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 Agents was 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 $I^*$, 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）的规范化是过去十年间一个涉及（物理与软件）智能体与法律的多学科研究主题，但自2016年起逐渐转向媒体所称的“机器人律师”。限制软件智能体行为的早期方案之一是电子机构。然而，随着人工神经网络（ANN）以深度学习（DL）的形式被重新定义，深度学习应用引发的安全、隐私、伦理及法律问题已在人工智能（AI）社区中引起关注。鉴于MAS的规范化问题已基本得到妥善解决，我们提出将人工神经网络的规范化视为基于智能体训练的一种特殊规范化人工神经网络——我们称之为机构神经网络（INN）。本文的主要目的是引起人们对人工教学（AT）的关注，并通过概念验证实现提供初步答案，展示规范化深度学习（RDL）的可行性。本文引入前一个概念，并提供$I^*$语言——一种先前用于声明式建模和扩展电子机构的语言——作为规范人工神经网络执行及其与人工教师（ATs）交互的手段。