Robustness to Spurious Correlations via Human Annotations

Understand

The reliability of machine learning systems critically assumes that the associations between features and labels remain similar between training and test distributions.

• However, unmeasured variables, such as confounders, break this assumption---useful correlations between features and labels at training time can become useless or even harmful at test time.
• For example, high obesity is generally predictive for heart disease, but this relation may not hold for smokers who generally have lower rates of obesity and higher rates of heart disease.
• We present a framework for making models robust to spurious correlations by leveraging humans' common sense knowledge of causality.