Artificial intelligence (AI) systems often interact with multiple agents. The regulation of such AI systems often requires that {\em a priori\/} guarantees of fairness and robustness be satisfied. With stochastic models of agents' responses to the outputs of AI systems, such {\em a priori\/} guarantees require non-trivial reasoning about the corresponding stochastic systems. Here, we present an open-source PyTorch-based toolkit for the use of stochastic control techniques in modelling interconnections of AI systems and properties of their repeated uses. It models robustness and fairness desiderata in a closed-loop fashion, and provides {\em a priori\/} guarantees for these interconnections. The PyTorch-based toolkit removes much of the complexity associated with the provision of fairness guarantees for closed-loop models of multi-agent systems.
@article{arxiv.2507.09626,
title = {humancompatible.interconnect: Testing Properties of Repeated Uses of Interconnections of AI Systems},
author = {Rodion Nazarov and Anthony Quinn and Robert Shorten and Jakub Marecek},
journal= {arXiv preprint arXiv:2507.09626},
year = {2025}
}