{"ID":2895229,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09626","arxiv_id":"2507.09626","title":"humancompatible.interconnect: Testing Properties of Repeated Uses of Interconnections of AI Systems","abstract":"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.","short_abstract":"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-triv...","url_abs":"https://arxiv.org/abs/2507.09626","url_pdf":"https://arxiv.org/pdf/2507.09626v1","authors":"[\"Rodion Nazarov\",\"Anthony Quinn\",\"Robert Shorten\",\"Jakub Marecek\"]","published":"2025-07-13T13:35:15Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"eess.SY\"]","methods":"[]","has_code":false}