  Toxicity mitigation consists in rephrasing text in order to remove offensive
or harmful meaning. Neural natural language processing (NLP) models have been
widely used to target and mitigate textual toxicity. However, existing methods
fail to detoxify text while preserving the initial non-toxic meaning at the
same time. In this work, we propose to apply counterfactual generation methods
from the eXplainable AI (XAI) field to target and mitigate textual toxicity. In
particular, we perform text detoxification by applying local feature importance
and counterfactual generation methods to a toxicity classifier distinguishing
between toxic and non-toxic texts. We carry out text detoxification through
counterfactual generation on three datasets and compare our approach to three
competitors. Automatic and human evaluations show that recently developed NLP
counterfactual generators can mitigate toxicity accurately while better
preserving the meaning of the initial text as compared to classical
detoxification methods. Finally, we take a step back from using automated
detoxification tools, and discuss how to manage the polysemous nature of
toxicity and the risk of malicious use of detoxification tools. This work is
the first to bridge the gap between counterfactual generation and text
detoxification and paves the way towards more practical application of XAI
methods.
