• Corpus ID: 239885503

Post-processing for Individual Fairness

@inproceedings{Petersen2021PostprocessingFI,
  title={Post-processing for Individual Fairness},
  author={Felix Petersen and Debarghya Mukherjee and Yuekai Sun and Mikhail Yurochkin},
  booktitle={NeurIPS},
  year={2021}
}
Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. The main appeal of postprocessing is that it avoids expensive retraining. In this work, we propose general post-processing algorithms for individual fairness (IF). We consider a setting where the learner only has access to the predictions of the original model and a similarity graph between individuals guiding the desired fairness constraints. We cast the IF… 
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Domain Adaptation meets Individual Fairness. And they get along
TLDR
It is shown that enforcing suitable notions of individual fairness (IF) can improve the out-of-distribution accuracy of ML models, and that it is possible to adapt representation alignment methods for domain adaptation to enforce (individual) fairness.
Gerrymandering Individual Fairness
TLDR
It will be proved that gerrymandering individual fairness in the context of predicting scores is possible and it will be argued that individual fairness provides a very weak notion of fairness for some choices of feature space and metric.
Gerrymandering Individual Fairness
TLDR
It will be proved that gerrymandering individual fairness in the context of predicting scores is possible and it will be argued that individual fairness provides a very weak notion of fairness for some choices of feature space and metric.

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