Fair Text Classification with Wasserstein Independence
Thibaud Leteno Antoine Gourru Charlotte Laclau Rémi Emonet Christophe Gravier
Group fairness is a central research topic in text classification, where
reaching fair treatment between sensitive groups (e.g. women vs. men) remains
an open challenge. This paper presents a novel method for mitigating biases in
neural text classification, agnostic to the model architecture. Considering the
difficulty to distinguish fair from unfair information in a text encoder, we
take inspiration from adversarial training to induce Wasserstein independence
between representations learned to predict our target label and the ones
learned to predict some sensitive attribute. Our approach provides two
significant advantages. Firstly, it does not require annotations of sensitive
attributes in both testing and training data. This is more suitable for
real-life scenarios compared to existing methods that require annotations of
sensitive attributes at train time. Second, our approach exhibits a comparable
or better fairness-accuracy trade-off compared to existing methods.