Fairness Assessment
Cross-source consensus on Fairness Assessment from 1 sources and 4 claims.
1 sources · 4 claims
Risks & contraindications
Evidence quality
Highlighted claims
- Fairness was assessed by comparing mean error and error distributions across sex, age, education level, and native language subgroups. — Prediction of Oswestry Disability Index and Numeric Rating Scale scores after lumbar spine surgery: machine learning model development and fairness assessment
- The models showed little difference in error across sex. — Prediction of Oswestry Disability Index and Numeric Rating Scale scores after lumbar spine surgery: machine learning model development and fairness assessment
- The models consistently underestimated improvement among non-native Norwegian speakers. — Prediction of Oswestry Disability Index and Numeric Rating Scale scores after lumbar spine surgery: machine learning model development and fairness assessment
- Despite native-language subgroup differences, the study considered the overall models fair across assessed demographic subgroups. — Prediction of Oswestry Disability Index and Numeric Rating Scale scores after lumbar spine surgery: machine learning model development and fairness assessment