  Automatic detection of toxic language plays an essential role in protecting
social media users, especially minority groups, from verbal abuse. However,
biases toward some attributes, including gender, race, and dialect, exist in
most training datasets for toxicity detection. The biases make the learned
models unfair and can even exacerbate the marginalization of people.
Considering that current debiasing methods for general natural language
understanding tasks cannot effectively mitigate the biases in the toxicity
detectors, we propose to use invariant rationalization (InvRat), a
game-theoretic framework consisting of a rationale generator and a predictor,
to rule out the spurious correlation of certain syntactic patterns (e.g.,
identity mentions, dialect) to toxicity labels. We empirically show that our
method yields lower false positive rate in both lexical and dialectal
attributes than previous debiasing methods.
