most citedInterpretable bias mitigation for textual data: Reducing gender bias in patient notes while maintaining classification performance

4 citations · 4 across the 1 of their papers we have counts for

collaborators

2 papers

cs.CL2021★ 4 cited

Interpretable bias mitigation for textual data: Reducing gender bias in patient notes while maintaining classification performance

Joshua R. Minot, Nicholas Cheney, Marc Maier +3

Medical systems in general, and patient treatment decisions and outcomes in particular, are affected by bias based on gender and other demographic elements. As language models are…

cs.CL2019

Generating Information Extraction Patterns from Overlapping and Variable Length Annotations using Sequence Alignment

Frank Meng, Craig A. Morioka, Danne C. Elbers

Sequence alignments are used to capture patterns composed of elements representing multiple conceptual levels through the alignment of sequences that contain overlapping and variab…