  The fairness of Natural Language Processing (NLP) models has emerged as a
crucial concern. Information theory indicates that to achieve fairness, a model
should not be able to predict sensitive variables, such as gender, ethnicity,
and age. However, information related to these variables often appears
implicitly in language, posing a challenge in identifying and mitigating biases
effectively. To tackle this issue, we present a novel approach that operates at
the embedding level of an NLP model, independent of the specific architecture.
Our method leverages insights from recent advances in XAI techniques and
employs an embedding transformation to eliminate implicit information from a
selected variable. By directly manipulating the embeddings in the final layer,
our approach enables a seamless integration into existing models without
requiring significant modifications or retraining. In evaluation, we show that
the proposed post-hoc approach significantly reduces gender-related
associations in NLP models while preserving the overall performance and
functionality of the models. An implementation of our method is available:
https://github.com/fanny-jourdan/TaCo
