  Machine Learning and Artificial Intelligence are considered an integral part
of the Fourth Industrial Revolution. Their impact, and far-reaching
consequences, while acknowledged, are yet to be comprehended. These
technologies are very specialized, and few organizations and select highly
trained professionals have the wherewithal, in terms of money, manpower, and
might, to chart the future. However, concentration of power can lead to
marginalization, causing severe inequalities. Regulatory agencies and
governments across the globe are creating national policies, and laws around
these technologies to protect the rights of the digital citizens, as well as to
empower them. Even private, not-for-profit organizations are also contributing
to democratizing the technologies by making them \emph{accessible} and
\emph{affordable}. However, accessibility and affordability are all but a few
of the facets of democratizing the field. Others include, but not limited to,
\emph{portability}, \emph{explainability}, \emph{credibility}, \emph{fairness},
among others. As one can imagine, democratizing AI is a multi-faceted problem,
and it requires advancements in science, technology and policy. At
\texttt{mlsquare}, we are developing scientific tools in this space.
Specifically, we introduce an opinionated, extensible, \texttt{Python}
framework that provides a single point of interface to a variety of solutions
in each of the categories mentioned above. We present the design details, APIs
of the framework, reference implementations, road map for development, and
guidelines for contributions.
