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Fair Data Representation for Machine Learning at the Pareto Frontier.

Shizhou Xu1, Thomas Strohmer2

  • 1Department of Mathematics, University of California Davis, Davis, CA 95616-5270, USA.

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Summary
This summary is machine-generated.

This study introduces a novel pre-processing algorithm for fair machine learning data representation. It optimizes the trade-off between prediction error and statistical disparity, enhancing fairness in AI decision-making.

Keywords:
Wasserstein barycenterWasserstein geodesicsconditional expectation estimationequalized oddsstatistical parity

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Machine learning (ML) is increasingly used in decision-making, necessitating fair data processing.
  • Ensuring fairness in ML models is crucial to prevent biased outcomes.

Purpose of the Study:

  • To propose a pre-processing algorithm for fair data representation in ML.
  • To establish a method for optimizing the Pareto frontier between prediction error and statistical disparity.

Main Methods:

  • Utilizing optimal affine transport for Wasserstein barycenter characterization.
  • Employing pre-processing data deformation for fair representation.
  • Analyzing Wasserstein geodesics to characterize the Pareto frontier.

Main Results:

  • The pre-processing algorithm is compatible with various supervised learning methods and unseen data.
  • Fair representation limits the inference of sensitive information from remaining data.
  • Optimal affine maps demonstrate computational efficiency, even with high-dimensional data.

Conclusions:

  • The proposed pre-processing method effectively achieves fair data representation.
  • The approach offers a computationally efficient way to balance prediction accuracy and fairness.
  • This work contributes to the development of more equitable AI systems.