  The lack of explainability of a decision from an Artificial Intelligence (AI)
based "black box" system/model, despite its superiority in many real-world
applications, is a key stumbling block for adopting AI in many high stakes
applications of different domain or industry. While many popular Explainable
Artificial Intelligence (XAI) methods or approaches are available to facilitate
a human-friendly explanation of the decision, each has its own merits and
demerits, with a plethora of open challenges. We demonstrate popular XAI
methods with a mutual case study/task (i.e., credit default prediction),
analyze for competitive advantages from multiple perspectives (e.g., local,
global), provide meaningful insight on quantifying explainability, and
recommend paths towards responsible or human-centered AI using XAI as a medium.
Practitioners can use this work as a catalog to understand, compare, and
correlate competitive advantages of popular XAI methods. In addition, this
survey elicits future research directions towards responsible or human-centric
AI systems, which is crucial to adopt AI in high stakes applications.
