no code implementations • ACL 2022 • Hillary Dawkins, Isar Nejadgholi
Particularly in the context of human-in-the-loop systems, high-quality low to mid-range certainty estimates are essential.
1 code implementation • 27 Mar 2024 • Hillary Dawkins, Isar Nejadgholi, Daniel Gillis, Judi McCuaig
Mitigation of gender bias in NLP has a long history tied to debiasing static word embeddings.
no code implementations • ACL (GeBNLP) 2021 • Hillary Dawkins
We observe an instance of gender-induced bias in a downstream application, despite the absence of explicit gender words in the test cases.
no code implementations • Findings (ACL) 2021 • Hillary Dawkins
We present a new observation of gender bias in a downstream NLP application: marked attribute bias in natural language inference.