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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.
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Translation Tutorial: Toward a Theory of Race for Fairness in Machine Learning
While computer scientists working on questions of fairness have diligently produced algorithmic approaches that seek to minimize disparate impacts across racial categories, the concept of race itself remains either unexamined, or constrained by definitions arising in legal and policy domains. While this may be appropriate for some applications, it is not altogether obvious that the FAT community benefits from refraining from developing a theory of race to guide its own practices. This tutorial will translate concepts from critical race theory and social scientific discourses into concepts legible to a community of machine learning practitioners through a dis- cussion of these theories and small-group activities that illustrate the salience of these theories for problems of fairness in machine learning.
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- Award ID(s):
- 1704425
- PAR ID:
- 10112012
- Date Published:
- Journal Name:
- Proceedings of the Conference on Fairness, Accountability, and Transparency
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
