Hyper-parameter Tuning for Fair Classification without Sensitive Attribute Access
ICLR 2023(2023)
摘要
Fair machine learning methods seek to train models that balance model
performance across demographic subgroups defined over sensitive attributes like
race and gender. Although sensitive attributes are typically assumed to be
known during training, they may not be available in practice due to privacy and
other logistical concerns. Recent work has sought to train fair models without
sensitive attributes on training data. However, these methods need extensive
hyper-parameter tuning to achieve good results, and hence assume that sensitive
attributes are known on validation data. However, this assumption too might not
be practical. Here, we propose Antigone, a framework to train fair classifiers
without access to sensitive attributes on either training or validation data.
Instead, we generate pseudo sensitive attributes on the validation data by
training a biased classifier and using the classifier's incorrectly (correctly)
labeled examples as proxies for minority (majority) groups. Since fairness
metrics like demographic parity, equal opportunity and subgroup accuracy can be
estimated to within a proportionality constant even with noisy sensitive
attribute information, we show theoretically and empirically that these proxy
labels can be used to maximize fairness under average accuracy constraints. Key
to our results is a principled approach to select the hyper-parameters of the
biased classifier in a completely unsupervised fashion (meaning without access
to ground truth sensitive attributes) that minimizes the gap between fairness
estimated using noisy versus ground-truth sensitive labels.
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关键词
fair classification,attribute access,sensitive
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