  Machines are being increasingly used in decision-making processes, resulting
in the realization that decisions need explanations. Unfortunately, an
increasing number of these deployed models are of a 'black-box' nature where
the reasoning behind the decisions is unknown. Hence, there is a need for
clarity behind the reasoning of these decisions. As humans, we would want these
decisions to be presented to us in an explainable manner. However, explanations
alone are insufficient. They do not necessarily tell us how to achieve an
outcome but merely tell us what achieves the given outcome. For this reason, my
research focuses on explainability/interpretability and how it extends to
counterfactual thinking.
