"Interpretable Bias Mitigation for Textual Data: Reducing Genderization in ..."

Joshua R. Minot et al. (2022)

> Home

Details and statistics

DOI: 10.1145/3524887

access: closed

type: Journal Article

metadata version: 2023-01-03

- view
  - electronic edition via DOI
  - unpaywalled version
  authority control:
- export record
  dblp key:
  - journals/health/MinotCMEDD22
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/health/MinotCMEDD22
Joshua R. Minot, Nicholas Cheney, Marc Maier, Danne C. Elbers, Christopher M. Danforth, Peter Sheridan Dodds:
Interpretable Bias Mitigation for Textual Data: Reducing Genderization in Patient Notes While Maintaining Classification Performance. ACM Trans. Comput. Heal. 3(4): 39:1-39:41 (2022)

manage site settings

To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.