Explainable AI: current status and future directions
It addresses the need for explainability in AI for critical domains like healthcare and defense, but is incremental as it reviews existing techniques.
This paper provides an overview of Explainable AI (XAI) techniques from a multimedia perspective, discussing their advantages, shortcomings, and future directions to address trust and transparency in critical applications.
Explainable Artificial Intelligence (XAI) is an emerging area of research in the field of Artificial Intelligence (AI). XAI can explain how AI obtained a particular solution (e.g., classification or object detection) and can also answer other "wh" questions. This explainability is not possible in traditional AI. Explainability is essential for critical applications, such as defense, health care, law and order, and autonomous driving vehicles, etc, where the know-how is required for trust and transparency. A number of XAI techniques so far have been purposed for such applications. This paper provides an overview of these techniques from a multimedia (i.e., text, image, audio, and video) point of view. The advantages and shortcomings of these techniques have been discussed, and pointers to some future directions have also been provided.