Dr. Svetlana Kiritchenko is a senior research scientist at the National Research Council Canada. She received her Ph.D. in Computer Science from the University of Ottawa (Canada) and her M.Sc. in Applied Mathematics and Computer Science from Moscow State University (Russia). She primarily works in the areas of Computational Linguistics and Natural Language Processing (NLP). Her research interests include ethics and fairness in NLP, sentiment and emotion analysis, text classification, social media analysis, and medical informatics. She has also done work in hierarchical text classification and semi-supervised learning. As part of the NRC-Canada team, she has developed several text classification systems (for sentiment analysis and health-related social media mining) that ranked first in international shared task competitions. She has co-organized the 2019 Canada-United Kingdom Symposium on Ethics in AI, several graduate student symposia at Canadian conferences on AI, and several shared tasks at the international workshops on Semantic Evaluation (SemEval). She has served as reviewer for major NLP and AI conferences and journals and as area chair and senior area chair for NAACL-2022, EACL-2021, and *SEM-2018.

Email: svetlana <dot> kiritchenko <at> nrc-cnrc <dot> gc <dot> ca

News and Announcements:

Understanding and Countering Stereotypes: We present a computational approach to interpreting stereotypes in text through the Stereotype Content Model (SCM), a comprehensive causal theory from social psychology. Further, we explore various strategies to counter stereotypical beliefs with anti-stereotypes.
Kathleen C. Fraser, Svetlana Kiritchenko, Isar Nejadgholi, and Anna Kerkhof. (2023) What Makes a Good Counter-Stereotype? Evaluating Strategies for Automated Responses to Stereotypical Text. In Proceedings of the First Workshop on Social Influence in Conversations (SICon), Toronto, ON, Canada, July 2023. [pdf]

Kathleen C. Fraser, Svetlana Kiritchenko, and Isar Nejadgholi. (2022). Computational Modelling of Stereotype Content in Text. Frontiers in Artificial Intelligence, April, 2022. [paper]

Kathleen C. Fraser, Isar Nejadgholi, and Svetlana Kiritchenko (2021). Understanding and Countering Stereotypes: A Computational Approach to the Stereotype Content Model. In Proceedings of the Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021), August 2021. [pdf]

Kathleen C. Fraser, Svetlana Kiritchenko, and Isar Nejadgholi. (2022). Extracting Age-Related Stereotypes from Social Media Texts. In Proceedings of the Language Resources and Evaluation Conference (LREC-2022), Marseille, France, June 2022. [pdf][project webpage]
Detecting and Countering Aporophobia: Aporophobia, a social bias against the poor, is a common phenomenon online, yet so far has been overlooked in NLP research on toxic language. We demonstrate that aporophobic attitudes are indeed present in social media and argue that the existing NLP datasets and models are inadequate to effectively address this problem.
Svetlana Kiritchenko, Georgina Curto, Isar Nejadgholi, and Kathleen C. Fraser. (2023) Aporophobia: An Overlooked Type of Toxic Language Targeting the Poor. In Proceedings of the 7th Workshop on Online Abuse and Harms (WOAH), Toronto, ON, Canada, July 2023. Outstanding Paper Award [pdf]
TCAV for Text Classifiers: We propose an interpretability technique, based on the Testing Concept Activation Vector (TCAV) method, to quantify the sensitivity of a trained model to human-defined concepts, and use that to explain the generalizability and fairness of abusive language detection models:
Isar Nejadgholi, Svetlana Kiritchenko, Kathleen C. Fraser, and Esma Balkir. (2023) Concept-Based Explanations to Test for False Causal Relationships Learned by Abusive Language Classifiers. In Proceedings of the 7th Workshop on Online Abuse and Harms (WOAH), Toronto, ON, Canada, July 2023. [pdf]

Isar Nejadgholi, Esma Balkir, Kathleen C. Fraser, and Svetlana Kiritchenko. (2022) Towards Procedural Fairness: Uncovering Biases in How a Toxic Language Classifier Uses Sentiment Information. In Proceedings of the Workshop on Analyzing and Interpreting Neural Networks for NLP (BlackboxNLP), Abu Dhabi, United Arab Emirates, Dec. 2022. [pdf][code]

Isar Nejadgholi, Kathleen C. Fraser, and Svetlana Kiritchenko. (2022). Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland, May 2022. [pdf][code]
A survey of NLP research on automatic abuse detection with a focus on ethical challenges, organized around eight established ethical principles: privacy, accountability, safety and security, transparency and explainability, fairness and non-discrimination, human control of technology, professional responsibility, and promotion of human values:
Svetlana Kiritchenko, Isar Nejadgholi, and Kathleen C. Fraser. Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective. Journal of Artificial Intelligence Research, 71: 431-478, July 2021. [pdf]
Equity Evaluation Corpus: 8,640 English sentences carefully chosen to tease out biases towards certain races and genders. We use the dataset to examine 219 automatic sentiment analysis systems that took part in a recent shared task, SemEval-2018 Task 1 'Affect in Tweets'
Svetlana Kiritchenko and Saif M. Mohammad. Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems. In Proceedings of *Sem, New Orleans, LA, USA, June 2018 [pdf] [data]

Svetlana Kiritchenko, Ph.D.

National Research Council Canada

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