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Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey

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June 30, 2024
Published Date

Research Abstract & Technology Focus

This article provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias mitigation for ML classifiers. These methods can be distinguished based on their intervention procedure (i.e., pre-processing, in-processing, post-processing) and the technique they apply. We investigate how existing bias mitigation methods are evaluated in the literature. In particular, we consider datasets, metrics, and benchmarking. Based on the gathered insights (e.g., What is the most popular fairness metric? How many datasets are used for evaluating bias mitigation methods?), we hope to support practitioners in making informed choices when developing and evaluating new bias mitigation methods.
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Correlated Market Trend: Adaptive Learning

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Frequently Asked Questions (FAQ)

Curated market intelligence mapped to this research.

What is the core focus of the research titled 'Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey'?

This literature focuses on: This article provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias mitigation for ML classifiers. These methods can be distinguishe...

Are there open-source GitHub repositories related to Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey?

Yes, open-source projects like THU-MAIC/OpenMAIC (Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click) are actively building upon these concepts.

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Products like Superset are bringing this to market. Their focus is: Run an army of Claude Code, Codex, etc. on your machine.

What other academic literature is closely related to 'Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey'?

Yes, highly correlated activity was mapped. An entry titled 'Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey' discusses this: This article provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total ...

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Commercial Realization

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