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Found 3 papers, 1 papers with code
FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networks
Empirical evaluation on real-world datasets indicates that FETA is not only able to guarantee fairness on-the-fly at prediction time but also is able to train accurate models exhibiting a much higher degree of individual fairness.
Post-processing Counterexample-guided Fairness Guarantees in Neural Networks
There is an increasing interest in adopting high-capacity machine learning models such as deep neural networks to semi-automate human decisions.
Counterexample-Guided Learning of Monotonic Neural Networks
Additionally, we propose a technique to use monotonicity as an inductive bias for deep learning.
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