Exposing racial dialect bias in abusive language detection: can explainability play a role?

Manerba Mm, Morini V

Algorithmic bias  NLP  Fairness in ML  Discrimination  Interpretability  Bias discovery  ML Evaluation  Data awareness  Algorithmic auditing  Explainability  ML 

Biases can arise and be introduced during each phase of a supervised learning pipeline, eventually leading to harm. Within the task of automatic abusive language detection, this matter becomes particularly severe since unintended bias towards sensitive topics such as gender, sexual orientation, or ethnicity can harm underrepresented groups. The role of the datasets used to train these models is crucial to address these challenges. In this contribution, we investigate whether explainability methods can expose racial dialect bias attested within a popular dataset for abusive language detection. Through preliminary experiments, we found that pure explainability techniques cannot effectively uncover biases within the dataset under analysis: the rooted stereotypes are often more implicit and complex to retrieve.

Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE (PRINT), pp. 483-497. Grenoble, France, 19-23/09/2022

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