activity
20192024
most citedExtracting UMLS Concepts from Medical Text Using General and Domain-Specific Deep Learning Models

15 citations · 24 across the 9 of their papers we have counts for

collaborators

11 papers

cs.CL2024

Adaptable Moral Stances of Large Language Models on Sexist Content: Implications for Society and Gender Discourse

Rongchen Guo, Isar Nejadgholi, Hillary Dawkins +2

This work provides an explanatory view of how LLMs can apply moral reasoning to both criticize and defend sexist language. We assessed eight large language models, all of which dem…

cs.CY20242 cited

Human-Centered AI Applications for Canada's Immigration Settlement Sector

Isar Nejadgholi, Maryam Molamohammadi, Kimiya Missaghi +1

While AI has been frequently applied in the context of immigration, most of these applications focus on selection and screening, which primarily serve to empower states and authori…

cs.CL2022

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…

cs.CL20221 cited

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…

cs.CL20221 cited

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…

cs.CY20215 cited

Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model

Kathleen C. Fraser, Isar Nejadgholi, Svetlana Kiritchenko

Stereotypical language expresses widely-held beliefs about different social categories. Many stereotypes are overtly negative, while others may appear positive on the surface, but…