Machine learning has revolutionized recommendation systems by employing ranking models for personalized item suggestions. Despite their effectiveness, learning-to-rank (LTR) models often operate as complex systems, making it difficult to discern the factors influencing their ranking decisions. This lack of transparency raises concerns about potential errors, biases, and ethical implications. As a result, interpretable LTR models have emerged as a solution to enhance transparency and mitigate these challenges.
Currently, the state-of-the-art intrinsically interpretable ranking model is led by generalized additive models. However, ranking GAMs have some limitations that affect their successful application in experiment environments, such as being computationally intensive and struggling to handle high-dimensional data. In contrast to these drawbacks, post-hoc methods can potentially provide more scalable and efficient solutions for real-time ranking. In this study, we propose a post-hoc method for learning-to-rank tasks combined with the interpretable GAMs. The evaluation results tested with Kendall’s 𝜏 value indicate that our model can effectively explain different types of black-box rankers.

In today's society, claims are everywhere, in the online and offline world. Fact-checking models can check these claims and predict if a claim is true or false, but how can these models be checked? Post-hoc XAI feature attribution methods can be used for this. These methods give scores indicating the influence of the individual tokens on the model's decision-making. In our research, we evaluate three popular feature attribution methods in the context of fact-checking: LIME, Kernel SHAP, and Integrated Gradients. We used the NLP architecture ExPred as a fact-checking model in our research. The feature attribution methods were evaluated using a human-grounded and pseudo ground truth evaluation. The results from these evaluations indicate that Integrated Gradients enables humans to form an opinion better and performs better in our pseudo ground truth evaluation. A potential explanation is that the iterations should be increased for LIME and Kernel SHAP. Our findings suggest that Integrated Gradients performs better in our study. Still, more research for other tasks and models would be beneficial to ensure that these results apply to other cases.

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

The goal of this paper is to examine how different presentation strategies of Explanainable Artificial Intelligence (XAI) explanation methods for textual data affect non-expert understanding in the context of fact-checking. The importance of understand- ing the decision of an Artificial Intelligence (AI) in human-AI interaction and the need for effective explanation methods to improve trust in AI models are highlighted. The study focuses on three expla- nation methods: interpretable-by-design model Ex- Pred, feature attribution technique LIME, and in- stance attribution method k-NN. Two presentation strategies were compared for each method, and par- ticipants were presented with a set of claims and asked to indicate their understanding and level of agreement with the AI’s classification. The main hypothesis is that participants will appreciate all available context and details, as long as it is pre- sented in a structured way, and will find visual rep- resentations of data easier to understand than tex- tual ones. Results from the study indicate that par- ticipants prefer explanations that are simple and structured, and that visual presentations are not as effective, especially when it is the first time a user interacts with this type of data. Additionally, it was found that better formatting leads to a better- calibrated understanding of the explanation. The results of this study will provide valuable insight into the best way to present XAI explanations to non-experts to enhance their understanding and re- duce the deployment risk associated with Natural Language Processing (NLP) models for automated fact-checking. The study’s code, data, and Figma templates are publicly available for reproducibility.