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The increasing sophistication of cyber threats necessitates advancements in intrusion detection systems (IDS). This research introduces a novel IDS framework that integrates advanced machine learning (ML) techniques, including ensemble learning, ...

Machine learning has become increasingly important in biomechanics. It allows to unveil hidden patterns from large and complex data, which leads to a more comprehensive understanding of biomechanical processes and deeper insights into human movement. ...

Despite all the fact-checking initiatives on news and social media aimed at countering misinformation, they remain insufficient to promptly address the wide array of misleading information disseminated by both news and social media outlets. Rather than ...

The ICMI 2024 workshop on “Multimodal Co-Construction of Explanations with XAI” bridges the fields of Explainable Artificial Intelligence (XAI) and Multimodal Interaction, focusing on the recent perspective that effective AI explanations should be ...

As machine learning and deep learning become increasingly integrated into our daily lives, understanding how these technologies make decisions is crucial. To ensure transparency, accountability, and ethical adherence, these so-called "black-box" models ...

Shapley value attribution (SVA) is an increasingly popular Explainable AI (XAI) approach that has been widely used in many recent applied studies to gain new insights into the underlying information systems. However, most existing SVA methods are error-...

Path integration methods generate attributions by integrating along a trajectory from a baseline to the input. These techniques have demonstrated considerable effectiveness in the field of explainability research. While multiple types of baselines for ...

Beyond the traditional CNN structure, we have recently witnessed lots of breakthroughs in computer vision architectures such as Vision Transformer, MLP-Mixer, SNN-MLP, and so on. However, many efforts in developing novel architectures for vision tasks ...

Despite the powerful capabilities of GNN-based drug screening model in predicting target drug properties, the black-box nature of these models poses a challenge for practical application, particularly in a field as critical as drug development where ...

Recommending products to users with intuitive explanations helps improve the system in transparency, persuasiveness, and satisfaction. Existing interpretation techniques include post hoc methods and interpretable modeling. The former category could ...

As autonomous vehicles (AVs) emerge as new agents in traffic, it becomes imperative to understand how they can be introduced effectively and acceptably. AVs enter existing socio-technical systems, where members of various social communities are already ...

Survival prediction for cancer patients is critical for optimal treatment selection and patient management. Current patient survival prediction methods typically extract survival information from patients’ clinical record data or biological and ...

We propose a permutation-based explanation method for image classifiers. Current image-model explanations like activation maps are limited to instance-based explanations in the pixel space, making it difficult to understand global model behavior. ...

Participatory Budgeting (PB) has evolved into a key democratic instrument for resource allocation in cities. Enabled by digital platforms, cities now have the opportunity to let citizens directly propose and vote on urban projects, using different voting ...

Explainable Predictive Process Monitoring aims at deriving explanations of the inner workings of black-box classifiers used to predict the continuation of ongoing process executions. Most existing techniques use data attributes (e.g., the loan ...

Explaining the mechanisms behind model predictions is a common strategy in AI-assisted decision-making to help users rely appropriately on AI. However, recent research shows that the effectiveness of explanations depends on numerous factors, leading to ...

Counterfactual explanations (CEs) play an important role in detecting bias and improving the explainability of data-driven classification models. A CE is a minimal perturbed data point for which the decision of the model changes. Most of the existing ...

This study introduces an explainable AI framework for online medical Question Answering (QA) tasks, a growing need in Internet hospitals and digital healthcare services. In response to the increasing demand for automated yet accountable medical ...

This article aims to develop a semi-formal representation for Human-AI (HAI) interactions, by building a set of interaction primitives which can specify the information exchanges between users and AI systems during their interaction. We show how these ...

In recent years, knowledge graphs (KGs) have been widely applied in various domains for different purposes. The semantic model of KGs can represent knowledge through a hierarchical structure based on classes of entities, their properties, and their ...