15 citations · 24 across the 7 of their papers we have counts for
8 papers
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…
Does Moral Code Have a Moral Code? Probing Delphi's Moral Philosophy
Kathleen C. Fraser, Svetlana Kiritchenko, Esma Balkir
In an effort to guarantee that machine learning model outputs conform with human moral values, recent work has begun exploring the possibility of explicitly training models to lear…
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…
Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors
Isar Nejadgholi, Kathleen C. Fraser, Svetlana Kiritchenko
Robustness of machine learning models on ever-changing real-world data is critical, especially for applications affecting human well-being such as content moderation. New kinds of…
Measuring Cognitive Status from Speech in a Smart Home Environment
Kathleen C. Fraser, Majid Komeili
The population is aging, and becoming more tech-savvy. The United Nations predicts that by 2050, one in six people in the world will be over age 65 (up from one in 11 in 2019), and…
Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective
Svetlana Kiritchenko, Isar Nejadgholi, Kathleen C. Fraser
The pervasiveness of abusive content on the internet can lead to severe psychological and physical harm. Significant effort in Natural Language Processing (NLP) research has been d…