  Robustness of machine learning models on ever-changing real-world data is
critical, especially for applications affecting human well-being such as
content moderation. New kinds of abusive language continually emerge in online
discussions in response to current events (e.g., COVID-19), and the deployed
abuse detection systems should be updated regularly to remain accurate. In this
paper, we show that general abusive language classifiers tend to be fairly
reliable in detecting out-of-domain explicitly abusive utterances but fail to
detect new types of more subtle, implicit abuse. Next, we propose an
interpretability technique, based on the Testing Concept Activation Vector
(TCAV) method from computer vision, to quantify the sensitivity of a trained
model to the human-defined concepts of explicit and implicit abusive language,
and use that to explain the generalizability of the model on new data, in this
case, COVID-related anti-Asian hate speech. Extending this technique, we
introduce a novel metric, Degree of Explicitness, for a single instance and
show that the new metric is beneficial in suggesting out-of-domain unlabeled
examples to effectively enrich the training data with informative, implicitly
abusive texts.
