Post-processing for Individual Fairness
This work addresses bias correction for users of production ML systems, offering a practical solution that avoids costly retraining, though it is incremental as it builds on existing post-processing methods.
The paper tackles the problem of correcting bias in deployed machine learning systems without retraining by proposing post-processing algorithms for individual fairness, which empirically reduce biases in large-scale NLP models like BERT while maintaining accuracy.
Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. The main appeal of post-processing is that it avoids expensive retraining. In this work, we propose general post-processing algorithms for individual fairness (IF). We consider a setting where the learner only has access to the predictions of the original model and a similarity graph between individuals, guiding the desired fairness constraints. We cast the IF post-processing problem as a graph smoothing problem corresponding to graph Laplacian regularization that preserves the desired "treat similar individuals similarly" interpretation. Our theoretical results demonstrate the connection of the new objective function to a local relaxation of the original individual fairness. Empirically, our post-processing algorithms correct individual biases in large-scale NLP models such as BERT, while preserving accuracy.