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（DEI）的监管是过去十年间的跨学科研究课题，自始便涉及（物理与软件）智能体与法律领域，但近期自2016年起已转向新闻界所称的“机器人律师”。早期限制软件智能体行为的方案之一便是电子机构。然而，随着人工神经网络（ANN）被重新定义为深度学习（DL），与深度学习使用相关的安全、隐私、伦理及法律问题已引发人工智能（AI）社区的关切。鉴于MAS的监管问题已基本得到妥善解决，我们提出将人工神经网络的监管，视为一种特殊类型受监管的人工神经网络——我们称之为机构神经网络（INN）——的智能体式训练。本文主要目的在于引起对人工教学（AT）的关注，并提供一个概念验证型受监管深度学习（RDL）的初步答案。本文引入了前述概念，并提供了SL语言——该语言此前被用于声明式建模和扩展电子机构——作为规范人工神经网络执行过程及其与人工教师（AT）交互的手段。