humancompatible.interconnect: Testing Properties of Repeated Uses of Interconnections of AI Systems

TL;DR

This paper introduces an open-source PyTorch toolkit for modeling and verifying fairness and robustness in interconnected AI systems using stochastic control, enabling a priori guarantees in multi-agent interactions.

Contribution

It presents a novel toolkit that simplifies the process of ensuring fairness and robustness guarantees in interconnected AI systems through stochastic control techniques.

Findings

Provides a PyTorch-based toolkit for stochastic modeling of AI interconnections.

Enables a priori guarantees of fairness and robustness in multi-agent AI systems.

Reduces complexity in verifying properties of closed-loop AI interactions.

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.