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@inproceedings{GBM23,
  author={Ghosh, Bishwamittra and Basu, Debabrota and Meel, Kuldeep S.},
  title={
    "How Biased are Your Features?": Computing Fairness Influence Functions with
    Global Sensitivity Analysis
  },
  abstract={
    Fairness in machine learning has attained significant focus due to the
    widespread
    application in high-stake decision-making tasks. Unregulated machine
    learning classifiers can
    exhibit bias towards certain demographic groups in data, thus the
    quantification and mitigation
    of classifier bias is a central concern in fairness in machine learning. In
    this paper, we aim
    to quantify the influence of different features in a dataset on the bias of
    a classifier. To do
    this, we introduce the Fairness Influence Function (FIF). This function
    breaks down bias into
    its components among individual features and the intersection of multiple
    features. The key idea
    is to represent existing group fairness metrics as the difference of the
    scaled conditional
    variances in the classifier's prediction and apply a decomposition of
    variance according to
    global sensitivity analysis. To estimate FIFs, we instantiate an algorithm
    FairXplainer that
    applies variance decomposition of classifier's prediction following local
    regression. Experiments
    demonstrate that FairXplainer captures FIFs of individual feature and
    intersectional features,
    provides a better approximation of bias based on FIFs, demonstrates higher
    correlation of FIFs
    with fairness interventions, and detects changes in bias due to fairness
    affirmative/punitive actions in the classifier.
  },
  year={2023},
  month=jun,
  booktitle=FAACT,
  bib2html_pubtype={Refereed Conference},
  bib2html_rescat={Formal Methods 4 ML},
  bib2html_dl_pdf={https://arxiv.org/pdf/2206.00667.pdf},
}