Explainable Automated Fact-Checking: A Survey (2020)

Abstract: A number of exciting advances have been made in automated fact-checking thanks to increasingly larger datasets and more powerful systems, leading to improvements in the complexity of claims which can be accurately fact-checked. However, despite these advances, there are still desirable functionalities missing from the fact-checking pipeline. In this survey, we focus on the explanation functionality – that is fact-checking systems providing reasons for their predictions. We summarize existing methods for explaining the predictions of fact-checking systems and we explore trends in this topic. Further, we consider what makes for good explanations in this specific domain through a comparative analysis of existing fact-checking explanations against some desirable properties. Finally, we propose further research directions for generating fact-checking explanations, and describe how these may lead to improvements in the research area.

## introduction the harms of false claims, which, through their verisimilitude, masquerade as statements of fact, have become widespread as a result of the self-publishing popularized on the modern web 2.0, without verification. it is now widely accepted that if we are to efficiently respond to this problem, computational approaches are required @xcite . falsehoods take many forms, including audio and graphical content, however a significant quantity of misinformation exists in natural language. for this reason, computational linguistics has a significant role to play in addressing this problem. steady progress has been made in the area of computational journalism, and more specifically automated fact-checking and its orthogonal tasks. a number of fact-checking tasks have been formulated. among these tasks are the following: identifying the severity or nature of false claims, i.e., distinguishing between hoaxes, rumors and satire @xcite ; detecting check-worthy claims @xcite ; distinguishing between hyper-partisan and mainstream news @xcite ; stance detection and opinion analysis in fake news @xcite ; fauxtography, i.e., verifying the captions or claims which accompany published images @xcite ; table-based fact verification @xcite ; credibility assessment @xcite ; and an end-to-end pipeline which encompasses both the document-level evidence retrieval and sentence-level evidence selection sub-tasks of claim verification @xcite . established orthogonal tasks such as claim detection @xcite and deception detection @xcite also play a key role in automated fact-checking. the fact-checking pipeline is shown in figure 1 . the focus of this survey is explainable automated fact-checking. indeed, over the last few years strong progress has been made both in terms of the performance of deep learning models for fact-checking and also in terms of the availability of comprehensive datasets for training these models. however, not nearly as much work has been devoted to acquiring explanations for these systems, either in the form of posthoc explanations for the outputs of fact-checking models or by hard-wiring explanation methods into these fact-checking models, even though the justification of claim verification judgments is arguably a very important part of journalistic processes when performing manual fact-checking. ## automated fact-checking: state of the art engineering accurate and resilient computational methods for automated fact-checking is challenging. however, steady progress has been made, and the current state-of-the-art with respect to a number of fact-checking datasets achieves promising results. ## explanations for fact-checking there is increasing research interest in explanations for dnns. these approaches include model-agnostic methods for explaining the outputs of machine learning classifiers, e.g., @xcite and shap (lundberg and lee, 2017); work on evaluating explanations @xcite ; and the formalization of desiderata (i.e., functional, operational, and user requirements) for explanations @xcite . for the purpose of this survey, we choose to examine explanations specifically with respect to the domain of automated fact-checking. to this end we will overview existing methods for explainable fact-checking and perform a comparative analysis thereof. before we begin our analysis, it is important to make a distinction between model interpretability and model explainability, which we understand as follows. interpretability can be understood as the ability of a machine learning model to offer a mechanism by which its decision-making can be analyzed, and possibly visualized. an interpretation is not necessarily readily understood by those who do not have specific technical expertise or else that are not well-versed in the architecture of the model, e.g., as may be the case with attention weights. explainability can be understood as the ability of a machine learning model to deliver a rationale for its decisions, e.g., for a model which verifies claims through cross-checking against rules in a knowledge base, the explanation could be the full set of rules used to arrive at the final judgment. in this survey, we are primarily interested in natural language, human-readable explanations of machine learning models. however, at the same same time we acknowledge that model interpretations are very much a prerequisite through which human-readable explanations are achieved, but they are neither on their own sufficient nor necessarily human comprehensible. ## comparative analysis of explainable systems in this section we offer a qualitative analysis of explainable fact-checking approaches. we evaluate the explainable fact-checking systems described in section 3.2 against the eight desirable properties shown in ## future directions here we present a number of limitations in the state-of-the-art approaches to explainable fact-checking (in addition to the limitations emerging from unverifiable claims. in fact-checking, there is often an issue which arises with unverifiable claims. indeed, there is sometimes insufficient evidence to either verify or refute claims. this problem arises in both manual and computational fact-checking (uscinski and butler, 2013; @xcite . for this reason, most formulations of the fact-checking task take this scenario into account with the inclusion of an additional class, e.g., the not-enough-info label in the fever dataset @xcite . in such cases it would be necessary to offer an explanation as to why the claim could neither be verified nor refuted or what supporting evidence was missing such as to prevent a verdict being reached (i.e., a counterfactual explanation). for many explainable systems this is particularly tricky because explanations are typically generated with respect to the inputs. the knowledge graph-based systems @xcite take steps towards this, as their output amounts to rules that serve as both evidence and counter-evidence, but more human understandable explanations are needed which state explicitly what evidence, if any, is available to verify the claim and why it is insufficient. predictions vs. models. one limitation of current explainable fact-checking systems is the emphasis on outcome-driven explanations. these typically amount to explaining the predictions with reference to relation prediction. there is no system which offers process-driven or model explanations for multiple or all sub-tasks in the fact-checking pipeline, including justifications which might shed light on why and how decisions made in earlier sub-tasks affect outputs of later sub-tasks, e.g., evidence selection and natural language inference (nli). there is existing work on explaining nli @xcite , with, in particular, the e-snli dataset containing explanation annotations for nli @xcite . building upon these works in explainable nli, there are a number of directions which we envision as future avenues of research in explainable fact-checking which could result in valuable gains. ## conclusion in conclusion, the emerging work on explainable machine learning in the fact-checking domain shows a great deal of promise despite the particularly challenging nature of the problem. however, there are some limitations of current methods and task formulations which we highlight in this survey: all existing methods only look to explain one component of the fact-checking pipeline (relation prediction or entailment) and the systems only explain the predictions. in order to garner the most relevant, useful and insightful fact-checking explanations, a holistic and journalistic-informed approach should be taken. we identify possible directions for future research, which will hopefully extend the work achieved so far.