algorithm also exhibit common patterns such as mitigation in the post-processing is often costly (in terms of performance) and mitigation in the pre-processing stage is preferred in most cases. We have also presented different trade-off choices of fairness mitigation decisions. Our study suggests future research directions to reduce the gap between theoretical fairness aware algorithms and the software engineering methods to leverage them in practice.

ACM Reference

Biswas, S. and Rajan, H. 2020. Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness. ESEC/FSE’2020: The 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (Nov. 2020).

BibTeX Reference

@inproceedings{biswas20machine,
  author = {Sumon Biswas and Hridesh Rajan},
  title = {Do the Machine Learning Models on a Crowd Sourced Platform Exhibit Bias? An Empirical Study on Model Fairness},
  booktitle = {ESEC/FSE'2020: The 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  location = {Sacramento, California, United States},
  month = {November 8-November 13, 2020},
  year = {2020},
  entrysubtype = {conference},
  abstract = {
    Machine learning models are increasingly being used in important
    decision-making software such as approving bank loans, recommending
    criminal sentencing, hiring employees, and so on. It is important to
    ensure the fairness of these models so that no discrimination is made
    between different groups in a protected attribute (e.g., race, sex, age)
    while decision making. Algorithms have been developed to measure
    unfairness and mitigate them to a certain extent. In this paper, we have
    focused on the empirical evaluation of fairness and mitigations on
    real-world machine learning models. We have created a benchmark of 40
    top-rated models from Kaggle used for 5 different tasks, and then using
    a comprehensive set of fairness metrics evaluated their fairness. Then,
    we have applied 7 mitigation techniques on these models and analyzed the
    fairness, mitigation results, and impacts on performance. We have found
    that some model optimization techniques result in inducing unfairness in
    the models. On the other hand, although there are some fairness control
    mechanisms in machine learning libraries, they are not documented. The
    mitigation algorithm also exhibit common patterns such as mitigation in
    the post-processing is often costly (in terms of performance) and
    mitigation in the pre-processing stage is preferred in most cases. We
    have also presented different trade-off choices of fairness mitigation
    decisions. Our study suggests future research directions to reduce the
    gap between theoretical fairness aware algorithms and the software
    engineering methods to leverage them in practice.
  }
}