Explainable Artificial Intelligence Approaches: A Survey
It addresses the need for explainability in AI for practitioners in high-stakes domains, but is incremental as it surveys existing methods.
This survey tackles the problem of AI's lack of explainability in high-stakes applications by analyzing popular Explainable AI (XAI) methods using a credit default prediction case study, providing insights into quantifying explainability and recommending paths toward responsible AI.
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.