构建工具链，保障多AI系统反复交互时的公平与鲁棒性。
humancompatible.interconnect: Testing Properties of Repeated Uses of Interconnections of AI Systems
- 基于PyTorch的开源工具，支持多智能体系统闭环建模。
- 提供对公平性与鲁棒性的预先保证，适用于重复交互场景。
- 降低复杂度，适合研究多代理系统安全与合规的开发者。
人工智能系统常需与多个智能体交互。此类系统的监管往往要求在事前满足公平性与鲁棒性的保证。在智能体对AI输出响应具有随机性的情况下，这些事前保证需对相应随机系统进行非平凡推理。本文提出一个基于PyTorch的开源工具包，用于建模多智能体系统的相互连接及其重复使用性质，采用闭环方式刻画鲁棒性与公平性需求，并提供此类连接的事前保证。该工具包显著降低了为闭环多智能体模型提供公平性保证的复杂性。
原文摘要 · Abstract (English)
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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