Using insights from the field of natural language processing, computer scientist Dan Roth and his research group are developing an online platform that helps users find relevant and trustworthy information about the novel coronavirus.

There's still a lot that's not known about the novel coronavirus SARS-CoV-2 and COVID-19, the disease it causes. What leads some people to have mild symptoms and others to end up in the hospital? Do masks help stop the spread? What are the economic and political implications of the pandemic? As researchers try to address many of these questions, many of which will not have a simple'yes or no' answer, people are also trying to figure out how to keep themselves and their families safe.

Machine learning: This is a subset of the field of artificial intelligence that addresses problems that a computer cannot be "programmed" to solve.

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?