  The increasing rate of information pollution on the Web requires novel
solutions to tackle that. Question Answering (QA) interfaces are simplified and
user-friendly interfaces to access information on the Web. However, similar to
other AI applications, they are black boxes which do not manifest the details
of the learning or reasoning steps for augmenting an answer. The Explainable
Question Answering (XQA) system can alleviate the pain of information pollution
where it provides transparency to the underlying computational model and
exposes an interface enabling the end-user to access and validate provenance,
validity, context, circulation, interpretation, and feedbacks of information.
This position paper sheds light on the core concepts, expectations, and
challenges in favor of the following questions (i) What is an XQA system?, (ii)
Why do we need XQA?, (iii) When do we need XQA? (iv) How to represent the
explanations? (iv) How to evaluate XQA systems?
