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Fair Facial Attribute Classification via Causal Graph-Based Attribute Translation.

Sunghun Kang1, Gwangsu Kim1, Chang D Yoo1

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Summary
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This study introduces a novel method for fair facial attribute classification, generating counterfactual images to reduce bias. The approach ensures accurate classification across diverse demographic groups by addressing complex attribute correlations.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Facial attribute classification exhibits racial and gender disparities.
  • Simple bias mitigation techniques are insufficient due to complex attribute correlations.

Purpose of the Study:

  • To achieve counterfactual fairness in facial attribute classification.
  • To develop a method that reduces performance disparities across sensitive attributes.

Main Methods:

  • Generated factual and counterfactual synthetic facial images using a causal graph-based attribute translation.
  • Employed an encoder-decoder framework to realistically alter facial attributes while preserving identity.
  • Trained an attribute classifier with counterfactual regularization for fair predictions.

Main Results:

  • Demonstrated the effectiveness of the proposed method on the CelebA dataset.
  • Showcased the interpretability of the causal graph-based approach.
  • Achieved reduced performance disparities in facial attribute classification.

Conclusions:

  • The proposed counterfactual fairness method effectively addresses bias in facial attribute classification.
  • Causal graph-based attribute translation offers a promising direction for fair AI.
  • The technique preserves identity while ensuring equitable performance.