🤖 AI Summary
This work addresses the challenge of enforcing strict output fairness constraints in deep learning models under streaming prediction and small-batch settings, where conventional batch-based fairness methods fall short. To this end, the authors propose a differentiable “fairness layer” integrated at the network output to hard-enforce prescribed fairness criteria. They further introduce the first online primal-dual inference algorithm capable of operating on arbitrarily small batches, thereby overcoming the limitations of traditional batch-constrained approaches. Notably, this is the first method to employ a differentiable optimization layer for enforcing aggregate fairness, complemented by stability analysis within backpropagation to ensure differentiability and convergence during training. Experiments demonstrate that the proposed framework rigorously satisfies fairness requirements without compromising model performance, with both theoretical analysis and empirical results confirming its efficacy.
📝 Abstract
Differentiable optimization layers are traditionally integrated in predict-then-optimize frameworks where a neural model estimates parameters that subsequently serve as fixed inputs to downstream decision-making optimization problems. In this work, we introduce the concept of a "fairness layer": a differentiable optimization layer appended to a model's output layer that guarantees a chosen notion of output parity is satisfied when integrated into a neural network. Additionally, we introduce an online primal-dual inference algorithm that provides provable aggregate fairness guarantees for streaming predictions with arbitrarily small batch sizes, where traditional per-batch constraints become overly restrictive. Numerical experiments demonstrate the effectiveness of the fairness layer and associated algorithm, and theoretical analysis characterizes the layer's differentiability and stability properties during model training and backpropagation. Our code for these experiments is publicly available on GitHub (https://github.com/dtroxell19/FairDL-ICML-2026.git) and our public Python package documentation can be found online: https://dtroxell19.github.io/fairness_training/.