A Road-map Towards Explainable Question Answering A Solution for Information Pollution
This is an incremental position paper that discusses the need for explainability in QA systems to help users combat information pollution, without introducing new methods or data.
The paper addresses the problem of information pollution on the Web by proposing Explainable Question Answering (XQA) systems to provide transparency and user validation, but it does not present specific results or concrete numbers as it is a position paper outlining concepts and challenges.
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?