79 citations · 118 across the 11 of their papers we have counts for
11 papers
Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers
Isar Nejadgholi, Svetlana Kiritchenko, Kathleen C. Fraser +1
Classifiers tend to learn a false causal relationship between an over-represented concept and a label, which can result in over-reliance on the concept and compromised classificati…
A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?
Kathleen C. Fraser, Svetlana Kiritchenko, Isar Nejadgholi
As text-to-image systems continue to grow in popularity with the general public, questions have arisen about bias and diversity in the generated images. Here, we investigate proper…
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…
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…
Extensive Error Analysis and a Learning-Based Evaluation of Medical Entity Recognition Systems to Approximate User Experience
Isar Nejadgholi, Kathleen C. Fraser, Berry De Bruijn
When comparing entities extracted by a medical entity recognition system with gold standard annotations over a test set, two types of mismatches might occur, label mismatch or span…
Extracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models
Kathleen C. Fraser, Isar Nejadgholi, Berry De Bruijn +3
Entity recognition is a critical first step to a number of clinical NLP applications, such as entity linking and relation extraction. We present the first attempt to apply state-of…