  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)
