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Fact-checking on the Web has become the main mechanism through which wedetect the credibility of the news or information. Existing fact-checkersverify the authenticity of the information (support or refute the claim) basedon secondary sources of information. However, existing approaches do notconsider the problem of model updates due to constantly increasing trainingdata due to user feedback. It is therefore important to conduct user studies tocorrect models' inference biases and improve the model in a life-long learningmanner in the future according to the user feedback. In this paper, we presentFaxPlainAC, a tool that gathers user feedback on the output of explainablefact-checking models. FaxPlainAC outputs both the model decision, i.e., whetherthe input fact is true or not, along with the supporting/refuting evidenceconsidered by the model. Additionally, FaxPlainAC allows for accepting userfeedback both on the prediction and explanation. Developed in Python,FaxPlainAC is designed as a modular and easily deployable tool. It can beintegrated with other downstream tasks and allowing for fact-checking humanannotation gathering and life-long learning.

FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop