Often used as a model fairness metric, recall parity measures how “sensitive” the model is for one group compared to another — or a model’s ability to predict true positives correctly.
Example:
A regional healthcare provider might be interested in ensuring that their models predict healthcare needs equally between Asians (the sensitive group) and whites (the base group). If recall parity falls outside of the 0.8-1.25 threshold, it may indicate that Asians are not receiving the level of needed followup care as whites, leading to different levels of future hospitalization and health outcomes.
