The use of Shapley Values (SVs) to explain machine learning model predictions is established. Recent research efforts have been devoted to generating efficient Neural Network-based SVs estimates. However, the variability of the generated estimates, which depend on the selected data sampling, model, and training parameters, brings the reliability of such estimates into question. By leveraging the concept of Interval SVs, we propose to incorporate SVs uncertainty directly into the learning process. Specifically, we explain ensemble models composed of multiple predictors, each one generating potentially different outcomes. Unlike all existing approaches, the explainer design is tailored to Interval SVs learning instead of SVs only. We present three new Network-based explainers relying on different ISV paradigms, i.e., a Multi-Task Learning network inspired by the Shapley value's weighted least squares characterization and two Interval Shapley-Like Value Neural estimators. The experiments thoroughly evaluate the new approaches on ten benchmark datasets, looking for the best compromise between intervals' accuracy and explainers' efficiency.

Question answering from visually rich documents (VRDs) is the task of retrieving the correct answer to a natural language question by considering the content of textual and visual elements in the document, as well as the pages’ layout. To answer closed-ended questions that require a deep understanding of the hierarchical relationships between the elements, i.e., the full document-level understanding (FDU) task, state-of-the-art graph-based approaches to FDU model the pairwise element relationships in a graph model. Although they incorporate logical links (e.g., a caption refers to a figure) and spatial ones (e.g., a caption is placed below the figure), they currently disregard the semantic similarity among multimodal document elements, thus potentially yielding suboptimal scoring of the elements’ relevance to the input question. In this paper, we propose GRAS-FDU, a new graph attention network tailored to FDU. GATS-FDU is trained to jointly consider multiple document facets, i.e., the local, spatial, and semantic elements’ relationships. The results show that our approach achieves superior performance compared to several baseline methods.

Thanks to the rapid progress of the digitalization process, Visually-Rich Documents (VRDs) such as PDF files or scanned documents have become among the most widespread sources of knowledge. However, Question Answering on VRDs is challenged by the presence of multi-page relationships between document elements such as tables, figures, sections. This paper addresses a specific Visual Question Answering subtask from VDRs where answer generation leverages pairwise element relations in multi-page documents. We explore the performance of text-only and multimodal Transformer-based architectures as well as open-source Large Language Models. The results show that multimodal Transformers outperform the other tested methods, particularly when training samples contain explicit textual references to the elements in the document layout.

The Document-based Visual Question Answering competition addresses the automatic detection of parent-child relationships between elements in multi-page documents. The goal is to identify the document elements that answer a specific question posed in natural language. This paper describes the PoliTo's approach to addressing this task, in particular, our best solution explores a text-only approach, leveraging an ad hoc sampling strategy. Specifically, our approach leverages the Masked Language Modeling technique to fine-tune a BERT model, focusing on sentences containing sensitive keywords that also occur in the questions, such as references to tables or images. Thanks to the effectiveness of this approach, we are able to achieve high performance compared to baselines, demonstrating how our solution contributes positively to this task.

Shapley Values are concepts established for eXplainable AI. They are used to explain black-box predictive models by quantifying the features' contributions to the model's outcomes. Since computing the exact Shapley Values is known to be computationally intractable on real-world datasets, neural estimators have emerged as alternative, more scalable approaches to get approximated Shapley Values estimates. However, experiments with neural estimators are currently hard to replicate as algorithm implementations, explainer evaluators, and results visualizations are neither standardized nor promptly usable. To bridge this gap, we present BONES, a new benchmark focused on neural estimation of Shapley Value. It provides researchers with a suite of state-of-the-art neural and traditional estimators, a set of commonly used benchmark datasets, ad hoc modules for training black-box models, as well as specific functions to easily compute the most popular evaluation metrics and visualize results. The purpose is to simplify XAI model usage, evaluation, and comparison. In this paper, we showcase BONES results and visualizations for XAI model benchmarking on both tabular and image data. The open-source library is available at the following link: https://github.com/DavideNapolitano/BONES.