post-processing

Adjusting the output of a model after the model has been run. Post-processing can be used to enforce fairness constraints without modifying models themselves. For example, one might apply post-processing to a binary classification model by setting a classification threshold such that equality of opportunity is maintained for some attribute by checking that the true positive rate is the same for all values of that attribute.

Practitioners refer to post-processing when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.