  Understanding model performance on unlabeled data is a fundamental challenge
of developing, deploying, and maintaining AI systems. Model performance is
typically evaluated using test sets or periodic manual quality assessments,
both of which require laborious manual data labeling. Automated performance
prediction techniques aim to mitigate this burden, but potential inaccuracy and
a lack of trust in their predictions has prevented their widespread adoption.
We address this core problem of performance prediction uncertainty with a
method to compute prediction intervals for model performance. Our methodology
uses transfer learning to train an uncertainty model to estimate the
uncertainty of model performance predictions. We evaluate our approach across a
wide range of drift conditions and show substantial improvement over
competitive baselines. We believe this result makes prediction intervals, and
performance prediction in general, significantly more practical for real-world
use.
