Evaluating Model Robustness and Stability to Dataset Shift

Understand

As the use of machine learning in high impact domains becomes widespread, the importance of evaluating safety has increased.

• An important aspect of this is evaluating how robust a model is to changes in setting or population, which typically requires applying the model to multiple, independent datasets.
• Since the cost of collecting such datasets is often prohibitive, in this paper, we propose a framework for analyzing this type of stability using the available data.
• We use the original evaluation data to determine distributions under which the algorithm performs poorly, and estimate the algorithm's performance on the "worst-case" distribution.