Due to the growing deployment of classification algorithms in various social contexts , developing methods that are fair with respect to protected attributes such as gender or race is an important problem . However, the information about protected attributes in datasets may be inaccurate due to either issues with data collection or when the protected attributes used are themselves predicted by algorithms . Such inaccuracies can prevent existing fair classification algorithms from achieving desired fairness guarantees. Motivated by this, we study fair classification problems when the protected attributes in the data may be ``noisy''. In particular, we consider a noise model where any protected type may be flipped to another with some fixed probability. We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes with provable guarantees . Empirically, we show that our framework can lead to near-perfect statistical parity with only a slight loss in accuracy for significant noise levels .
Due to the deployment of classification algorithms in a multitude of applications directly and indirectly affecting people and society , developing methods that are fair with respect to protected attributes such as gender or race is crucial . However, protected attributes in datasets may be inaccurate due to noise in the data collection or if the protected attributes are imputed either in whole or in part . Such inaccuracies can prevent existing fair classification algorithms from achieving their claimed fairness guarantees. Motivated by this, recent works have studied the fair classification problem in which a binary protected attribute is "noisy" (the protected type is flipped with a known fixed probability) by either suggesting optimization using tighter statistical or equalized odds constraints to counter the noise or by identifying conditions under which prior equalized odds post-processing algorithms can handle noisy attributes. We extend the study of noise-tolerant fair classification to a very general setting. Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with linear and linear-fractional class of fairness constraints, comes with probabilistic guarantees on accuracy and fairness, and can handle multiple, non-binary protected attributes . Empirically, we show that our framework can be used to attain either statistical rate or false positive rate fairness guarantees with a minimal loss in accuracy , even when the noise corruption is large in two real-world datasets. Prior existing noisy fair classification approaches, on the other hand, either do not always achieve the desired fairness levels or suffer a larger loss in accuracy for guaranteeing high fairness compared to our framework .
D
growing
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various social contexts
a multitude of applications directly and indirectly affecting people and society
R
an important problem
crucial
D
the information about
R
either issues with
noise in the
R
when
if
R
used are themselves predicted by algorithms
are imputed either in whole or in part
R
desired
their claimed
R
we study fair classification problems when the protected attributes in the data may be ``noisy''. In particular, we consider a noise model where any protected type may be flipped to another with some fixed probability. We propose a ``denoised'' fair optimization formulation that can incorporate very general fairness goals via a set of constraints, mitigates the effects of such noise perturbations , and comes
recent works have studied the fair classification problem in which a binary protected attribute is "noisy" (the protected type is flipped
R
provable guarantees
a known fixed probability) by either suggesting optimization using tighter statistical or equalized odds constraints to counter the noise or by identifying conditions under which prior equalized odds post-processing algorithms can handle noisy attributes. We extend the study of noise-tolerant fair classification to a very general setting. Our main contribution is an optimization framework for learning a fair classifier in the presence of noisy perturbations in the protected attributes that can be employed with linear and linear-fractional class of fairness constraints, comes with probabilistic guarantees on accuracy and fairness, and can handle multiple, non-binary protected attributes
R
lead to near-perfect statistical parity with only a slight
be used to attain either statistical rate or false positive rate fairness guarantees with a minimal
R
for significant noise levels
, even when the noise corruption is large in two real-world datasets. Prior existing noisy fair classification approaches, on the other hand, either do not always achieve the desired fairness levels or suffer a larger loss in accuracy for guaranteeing high fairness compared to our framework