Abstract:
Artificial Intelligence (AI) and its data-centric
branch of machine learning (ML) have greatly evolved over
the last few decades. However, as AI is used increasingly in
real world use cases, the importance of the interpretability of
and accessibility to AI systems have become major research
areas. The lack of interpretability of ML based systems is a
major hindrance to widespread adoption of these powerful
algorithms. This is due to many reasons including ethical and
regulatory concerns, which have resulted in poorer adoption
of ML in some areas. The recent past has seen a surge in
research on interpretable ML. Generally, designing a ML
system requires good domain understanding combined with
expert knowledge. New techniques are emerging to improve
ML accessibility through automated model design. This paper
provides a review of the work done to improve interpretability
and accessibility of machine learning in the context of global
problems while also being relevant to developing countries.
We review work under multiple levels of interpretability
including scientific and mathematical interpretation, statistical
interpretation and partial semantic interpretation. This review
includes applications in three areas, namely food processing,
agriculture and health.
