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

TL;DR

FaxPlainAC is an explainable fact-checking tool that incorporates user feedback to improve model accuracy and support lifelong learning, addressing the need for model updates with increasing data and user input.

Contribution

It introduces a modular, Python-based fact-checking tool that gathers user feedback on both predictions and explanations for continuous model improvement.

Findings

Supports user feedback on predictions and explanations

Enables lifelong learning through model updates

Facilitates integration with downstream tasks

Abstract

Fact-checking on the Web has become the main mechanism through which we detect the credibility of the news or information. Existing fact-checkers verify the authenticity of the information (support or refute the claim) based on secondary sources of information. However, existing approaches do not consider the problem of model updates due to constantly increasing training data due to user feedback. It is therefore important to conduct user studies to correct models' inference biases and improve the model in a life-long learning manner in the future according to the user feedback. In this paper, we present FaxPlainAC, a tool that gathers user feedback on the output of explainable fact-checking models. FaxPlainAC outputs both the model decision, i.e., whether the input fact is true or not, along with the supporting/refuting evidence considered by the model. Additionally, FaxPlainAC allows for accepting user feedback both on the prediction and explanation. Developed in Python, FaxPlainAC is designed as a modular and easily deployable tool. It can be integrated with other downstream tasks and allowing for fact-checking human annotation gathering and life-long learning.