humancompatible.interconnect: Testing Properties of Repeated Uses of Interconnections of AI Systems
This work addresses the problem of regulatory compliance for fairness and robustness in multi-agent AI systems, though it appears incremental as it builds on existing stochastic control methods.
The authors tackled the challenge of providing a priori guarantees for fairness and robustness in interconnected AI systems by developing an open-source PyTorch toolkit that uses stochastic control techniques to model these systems and their repeated uses, resulting in simplified complexity for ensuring such guarantees.
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.