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

2025-07-13

AI Summary

Multi-agent AI systems lack formal guarantees of fairness and robustness under repeated interactions. Method: We propose a closed-loop dynamic modeling framework grounded in stochastic control theory to formally verify the long-term behavioral properties of inter-agent interconnection structures. Contribution/Results: We introduce the first open-source framework supporting reusable verification of fairness and robustness properties, enabling provably guaranteed fairness and robustness within closed-loop models. Built on PyTorch, the toolkit integrates stochastic control and probabilistic modeling techniques to support both dynamic analysis and theoretical verification of multi-agent responses. Compared to existing approaches, it significantly reduces the computational complexity of fairness verification while providing mathematically rigorous, long-term behavioral assurances for AI system interconnectivity.

Technology Category

• Multiagent Systems: Adversarial Agents
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop 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.

Problem

• Testing fairness and robustness in repeated AI system interconnections
• Providing a priori guarantees for multi-agent AI interactions
• Simplifying fairness assurance in closed-loop multi-agent models

Innovation

• PyTorch toolkit for stochastic control modeling
• Closed-loop fairness and robustness guarantees
• Simplifies multi-agent system fairness analysis