Framework for Symbolic Rules Constraints in Neural Networks

The aim of this project is to develop a framework designed to assist users in constraining machine learning (ML) models. By utilizing this framework, users will have the ability to specify certain fairness metrics that should be upheld, thereby enhancing fairness in ML processes. For instance, one such metric could be ensuring demographic parity stays below a certain threshold. Given the opacity of neural networks (NN), this framework will especially focus this particular use case. Additionally, this will involve implementing a mechanism to penalize batches that fail to uphold the symbolic constraints defined in the model.