  The current wave of digital transformation has spurred digitisation reforms
and has led to prodigious development of AI & NLP systems, with several of them
entering the public domain. There is a perception that these systems have a non
trivial impact on society but there is a dearth of literature in critical AI
exploring what kinds of systems exist and how do they operate. This paper
constructs a broad taxonomy of NLP systems which impact or are impacted by the
``public'' and provides a concrete analyses via various instrumental and
normative lenses on the socio-technical nature of these systems. This paper
categorises thirty examples of these systems into seven families, namely;
finance, customer service, policy making, education, healthcare, law, and
security, based on their public use cases. It then critically analyses these
applications, first the priors and assumptions they are based on, then their
mechanisms, possible methods of data collection, the models and error functions
used, etc. This paper further delves into exploring the socio-economic and
political contexts in which these families of systems are generally used and
their potential impact on the same, and the function creep of these systems. It
provides commentary on the potential long-term downstream impact of these
systems on communities which use them. Aside from providing a birds eye view of
what exists our in depth analysis provides insights on what is lacking in the
current discourse on NLP in particular and critical AI in general, proposes
additions to the current framework of analysis, provides recommendations future
research direction, and highlights the need to importance of exploring the
social in this socio-technical system.
