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

Saeedeh Shekarpour  Email<EMAIL_ADDRESS>University of Dayton,
Dayton, USA  Faisal Alshargi  Email<EMAIL_ADDRESS>University of Leipzig, Leipzig, Germany

###### 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?

Index terms— Explainability, Explainable Question Answering, XQA, Knowledge
Graph, Corpus, Evidence, Source Checking, Fact Checking, Reasoning, Context,
Provenance, Information Pollution.

##  1 Introduction

Question Answering (QA) applications are a subcategory of Artificial
Intelligence (AI) applications where for a given question, an adequate
answer(s) is provided to the end-user regardless of concerns related to the
structure and semantics of the underlying data. The spectrum of QA
implementations varies from statistical approaches [10, 9], deep learning
models [16, 10] to simple rule-based (i.e., template-based) approaches [13,
8]. Also, the underlying data sets in which the answer is exploited might
range from Knowledge Graphs (KG) holding a solid semantics as well as
structure to unstructured corpora (free text) or consolidation of both. Apart
from the implementation details and the background data, roughly speaking, the
research community introduced the following categories of QA systems:

  * •

Ad-hoc QA: advocates simple and short questions and typically relies on one
single KG or Corpus.

  * •

Hybrid QA: requires federating knowledge from heterogeneous sources [2].

  * •

Complex QA: deals with complex questions which are long, and ambiguous.
Typically, to answer such questions, it is required to exploit answers from a
hybrid of KGs and textual content [1].

  * •

Visualized QA: answers texual questions from images [6].

  * •

Pipeline-based QA: provides automatic integration of the state-of-the-art QA
implementations [12, 11].

A missing point in all types of QA systems is that in case of either success
or failure, they are silent to the question of why? Why have been a particular
answer chosen? Why were the rest of the candidates disregarded? Why did the QA
system fail to answer? whether it is the fault of the model, quality of data,
or lack of data? The truth is that the existing QA systems similar to other AI
applications are a black box (see Figure 1) meaning they do not provide any
supporting fact (explanation) about the represented answer with respect to the
trustworthiness rate to the source of information, the confidence/reliability
rate to the chosen answer, and the chain of reasoning or learning steps led to
predict the final answer. For example, Figure 1 shows that the user sends the
question ‘what is the side effect of antibiotics?’ to the QA system. If the
answer is represented in a way similar to the interface of Google, then the
end-user might have a mixed feeling as to whether s/he can rely on this answer
or how and why such an answer is chosen among numerous candidates?

Figure 1: The existing QA systems are a black box which do not provide any
explanation for their inference.

The rising challenges regarding the credibility, reliability, and validity of
the state-of-the-art QA systems are of high importance, especially on critical
domains such as life-science involved with human life. The Explainable
Question Answering (XQA) systems are an emerging area which tries to address
the shortcomings of the existing QA systems. The recent article [17] published
a data set containing pairs of question/answer along with the supporting facts
of the corpus where an inference mechanism over them led to the answer. Figure
2 is an example taken from the original article [17]. The assumption behind
this data set is that the questions require multi-hops to conclude the answer,
which is not the case all the time. Besides, this kind of representations
might not be an ideal form for XQA; for example, whether representing solely
the supporting facts is sufficient? how reliable are the supporting facts? Who
published them? And how credible is the publisher? And furthermore, regarding
the interface, is not the end-user overwhelmed if s/he wants to go through all
the supporting facts? Is not there a more user-friendly approach for
representation?

Figure 2: An example from [17] where the supporting facts necessary to answer
the given question Q are listed.

The XQA similar to all applications of Explainable AI (XAI) is expected to be
transparent, accountable and fair [7]. If QA is biased (bad QA), it will come
up with discriminating information which is biased based on race, gender, age,
ethnicity, religion, social or political rank of publisher and targeted user
[3]. [4] raises six fundamental competency questions regarding XAI as follows:

  1. 1.

Why did the AI system do that?

  2. 2.

Why did not the AI system do something else?

  3. 3.

When did the AI system succeed?

  4. 4.

When did the AI system fail?

  5. 5.

When does the AI system give enough confidence in the decision that you can
trust?

  6. 6.

How can the AI system correct an error?

In the area of XQA, we adopt these questions; however, we apply sufficient
modifications as follows:

  1. 1.

Why did the QA system choose this answer?

  2. 2.

Why did not the QA system answer something else?

  3. 3.

When did the QA system succeed?

  4. 4.

When did the QA system fail?

  5. 5.

When does the QA system give enough confidence in the answer that you can
trust?

  6. 6.

How can the QA system correct an error?

This visionary paper introduces the core concepts, expectations and challenges
in favor of the questions (i) What is an Explainable Question Answering (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? In the following
sections, we address each question respectively.

Figure 3: The explainable question answering exposes explainable models and
explainable interface; then the user can make a decision as to whether to
trust or not.

##  2 What is XQA?

To answer the question of What is XQA?, we feature two layers i.e., model and
interface for XQA similar to XAI [4]. Figure 3 shows our envisioned plan for
XQA where at the end, the end user confidently conclude that he can/cannot
trust to the answer. In the following, we present a formal definition of XQA.

######  Definition 1 (Explainable Question Answering)

XQA is a system relying on an explainable computational model for exploiting
the answer and then utilizes an explainable interface to represent the
answer(s) along with the explanation(s) to the end-user.

This definition highlights two major components of XQA as (i) explainable
computational model and (ii) explainable interface. In the following we
discuss these two components in more details:

### Explainable Computational Model.

Whatever computational model employed in XQA system, (e.g., learning-based
model, schema-driven approach, reasoning approach, heuristic approach, rule-
based approach, or a mixture of various models) it has to explain all
intermediate and final choices meaning the rationale behind the decisions
should be transparent, fair, and accountable [7]. The responsible QA system
distinguishes misinformation, disinformation, mal-information, and true facts
[15]. Furthermore, it cares about the untrustworthiness and trustworthiness of
data publisher, information representation, updated or outdated information,
accurate or inaccurate information, and also the interpretations that the
answer might raise. Whereas, the fair QA system is not biased based on the
certain characteristics of the data publisher, or the targeted end user (e.g.,
region, race, social or political rank). Finally, the transparency of QA
systems refers to the availability and accessibility to the reasons behind the
decisions of the QA system in each step upon the request of involving
individuals (e.g., end user, developer, data publisher, policymakers).

### Explainable Interface.

The explainable interface introduced in [4] contains two layers (i) a
cognitive layer and (ii) an explanation layer. The cognitive layer represents
the implications learned from the computational model in an explainable form
(abstractive or summarized representation), and then the explanation layer is
responsible for delivering them to the end user in an interactive mode. We
introduce several fundamental features which the future generation of XQA have
to launch. We extensively elaborate on our view about the interface in Section
5.

##  3 Why do we need XQA?

We showcase the importance of having XQA using the two following arguments.

### Information Disorder Era.

The growth rate of mis-, dis-, mal- information on the Web is getting
dramatically worsened [14]. Still, the existing search engines fail to
identify misinformation even where it is highly crucial [5]. It is expected
from the information retrieval systems (either keyword-based search engines or
QA systems) to identify mis-, dis-, mal- information from reliable and
trustworthy information.

###  Human Subject Area.

Having XQA for areas being subjected to lives particularly human subject is
highly important. For example, bio-medical and life-science domains require to
discriminate between the hypothetical facts, resulting facts, methodological
facts, or goal-oriented facts. Thus XQA has to infer the answer of
informational question based on the context of the question as to whether it
is asking about resulting facts, or hypothetical facts, etc.

##  4 When do we need XQA?

Typically in the domains that the user wants to make a decision upon the given
answer, XQA matters since it enables the end user to make a decision with
trust. There are domains that traditional QA does not hurt. For example, if
the end user is looking for the ‘nearby Italian restaurant’, QA systems
suffice. On the contrary, in the domain of health, having the explanations is
demanding otherwise the health care providers can not entirely rely on the
answers disposed by the system.

##  5 How to represent the explanation?

Before, presenting the features of the interface; we illustrate the life cycle
of information on the Web (see Figure 4). Each piece of information has a
source who initially published it, then After reposting the original piece, it
might be framed in a context or manipulated. Also, concerning its circulation
on social media or the Web, it might be annotated or commented on by the
crowd.

Figure 4: The life cycle of information on the Web.

We feature the explainable QA interface with respect to its life cycle in
Figure 5. The explainable interface should enable the end user to

  * •

access context

  * •

find the provenance of information

  * •

do fact-checking

  * •

do source-checking

  * •

check the credibility of the source

  * •

detect manipulated information

  * •

report mis-, dis-, mal- information

  * •

access annotations (feedbacks) of the crowd

  * •

see the circulation history

Figure 5: The features of explainable interface for XQA.

##  6 How to evaluate XQA systems?

The evaluation of the XQA systems has to check the validity and performance of
all the features in the computational model and interface. For example,
whether the interface disposes a fact-checking feature or source checking
feature? If yes, how fair, transparent, and accountable are these features?
The research community requires to introduce metrics, criteria, and benchmarks
for evaluating these features.

##  7 Conclusion

In this paper, we discussed the concepts, expectations, and challenges of XQA.
The expectation is that the future generation of QA systems (or search
engines) rely on computational explainable models and interact with the end-
user via the explainable user interface. The explainable computational models
are transparent, fair and accountable. Also, the explainable interfaces enable
the end-user to interact with features for source-checking, fact-checking and
also accessing to context and circulation history. In addition, the
explainable interfaces allow the end-user to report mis-, dis-, mal-
information.

We are at the beginning of a long-term agenda to mature this vision and
furthermore provide standards and solutions. The phenomena of information
pollution is a dark side of the Web which will endanger our society,
democracy, justice service and health care. We hope that the XQA will be the
attention of the research community in the next couple of years.

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