Explainable Automated Fact-Checking: A Survey
It tackles the problem of improving transparency and trust in automated fact-checking for users and researchers, but it is incremental as it reviews and synthesizes existing work rather than introducing new methods.
This survey addresses the lack of explanation functionality in automated fact-checking systems by summarizing existing methods for providing reasons behind predictions and analyzing trends and desirable properties for good explanations in this domain.
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.