"FAIM: Fairness-aware interpretable modeling for trustworthy machine ..."

Mingxuan Liu et al. (2024)

> Home

Details and statistics

DOI: 10.1016/J.PATTER.2024.101059

access: open

type: Journal Article

metadata version: 2024-10-17

- view
  - electronic edition via DOI (open access)
  authority control:
- export record
  dblp key:
  - journals/patterns/LiuNKSCOVL24
- ask others
- share record
  persistent URL:
  - https://dblp.org/rec/journals/patterns/LiuNKSCOVL24
Mingxuan Liu, Yilin Ning, Yuhe Ke, Yuqing Shang, Bibhas Chakraborty, Marcus Eng Hock Ong, Roger Vaughan, Nan Liu:
FAIM: Fairness-aware interpretable modeling for trustworthy machine learning in healthcare. Patterns 5(10): 101059 (2024)

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.