35 citations · 43 across the 9 of their papers we have counts for
3 papers · 2 filters
Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information
Isar Nejadgholi, Esma Balkır, Kathleen C. Fraser +1
Previous works on the fairness of toxic language classifiers compare the output of models with different identity terms as input features but do not consider the impact of other im…
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models
Esma Balkir, Svetlana Kiritchenko, Isar Nejadgholi +1
Motivations for methods in explainable artificial intelligence (XAI) often include detecting, quantifying and mitigating bias, and contributing to making machine learning models fa…
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection
Esma Balkir, Isar Nejadgholi, Kathleen C. Fraser +1
We present a novel feature attribution method for explaining text classifiers, and analyze it in the context of hate speech detection. Although feature attribution models usually p…