Research Interests:

Artificial Intelligence, Machine Learning, Computational Social Science and Natural Language Processing, especially in areas of Fairness & Bias in AI/ML/NLP, Human-Computer Interaction, Data Science for Social Good, and AI + X: Education and Healthcare with applications for people with disabilities (PWDs), criminal justice, and more. Specifically, focused on empirically modeling social factors such as Race, Gender & Language, and conceptualizing and mitigating socioethical implications (e.g., bias, stereotypes, and representational harms) in new and innovative technology. Selected Publications

Jamell Dacon, Haochen Liu and Jiliang Tang. Evaluating and Mitigating Inherent Linguistic Bias of African American English through Inference COLING'22: In Proceedings in the 29th International Conference on Computational Linguistics, 2022.

Jamell Dacon, Harry Shomer, Shaylynn Crum-Dacon, Jiliang Tang. Detecting Harmful Online Conversational Content towards LGBTQIA+ Individuals. NAACL'22: Queer in AI Workshop, 2022.

Jamell Dacon. Towards a Deep Multi-layered Dialectal Language Analysis: A Case Study of African-American English. NAACL'22: In Proceedings of the Second Workshop on Bridging Human-Computer Interaction and Natural Language Processing (HCI+NLP), 2022.

Jamell Dacon and Haochen Liu. Does Gender Matter in the News? Detecting and Examining Gender Bias in News Articles. TheWebConf'21: In Proceedings in the 30th International Web Conference, 2021.

Haochen Liu, Jamell Dacon, Wenqi Fan, Hui Liu, Zitao Liu and Jiliang Tang. Does Gender Matter? Towards Fairness in Dialogue Systems. COLING'20: In Proceedings in the 28th International Conference on Computational Linguistics, 2020.