  AI regulations are expected to prohibit machine learning models from using
sensitive attributes during training. However, the latest Natural Language
Processing (NLP) classifiers, which rely on deep learning, operate as black-box
systems, complicating the detection and remediation of such misuse. Traditional
bias mitigation methods in NLP aim for comparable performance across different
groups based on attributes like gender or race but fail to address the
underlying issue of reliance on protected attributes. To partly fix that, we
introduce NLPGuard, a framework for mitigating the reliance on protected
attributes in NLP classifiers. NLPGuard takes an unlabeled dataset, an existing
NLP classifier, and its training data as input, producing a modified training
dataset that significantly reduces dependence on protected attributes without
compromising accuracy. NLPGuard is applied to three classification tasks:
identifying toxic language, sentiment analysis, and occupation classification.
Our evaluation shows that current NLP classifiers heavily depend on protected
attributes, with up to $23\%$ of the most predictive words associated with
these attributes. However, NLPGuard effectively reduces this reliance by up to
$79\%$, while slightly improving accuracy.
