Limitations of Pinned AUC for Measuring Unintended Bias
This work identifies limitations in a metric for measuring unintended bias in AI models, which is important for researchers and practitioners in fairness and machine learning, though it appears incremental as it critiques an existing method.
The report examines the Pinned AUC metric for measuring unintended bias in classification models and finds that it can obscure different kinds of unintended biases when underlying class distributions are not carefully controlled.
This report examines the Pinned AUC metric introduced and highlights some of its limitations. Pinned AUC provides a threshold-agnostic measure of unintended bias in a classification model, inspired by the ROC-AUC metric. However, as we highlight in this report, there are ways that the metric can obscure different kinds of unintended biases when the underlying class distributions on which bias is being measured are not carefully controlled.