The rapid adoption of explainable artificial intelligence (XAI) tools within open sourceMachine L[REDACTED]g (ML) ecosystems has amplified concerns regarding supply chain security. While XAI techniques enhance model transparency, their integration often relies on third‑party libraries, data pipelines, and inference services that introduce hidden vulnerabilities. This article investigates the se...
Open source artificial intelligence (AI) projects are increasingly shaping technological trajectories, yet their governance structures often remain opaque, undermining trustworthiness assessments. This article investigates how community-driven governance models affect the perceived trustworthiness of explainable AI (XAI) initiatives. We pose three research questions: (1) What governance models ...
Shadow economies impose massive revenue losses on governments worldwide, yet detecting illicit financial activity remains a persistent challenge. Traditional statistical and rule‑based methods often lack the interpretability needed for regulators to trust automated alerts. Recent advances in Explainable Artificial Intelligence (XAI) offer a pathway to illuminate decision‑making processes, enabl...
The ongoing reconstruction of Ukraine’s fiscal architecture presents a unique opportunity to reengineer the nation’s tax system for the post-war era. This article investigates how integrating empirical insights from shadow economy research can shape tax reforms that simultaneously expand the formal tax base and mitigate evasion behaviors. Drawing on a curated set of ten peer‑reviewed sources pu...
The deployment of AI systems in high-stakes domains such as healthcare, finance, and autonomous infrastructure demands rigorous specification of behavioral expectations. Existing regulatory frameworks often lack the granularity required to capture the multifaceted nature of these systems, leading to gaps between intended safety guarantees and actual operational realities. This article investiga...
Explainable Artificial Intelligence (XAI) seeks to make model decisions transparent and understandable to diverse stakeholders. However, the notion of an “acceptable” explanation remains under-specified, lacking consensus on quantitative criteria. This article formalizes explanation quality by defining three interrelated research questions: (RQ1) what fidelity thresholds guarantee faithful repr...
The manufacturing sector is undergoing a fundamental shift in how artificial intelligence influences operational decision-making. This article examines the evolution from reactive maintenance strategies—historically dominated by schedule-based or failure-driven interventions—to predictive analytics that forecast equipment degradation, and finally to prescriptive systems that dynamically optimiz...
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Humanitarian assistance is increasingly channelled through complex logistical networks that span unstable conflict zones, fragile state infrastructures, and volatile political landscapes. While digital innovations such as privacy‑preserving wallets [1], satellite‑based monitoring [2], and bio‑inspired optimisation algorithms [3] promise greater transparency and efficiency, they also introduce n...
The rapid deployment of AI-driven decision systems in production environments has intensified the demand for explanation generation that is not only semantically meaningful but also temporally bounded and resource-constrained. This article establishes a formal specification framework for real-time explainability, defining precise performance requirements for latency, fidelity, and computational...