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Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning

AbstractMachine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. This posits the requirement of algorithmic fairness, which holds that automated decisions should be equitable with respect to protected features (e.g., gender, race). Training datasets can contain both class imbalance and protected feature bias. We postulate that, to be effective, both class and protected feature bias should be reduced—which allows for an increase in model accuracy and fairness. Our method, Fair OverSampling (FOS), uses SMOTE (Chawla in J Artif Intell Res 16:321–357, 2002) to reduce class imbalance and feature blurring to enhance group fairness. Because we view bias in imbalanced learning and algorithmic fairness differently, we do not attempt to balance classes and features; instead, we seek to de-bias features and balance the number of class instances. FOS restores numerical class balance through the creation of synthetic mino

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Damien Dablain · · 1 min read · 3 views

AbstractMachine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. This posits the requirement of algorithmic fairness, which holds that automated decisions should be equitable with respect to protected features (e.g., gender, race). Training datasets can contain both class imbalance and protected feature bias. We postulate that, to be effective, both class and protected feature bias should be reduced—which allows for an increase in model accuracy and fairness. Our method, Fair OverSampling (FOS), uses SMOTE (Chawla in J Artif Intell Res 16:321–357, 2002) to reduce class imbalance and feature blurring to enhance group fairness. Because we view bias in imbalanced learning and algorithmic fairness differently, we do not attempt to balance classes and features; instead, we seek to de-bias features and balance the number of class instances. FOS restores numerical class balance through the creation of synthetic minority class instances and causes a classifier to pay less attention to protected features. Therefore, it reduces bias for both classes and protected features. Additionally, we take a step toward bridging the gap between fairness and imbalanced learning with a new metric, Fair Utility, that measures model effectiveness with respect to accuracy and fairness. Our source code and data are publicly available at https://github.com/dd1github/Fair-Over-Sampling.

Executive Summary

The article 'Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning' addresses the critical issue of bias in machine learning models, particularly focusing on the intersection of algorithmic fairness and imbalanced learning. The authors propose a novel method called Fair OverSampling (FOS) that aims to reduce both class imbalance and protected feature bias, thereby enhancing model accuracy and fairness. The method leverages SMOTE for class imbalance and feature blurring to improve group fairness. Additionally, the article introduces a new metric, Fair Utility, to evaluate model effectiveness in terms of both accuracy and fairness. The study contributes to the ongoing discourse on ethical AI by providing practical tools and theoretical insights to mitigate bias in machine learning.

Key Points

  • ▸ Introduction of Fair OverSampling (FOS) method to address both class imbalance and protected feature bias.
  • ▸ Use of SMOTE for class imbalance and feature blurring for group fairness.
  • ▸ Proposal of a new metric, Fair Utility, to measure model effectiveness in terms of accuracy and fairness.
  • ▸ Emphasis on the importance of reducing bias in machine learning models to ensure equitable automated decisions.

Merits

Innovative Methodology

The FOS method is innovative as it addresses both class imbalance and protected feature bias simultaneously, which is a significant advancement in the field of algorithmic fairness.

Practical Application

The proposed method and metric are practical and can be readily applied to real-world machine learning models to improve fairness and accuracy.

Comprehensive Approach

The article takes a holistic view of bias in machine learning, bridging the gap between algorithmic fairness and imbalanced learning, which is crucial for developing fair and effective AI systems.

Demerits

Limited Scope of Evaluation

The study primarily focuses on the FOS method and Fair Utility metric, but does not extensively evaluate these tools across a wide range of datasets and scenarios, which could limit the generalizability of the findings.

Potential Overlap with Existing Methods

While the FOS method is novel, there may be overlaps with existing techniques for addressing bias and imbalance in machine learning, which could necessitate further differentiation and validation.

Complexity of Implementation

The implementation of the FOS method and the use of the Fair Utility metric may be complex and require significant computational resources, which could be a barrier for some practitioners.

Expert Commentary

The article 'Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning' presents a significant contribution to the field of ethical AI. The authors' innovative approach to addressing both class imbalance and protected feature bias through the Fair OverSampling (FOS) method is commendable. By leveraging SMOTE and feature blurring, the method aims to improve model accuracy and fairness, which are critical for ensuring equitable automated decisions. The introduction of the Fair Utility metric further enhances the practical applicability of the study, providing a comprehensive tool for evaluating model effectiveness. However, the study's limited scope of evaluation and potential complexity of implementation are notable limitations. Future research should focus on validating the FOS method and Fair Utility metric across diverse datasets and scenarios to ensure their generalizability. Additionally, further differentiation from existing techniques for addressing bias and imbalance would strengthen the study's contributions. Overall, the article provides valuable insights and tools for advancing the development of fair and effective machine learning models, with significant implications for both practical applications and policy decisions.

Recommendations

  • ✓ Conduct extensive evaluations of the FOS method and Fair Utility metric across a wide range of datasets and scenarios to validate their effectiveness and generalizability.
  • ✓ Explore potential overlaps with existing techniques for addressing bias and imbalance in machine learning to further differentiate and refine the FOS method and Fair Utility metric.

Sources

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