  With the recent rise of toxicity in online conversations on social media
platforms, using modern machine learning algorithms for toxic comment detection
has become a central focus of many online applications. Researchers and
companies have developed a variety of models to identify toxicity in online
conversations, reviews, or comments with mixed successes. However, many
existing approaches have learned to incorrectly associate non-toxic comments
that have certain trigger-words (e.g. gay, lesbian, black, muslim) as a
potential source of toxicity. In this paper, we evaluate several
state-of-the-art models with the specific focus of reducing model bias towards
these commonly-attacked identity groups. We propose a multi-task learning model
with an attention layer that jointly learns to predict the toxicity of a
comment as well as the identities present in the comments in order to reduce
this bias. We then compare our model to an array of shallow and deep-learning
models using metrics designed especially to test for unintended model bias
within these identity groups.
