  In algorithmic toxicity detection pipelines, it is important to identify
which demographic group(s) are the subject of a post, a task commonly known as
\textit{target (group) detection}. While accurate detection is clearly
important, we further advocate a fairness objective: to provide equal
protection to all groups who may be targeted. To this end, we adopt
\textit{Accuracy Parity} (AP) -- balanced detection accuracy across groups --
as our fairness objective. However, in order to align model training with our
AP fairness objective, we require an equivalent loss function. Moreover, for
gradient-based models such as neural networks, this loss function needs to be
differentiable. Because no such loss function exists today for AP, we propose
\emph{Group Accuracy Parity} (GAP): the first differentiable loss function
having a one-on-one mapping to AP. We empirically show that GAP addresses
disparate impact on groups for target detection. Furthermore, because a single
post often targets multiple groups in practice, we also provide a mathematical
extension of GAP to larger multi-group settings, something typically requiring
heuristics in prior work. Our findings show that by optimizing AP, GAP better
mitigates bias in comparison with other commonly employed loss functions.
