标题： humancompatible.interconnect：人工智能系统互连重复使用的属性测试 显示英文标题

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

摘要： 人工智能（AI）系统通常与多个智能体进行交互。 此类AI系统的监管通常需要满足{\em 先验}的公平性和鲁棒性保证。 对于智能体对AI系统输出的随机模型，此类{\em 先验}的保证需要对相应的随机系统进行非平凡的推理。 在此，我们提出一个基于PyTorch的开源工具包，用于在建模AI系统之间的相互连接及其重复使用特性时应用随机控制技术。 它以闭环方式建模鲁棒性和公平性期望，并为这些相互连接提供{\em 先验}的保证。 基于PyTorch的工具包消除了为多智能体系统的闭环模型提供公平性保证所涉及的许多复杂性。 显示英文摘要

摘要： 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.