\Mathcal{I}$ Net

$\mathcal{I}$-Nets represent a burgeoning area of research focused on developing methods for explaining and interpreting the behavior of neural networks, particularly in situations where training data is unavailable. Current efforts center on creating differentiable neural network architectures, such as $\partial\mathbb{B}$ nets and $\mathcal{TT}$nets, that learn discrete functions and can be readily translated into interpretable forms like Boolean logic circuits or decision trees. This research aims to enhance the transparency and trustworthiness of neural networks, thereby increasing their applicability in safety-critical domains and fostering greater understanding of their decision-making processes.