Abstract: In this study, we investigate the expressive power of deep neural networks with ReLU activations in binary classification tasks, where the final readout is given by a linear hyperplane. Our focus is on the geometric and topological properties of the negative region induced in the input space. Using structural properties of the tope graph associated with hyperplane arrangements, we analyze the topology of the decision region at the output layer and reveal certain constraints on its structure. We then examine how this region is propagated backward through the layers of the network via the ReLU transformations. This viewpoint allows us to study how topological features evolve under layer-wise pullbacks. Based on this framework, we propose a conjectural principle describing the stability of topological properties of decision regions under such maps. This work establishes a connection between neural network expressivity and combinatorial topology.

Abstract: The growing use of machine learning in data-driven organizations has increased the need for models that are not only accurate, but also interpretable, reliable, and operationally meaningful. Particular attention is given to decision environments shaped by complex organizational data, where transparency and robustness play a critical role in the quality of managerial actions. Within this context, machine learning is addressed as a core component of decision intelligence supported by analytical reasoning, domain knowledge, and implementation constraints. This perspective establishes a connection between interpretable machine learning and trustworthy decision intelligence in management information systems.