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.

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• arxiv keynazarov2025humancompatible

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