Finding Shortcuts to a black-box model using Frequent Sequence Mining
Explaining Deep Learning models for Fact-Checking
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Abstract
Deep-learning (DL) models could greatly advance the automation of fact-checking, yet have not widely been adopted by the public because of their hard-to-explain nature. Although various techniques have been proposed to use local explanations for the behaviour of DL models, little attention has been paid to global explanations.
In response, we investigate whether a frequent sequence mining (FSM) tool finds sequence patterns, that act as shortcuts, to a state-of-the-art model in the context of fact-checking. By studying the connections between a model’s input and output, association rules (ARs) can be used as a global explanation for the interpretation of the model. The shortcuts were evaluated using a heuristic-based minimum support value, the strength of each rule was determined using confidence, and the support value indicates the global coverage of rules. Shortcuts help to form an interpretation for creating counterfactual prompts, which can be used as a risk assessment tool for DL models. Other applications for rule-based global explanations are left for future work