  In recent years, the use of sophisticated statistical models that influence
decisions in domains of high societal relevance is on the rise. Although these
models can often bring substantial improvements in the accuracy and efficiency
of organizations, many governments, institutions, and companies are reluctant
to their adoption as their output is often difficult to explain in
human-interpretable ways. Hence, these models are often regarded as
black-boxes, in the sense that their internal mechanisms can be opaque to human
audit. In real-world applications, particularly in domains where decisions can
have a sensitive impact--e.g., criminal justice, estimating credit scores,
insurance risk, health risks, etc.--model interpretability is desired.
Recently, the academic literature has proposed a substantial amount of methods
for providing interpretable explanations to machine learning models. This
survey reviews the most relevant and novel methods that form the
state-of-the-art for addressing the particular problem of explaining individual
instances in machine learning. It seeks to provide a succinct review that can
guide data science and machine learning practitioners in the search for
appropriate methods to their problem domain.
