The increasing integration of artificial intelligence (AI) in educational decision-making raises a critical question: how to design AI systems that can effectively support teachers while maintaining an appropriate level of trust. Addressing this ques...
Purpose: Explainable artificial intelligence (XAI) is increasingly promoted as a mechanism for improving transparency, trust, and accountability in AI-assisted decision-making. However, its ethical value remains uncertain when AI systems are embedded...
This paper investigates adversarial robustness and explainability in AI-generated face detection through the Robust and Explainable Detection (RED) framework. RED unifies CNN and Vision Transformer (ViT) backbones with FGSM/PGD adversarial training a...
Leveraging Artificial Intelligence (AI) ethically in connected healthcare systems requires a quantifiable framework that measures not only outcome correctness, but also the clarity, auditability, and ethical acceptability of model explanations in hig...
The integration of artificial intelligence (AI) in the financial sector has seen a rapid increase over the past few years, offering new possibilities to streamline processes while ensuring profitability for lending institutions. With its data-driven...
The Architecture, Engineering, and Construction (AEC) sector is increasingly adopting Artificial Intelligence (AI) technologies to enhance efficiency, decision-making, and operational performance across infrastructure and construction workflows. The...
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, driving the need for reliable tools for early risk prediction. Artificial intelligence (AI) applied to electrocardiograms (ECGs) has shown strong predictive performance f...
Background: The integration of Artificial Intelligence (AI) into Business Process Management Systems (BPMSs) has led to the emergence of AI-Augmented Business Process Management Systems (ABPMSs). These systems offer dynamic adaptation, real-time proc...
Artificial intelligence is increasingly embedded in consumer-facing FinTech, but trust in AI-enabled finance depends not only on performance, but also on whether users can understand and appropriately evaluate algorithmic outputs. This review synthes...
Agentic AI can coordinate distributed energy resources, invoke tools, revise plans, and act through multiple interacting agents. In critical energy systems, however, model-level explainable artificial intelligence (XAI) cannot reveal how goals, const...
The integration of artificial intelligence into criminal sentencing decisions represents one of the most consequential applications of algorithmic systems in contemporary governance. While AI-assisted risk assessment tools promise enhanced consistenc...
Background: Large language models and other generative artificial intelligence (AI) tools are increasingly being embedded in digital healthcare services, including mobile health applications, telerehabilitation, remote monitoring, and hybrid care pat...
Objectives: Alzheimer’s disease (AD) remains one of the most prevalent neurodegenerative conditions among older adults, underscoring the urgent need for accurate and ethically grounded early detection methods. Artificial intelligence (AI) techn...
Industry 5.0 promotes human-centric industrial safety, yet it remains unclear whether AI-based risk-prevention systems support meaningful human decision-making or merely leave humans nominally in control. Following PRISMA-ScR, this scoping review exa...
The integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic capabilities, yet the black-box nature of deep learning models poses significant challenges for clinical adoption. Current explainable AI (XAI) approache...
Background/Objectives: Artificial intelligence (AI) systems for detecting lymph node metastases in histopathological images achieve near-expert classification performance but remain opaque to clinicians, limiting their clinical adoption and regulator...
Rapid advances in artificial intelligence are transforming high-stakes fields like medicine and education while raising pressing ethical challenges. This paper introduces the Ethical Firewall Architecture—a comprehensive framework that embeds m...
The rapid development of artificial intelligence in recent years has been driven primarily by engineering advances, while the philosophical foundations of the concept of “intelligence” itself have been reconsidered much more slowly. This...
European law now requires AI to be explainable in the context of adverse decisions affecting the European Union (EU) citizens. At the same time, we expect increasing instances of AI failure as it operates on imperfect data. This paper puts forward a...
Generative Artificial Intelligence (GenAI) models produce increasingly sophisticated outputs, yet their underlying mechanisms remain opaque. To clarify how explainability is conceptualized and implemented in GenAI research, this two-stage review syst...
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...
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...
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...
Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by struc...
Sunflower is a crop that has many economic values and ornamental usages. However, its production can be hampered due to various diseases such as downy mildew, gray mold, and leaf scars, and it is challenging for farmers to identify disease-prone cond...
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...
Artificial intelligence (AI) features in FinTech-enabled digital banking support financial decisions but also introduce technological risks, such as black-box reasoning and erroneous recommendations. Studies often treat customer-facing AI as a single...
Exposure to pollutants like ozone and nitrogen dioxide gas can cause serious health issues and harm the environment. Therefore, the interest in air quality and its impact on health and well-being has been steadily increasing over the years, making lo...
17 May 2024
As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fos...
Artificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified...
Research into conversationally explainable artificial intelligence (CXAI) aims to emulate the interactive and co-constructive nature of explanations. From the perspective of human-centredness, previous work has shown that AI users prefer conversation...
This research proposes a comprehensive framework for implementing explainable and transparent artificial intelligence (XAI) sensors in healthcare, addressing the challenges posed by AI “black boxes” while adhering to the European Union (E...
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...
Human–AI collaboration (HAIC) increasingly mediates high-risk decisions in public and private sectors, yet many documented AI harms arise not only from model error but from breakdowns in joint human–AI work: miscalibrated reliance, impair...
Artificial intelligence (AI) with deep learning models has been widely applied in numerous domains, including medical imaging and healthcare tasks. In the medical field, any judgment or decision is fraught with risk. A doctor will carefully judge whe...
The study introduces an AI-enhanced Environmental, Social, and Governance (ESG) framework that integrates explainable artificial intelligence (XAI) and bias-mitigation techniques to improve transparency and comparability of sustainability assessments...
As artificial intelligence (AI) systems are increasingly adopted in recruitment practices, applicants’ responses to AI-mediated interviews have become an important issue for organizations. Understanding how applicants interpret these systems is...
On 21 April 2021, the European Commission proposed the first legal framework on Artificial Intelligence (AI) to address the risks posed by this emerging method of computation. The Commission proposed a Regulation known as the AI Act. The proposed AI...
Currently, explainability represents a major barrier that Artificial Intelligence (AI) is facing in regard to its practical implementation in various application domains. To combat the lack of understanding of AI-based systems, Explainable AI (XAI) a...
Alzheimer’s disease (AD) is the most common cause of dementia, marked by cognitive decline and memory loss. Recently, machine learning and deep learning techniques have introduced promising solutions for improving AD detection through MRI, espe...
Generative artificial intelligence tools are reshaping design by enabling novice designers to produce professional-quality user interfaces rapidly. However, for novice designers, exposure to AI-generated outputs that are far beyond their capabilities...
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...
Generative Artificial Intelligence (GenAI) is widely recognized for its profound impact on labor market demand, supply, and skill dynamics. However, due to its transformative nature, GenAI increasingly overlaps with traditional AI roles, blurring bou...
11 January 2024
Typical state-of-the-art flow cytometry data samples typically consist of measures of 10 to 30 features of more than 100,000 cell “events”. Artificial intelligence (AI) systems are able to diagnose such data with almost the same accuracy...
This study delves into the realm of Explainable Artificial Intelligence (XAI) frameworks, aiming to empower researchers and practitioners with a deeper understanding of these tools. We establish a comprehensive knowledge base by classifying and analy...
An important impediment to the incorporation of artificial intelligence-based tools into healthcare is their association with so-called black box medicine, a concept arising due to their complexity and the difficulties in understanding how they reach...
Artificial Intelligence (AI) shows significant potential across healthcare domains, including advanced diagnostics, clinical decision support, and personalized medicine. Despite these advancements, the opaque ‘black box’ nature of complex...
The rapid adoption of generative artificial intelligence tools, particularly ChatGPT, is transforming teaching and learning in higher education. This study proposes an explainable artificial intelligence (XAI) framework that integrates the Unified Th...
Modern explainable AI (XAI) methods remain far from providing human-like answers to ‘why’ questions, let alone those that satisfactorily agree with human-level understanding. Instead, the results that such methods provide boil down to set...
With the increasing adaptability and complexity of advisory artificial intelligence (AI)-based agents, the topics of explainable AI and human-centered AI are moving close together. Variations in the explanation itself have been widely studied, with s...
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