About

I am a third year Ph.D. student at the University of Maryland, College Park in the Department of Computer Science advised by Prof. Hal Daumé III. I am broadly interested in fairness, explainability/interpretability, and trust/trustworthiness, especially as they relate to NLP. More specifically, I am interested in fairness from a human-centered perspective. When people interact with a model exhibiting certain biases (or even a “debiased” model), what are the tangible effects/harms on the person? When people work together with a model to make decisions, how can we encourage outcomes that are more fair to the people effected by these decisions?

Previously, I did a combined BS/MS in Computer Science (with a secondary major in Japanese Studies and a concentration in AI) at Case Western Reserve University, advised by Prof. Soumya Ray.

Conference Papers

Which Examples Should be Multiply Annotated? Active Learning When Annotators May Disagree
Connor Baumler*, Anna Sotnikova*, Hal Daumé III
Finding of ACL 2023
[Paper] [Repo] [ACL Anthology]

Recognition of They/Them as Singular Personal Pronouns in Coreference Resolution
Connor Baumler, Rachel Rudinger
NAACL 2022
[Paper] [Repo] [ACL Anthology]

Hybrid Semantics for Goal-Directed Natural Language Generation
Connor Baumler, Soumya Ray
ACL 2022
[Paper] [ACL Anthology]

Preprints

The Impact of Explanations on Fairness in Human-AI Decision Making: Protected vs Proxy Features
Connor Baumler*, Navita Goyal*, Tin Nguyen, Hal Daumé III
(A preliminary version appeared at the CHI 2023 Workshop on Trust and Reliance in AI-Assisted Tasks (TRAIT))
[arXiv]