# A Road-map Towards Explainable Question Answering A Solution for Information Pollution

## TL;DR

This paper advocates for developing explainable question answering systems to combat information pollution by providing transparency, validation, and interpretability of answers, addressing core challenges and conceptual questions.

## Contribution

It offers a comprehensive overview of the core concepts, challenges, and key questions in developing explainable question answering systems for better information validation.

## Key findings

- Highlights the need for transparency in QA systems
- Identifies key challenges in explanation representation and evaluation
- Proposes a conceptual framework for XQA development

## Abstract

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?