• Corpus ID: 238531802

FairCal: Fairness Calibration for Face Verification

@inproceedings{Salvador2021FairCalFC,
  title={FairCal: Fairness Calibration for Face Verification},
  author={Tiago Salvador and Stephanie Cairns and Vikram S. Voleti and Noah Marshall and Adam M. Oberman},
  year={2021}
}
Despite being widely used, face recognition models suffer from bias: the probability of a false positive (incorrect face match) strongly depends on sensitive attributes such as the ethnicity of the face. As a result, these models can disproportionately and negatively impact minority groups, particularly when used by law enforcement. The majority of bias reduction methods have several drawbacks: they use an end-to-end retraining approach, may not be feasible due to privacy issues, and often… 

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