Regulation of Multi-Agent Systems (MAS) and Declarative Electronic Institutions (DEIs) was a multidisciplinary research topic of the past decade involving (Physical and Software) Agents and Law since the beginning, but recently evolved towards News-claimed Robot Lawyer since 2016. One of these first proposals of restricting the behaviour of Software Agentswas Electronic Institutions.However, with the recent reformulation of Artificial Neural Networks (ANNs) as Deep Learning (DL), Security, Privacy,Ethical and Legal issues regarding the use of DL has raised concerns in the Artificial Intelligence (AI) Community. Now that the Regulation of MAS is almost correctly addressed, we propose the Regulation of Artificial Neural Networks as Agent-based Training of a special type of regulated Artificial Neural Network that we call Institutional Neural Network (INN).The main purpose of this paper is to bring attention to Artificial Teaching (AT) and to give a tentative answer showing a proof-of-concept implementation of Regulated Deep Learning (RDL). This paper introduces the former concept and provide sI, a language previously used to model declaratively and extend Electronic Institutions, as a means to regulate the execution of Artificial Neural Networks and their interactions with Artificial Teachers (ATs)

翻译：多智能体系统（MAS）与声明式电子机构（DEIs）的规制是过去十年间横跨（物理与软件）智能体及法学领域的多学科研究主题，但自2016年起逐步转向新闻界所称的“机器人律师”。早期限制软件智能体行为的方案之一是电子机构。然而，随着人工神经网络（ANNs）被重新表述为深度学习（DL），深度学习在安全性、隐私性、伦理及法律层面的应用问题引发了人工智能（AI）界的广泛关注。鉴于MAS规制已基本得到妥善解决，我们提出将人工神经网络的规制转化为基于智能体训练的受规制人工神经网络——即我们所称的“制度神经网络（INN）”。本文的主要目的在于引起学界对“人工教学（AT）”的重视，并通过概念验证实现初步展示“受规制深度学习（RDL）”。本文引入前述概念，并提供sI（一种此前用于声明式建模与扩展电子机构的语言）作为规制人工神经网络执行及其与人工教学者（ATs）交互的途径。