Explaining the decisions of learned models in human-interpretable language is critical for building trustworthy AI. Much of the current work on explainable AI in image classification focuses on generating local (image-specific) explanations that high...
AI-generated content has grown significantly in recent years. Today, AI-generated and human-made images coexist across various settings, including news media, social platforms, and beyond. However, we still know relatively little about how audiences...
The use of AI-assisted feedback systems has steadily increased in higher education, yet there has been limited research as to which components of expert feedback can be supported by AI and which require continued human judgment. This issue is particu...
The aim of this study was to investigate the feasibility of using machine learning techniques based on morphological features in classifying two subtypes of primary intestinal T-cell lymphomas (PITLs) defined according to the WHO criteria: monomorphi...
The convergence of artificial intelligence (AI), explainable AI (XAI), and neuroscience is fostering new opportunities for understanding both machine and biological intelligence through interpretable and human-centered learning paradigms. In this Per...
Evaluations of medical artificial intelligence (AI) explanations often rely on self-reported trust, perceived usefulness, acceptance, or final decision outcomes, while less directly characterizing whether users check evidence around AI outputs during...
The rapid advancement of artificial intelligence (AI) technologies has transformed various sectors, significantly enhancing processes and augmenting human capabilities. However, these advancements have also introduced critical concerns related to the...
The integration of artificial intelligence (AI) into financial advisory services has transformed conventional advisory models into intelligent decision-support systems capable of processing large volumes of financial information and generating automa...
This article develops a conceptual account of semantic displacement in conversational AI. The central question concerns how agency is affected when systems do more than automate information retrieval and begin to supply the descriptions, classificati...
This review investigates the application of Explainable Artificial Intelligence (XAI) in biomedical informatics, encompassing domains such as medical imaging, genomics, and electronic health records. Through a systematic analysis of 43 peer-reviewed...
Explainable artificial intelligence (XAI) has become a central methodology for developing transparent, accountable, and human-centered AI systems. As data-driven models are increasingly deployed in high-stakes and socially consequential settings, exp...
Artificial intelligence (AI) is increasingly applied to healthcare decision-making; however, many persistent patient safety risks arise from sociotechnical conditions such as communication breakdowns, coordination failures, and organisational culture...
The educational value of artificial intelligence (AI) depends on technological capabilities and human–AI interaction. This is particularly relevant in interpreter training, which involves high cognitive demands, contextual decisions, and time-s...
Open-ended Student Evaluations of Teaching (SET) provide rich information about students’ learning experiences and instructional needs, yet their large-scale analysis remains difficult to conduct through manual coding alone. This study proposes...
With the continuous advancement of artificial intelligence (AI) technology, AI algorithms have demonstrated exceptional aircraft control capabilities in highly dynamic and complex scenarios such as aerial combat. However, the inherent lack of explain...
This paper proposes the AI–Human Collaborative Interpretation (AHCI) human–machine collaboration methodological framework, offering a new research pathway for the digital interpretation of complex cultural heritage. The framework integrat...
Junctional hemorrhage has a high fatality rate due to how difficult it is to control rapid bleeding from major vessels. The available methods to stop junctional blood loss are prone to placement errors as well as failure during transport and during p...
In a wide range of industries and academic fields, artificial intelligence is becoming increasingly prevalent. AI models are taking on more crucial decision-making tasks as they grow in popularity and performance. Although AI models, particularly mac...
Public discussion of artificial intelligence and employment is often reduced to a binary between technological optimism and pessimism. This qualitative study examines 3275 substantive comments from 22 purposively selected English-language Reddit thre...
Background/Objectives: Diagnostic metabolites measured in newborn screening, inherited metabolic disease, lysosomal storage disease, oncometabolite testing and routine clinical biochemistry are direct read-outs of human metabolic state. Their mechani...
Emotion is increasingly incorporated into AI-supported feedback in education, yet less is known about how learners interpret emotion-related messages once they are presented. This paper reports an exploratory classroom-based study comparing three lea...
Deep visual recognition has achieved remarkable success across various domains, including medical imaging, autonomous vehicles, and security systems. However, the black-box nature of deep learning models poses challenges in terms of transparency and...
The lack of transparency in many AI systems continues to hinder their adoption in critical domains such as healthcare, finance, and autonomous systems. While recent explainable AI (XAI) methods—particularly those leveraging large language model...
While Artificial Intelligence (AI) promises significant enhancements for Clinical Decision Support Systems (CDSSs), the opacity of many AI models remains a major barrier to clinical adoption, primarily due to interpretability and trust challenges. Ex...
This exploratory cluster-randomized mixed-methods study examined reading development and learner experience across AI-adaptive, teacher-differentiated, and non-differentiated EFL reading instruction. Eighty-seven university-level EFL learners, classi...
Cultural heritage interpretation requires communicating relationships among physical locations, historical narratives, and symbolic meanings while accommodating diverse visitor information needs. Conversational artificial intelligence (AI) enables fl...
Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intel...
As the performance and complexity of machine learning models have grown significantly over the last years, there has been an increasing need to develop methodologies to describe their behaviour. Such a need has mainly arisen due to the widespread use...
Well-evidenced advances of data-driven complex machine learning approaches emerging within the so-called second wave of artificial intelligence (AI) fostered the exploration of possible AI applications in various domains and aspects of human life, pr...
In response to the increasing complexity of insider threats, this study proposes an explainable AI-driven framework designed to emulate real-world analyst workflows in security operations centers (SOCs). The framework integrates ensemble learning mod...
While objective street metrics derived from imagery or GIS have become standard in urban analytics, they remain insufficient to capture subjective perceptions essential to inclusive urban design. This study introduces a novel Multimodal Street Evalua...
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Park audits can inform neighbourhood-scale urban design strategies that support health. However, interpreting park audit data is time-consuming and requires specialised expertise. Recent advances in large language models suggest potential for assisti...
This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of dec...
Artificial intelligence (AI) is increasingly adopted in manufacturing for tasks such as automated inspection, predictive maintenance, and condition monitoring. However, the opaque, black-box nature of many AI models remains a major barrier to industr...
Retaining the green workforce, employees driving sustainability and environmental innovation, is essential for organizational resilience and long-term environmental goals. While prior Green HRM research has primarily relied on survey-based methodolog...
This paper extends Rodney Brooks’ subsumption architecture into the era of Agentic AI by replacing its priority arbiter with a Generative Orchestrator that performs semantic mediation—interpreting heterogeneous agent outputs and integrati...
Deep Reinforcement Learning (DRL) has achieved remarkable success in optimizing complex control tasks; however, its opaque decision-making process limits accountability and erodes user trust in safety-critical domains such as autonomous driving and c...
Artificial intelligence (AI) is increasingly used in water research. However, many AI models, particularly complex machine learning models, often generate outcomes that are difficult for humans to interpret. Explainable artificial intelligence (XAI)...
Kolmogorov–Arnold Networks (KANs) have recently emerged as a powerful alternative to traditional Artificial Neural Networks (ANNs), offering superior accuracy and interpretability, which are two critical requirements in healthcare applications....
This work deals with the investigation and optimization of the MINDWALC node classification algorithm with a focus on its ability to learn human-interpretable decision trees from knowledge graph databases. For this, we introduce methods to optimize M...
Deep learning models are widely used in autonomous vehicle systems for perception, localization, and decision-making. However, their lack of transparency poses significant challenges in safety-critical environments. This systematic review presents a...
This paper presents OpS-EWMA-LLM (Operational State Shifts Detection using Exponential Weighted Moving Average and Labeling using Large Language Model), a hybrid framework that combines fleet-normalized statistical shift detection with LLM-assisted d...
The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for aver...
Early stroke risk prediction offers an opportunity for timely interventions and may help reduce the clinical burden associated with stroke. Artificial intelligence (AI) provides medical practitioners with tools to analyse clinical biomarkers and pred...
Artificial intelligence (AI), business intelligence (BI) and analytics are increasingly used in hotel accounting, financial reporting and performance management. Existing research, however, has concentrated mainly on prediction, automation and operat...
Background: The integration of artificial intelligence (AI) in digital mammography (DM) screening holds promise for early breast cancer detection, potentially enhancing accuracy and efficiency. However, AI performance is not identical to that of huma...
This study investigates the potential, limitations, and pedagogical implications of applying a parallel multi-model AI evaluation workflow, using ChatGPT, DeepSeek, and Uizard, to assess student design projects in higher education. Because design ass...
In the realm of intelligent sensor systems, the dependence on Artificial Intelligence (AI) applications has heightened the importance of interpretability. This is particularly critical for opaque models such as Deep Neural Networks (DNN), as understa...
Background/Objectives: Double reading with arbitration improves diagnostic reliability in fundus screening but requires repeated human interpretation. Whether artificial intelligence (AI) can serve as a first-pass decision source within this workflow...
The integration of artificial intelligence (AI) in design disciplines marks a shift in design agency, from predominantly human-centered processes toward distributed forms of Human–AI co-production. This study conceptualizes Human–AI co-pr...
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