Measuring Model Biases in the Absence of Ground Truth
Osman Aka, Ken Burke, Alex Bauerle, Christina Greer, Margaret Mitchell · 2021
The measurement of bias in machine learning often focuses on model performance across identity subgroups (such as man and woman) with respect to groundtruth labels. However, these methods do not directly measure the associations that a model may have learned, for example between labels and identity subgroups. Further, measuring a model's bias requires a fully annotated evaluation dataset which may not be easily available in practice.