  Toxic language is difficult to define, as it is not monolithic and has many
variations in perceptions of toxicity. This challenge of detecting toxic
language is increased by the highly contextual and subjectivity of its
interpretation, which can degrade the reliability of datasets and negatively
affect detection model performance. To fill this void, this paper introduces a
toxicity inspector framework that incorporates a human-in-the-loop pipeline
with the aim of enhancing the reliability of toxicity benchmark datasets by
centering the evaluator's values through an iterative feedback cycle. The
centerpiece of this framework is the iterative feedback process, which is
guided by two metric types (hard and soft) that provide evaluators and dataset
creators with insightful examination to balance the tradeoff between
performance gains and toxicity avoidance.

Computation and Language;Social and Information Networks