  Artificial Intelligence (AI) and its relation with societies is increasingly
becoming an interesting object of study from the perspective of sociology and
other disciplines. Theories such as the Economy of Conventions (EC) are usually
applied in the context of interpersonal relations but there is still a clear
lack of studies around how this and other theories can shed light on
interactions between human an autonomous systems. This work is focused into
studying a preliminary step that is a key enabler for the subsequent
interaction between machines and humans: how the processes of researching,
designing and developing AI related systems reflect different moral registers,
represented by conventions within the EC. Having a better understanding of
those conventions guiding the advances in AI is considered as the first and
required advance to understand the conventions afterwards reflected by those
autonomous systems in the interactions with societies. For this purpose, we
develop an iterative tool based on active learning to label a data set from the
field of AI and Machine Learning (ML) research and present preliminary results
of a supervised classifier trained on these conventions. To further demonstrate
the feasibility of the approach, the results are contrasted with a classifier
trained on software conventions.
