Forecast hub fairness dashboard showing the average error ratio (AER) distribution across different COVID-19 prediction models, organized by model type.
<p>Within each model type, teams are sorted in ascending order based on their median AER values. Since the user has selected “Only Race" as the variable of interest (see bottom left box) and “Hispanic" as the protected variable (see bottom center box), the AER values compare predicti...
Saved in:
| Main Author: | Saad Mohammad Abrar (21162498) (author) |
|---|---|
| Other Authors: | Naman Awasthi (21162501) (author), Daniel Smolyak (21162504) (author), Nekabari Sigalo (21162507) (author), Vanessa Frias Martinez (21162510) (author) |
| Published: |
2025
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Model fairness card displaying key performance metrics including model information, prediction error differences between protected and unprotected groups, AER values, and coverage statistics.
by: Saad Mohammad Abrar (21162498)
Published: (2025) -
Prediction fairness will be evaluated across types of models, training datasets (mobility), lookaheads and phases.
by: Saad Mohammad Abrar (21162498)
Published: (2025) -
Summary of generalized linear models and their specifications.
by: Saad Mohammad Abrar (21162498)
Published: (2025) -
GLM-2c: Urbanicity × model type effects relative to LM areas.
by: Saad Mohammad Abrar (21162498)
Published: (2025) -
GLM-1c: Race × model type effects relative to white reference group.
by: Saad Mohammad Abrar (21162498)
Published: (2025)