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

By: Sumon Biswas and Hridesh Rajan

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 based on 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: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Virtual Event, USA (2020), 642–653.

BibTeX Reference

@inproceedings{BiswasRajan2020,
  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: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, Virtual Event, USA},
  pages = {642--653},
  year = {2020},
  publisher = {{ACM}},
  editor = {Prem Devanbu and Myra B. Cohen and Thomas Zimmermann},
  doi = {10.1145/3368089.3409704},
  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
  based on 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.},
}