Armed conflict fundamentally alters the structural dynamics of informal economies, yet systematic quantification of these transformations remains fragmented. This article investigates how prolonged armed conflict in Ukraine has reshaped shadow economy patterns, focusing on three interlocking dimensions: (1) the quantitative expansion of informal market activity, (2) the emergence of novel trans...
The next four years will witness a profound reconfiguration of economic and organizational ecosystems as artificial intelligence matures from experimental pilots to core infrastructure [1] [2]. This paper maps the intensity of AI-driven transformation across twelve principal industry verticals, identifying where disruptive potential aligns with measurable efficiency gains and where regulatory c...
Explainability (XAI) systems are increasingly deployed in safety-critical domains, yet their vulnerability to adversarial manipulation threatens trust and decision integrity. This article investigates the adversarial robustness of specification-based XAI mechanisms, focusing on how malicious inputs can subvert explanatory outputs without altering the underlying model behavior. We pose three cor...
Explainable AI (XAI) has matured from exploratory research into a production-critical capability for high-stakes machine l[REDACTED]g systems. This article conducts a systematic trust analysis of the four most widely adopted open-source XAI libraries: SHAP, LIME, DiCE, and Alibi. We frame the inquiry around three research questions: (1) How do these libraries compare across community activity, ...
Blockchain technology presents a transformative opportunity for tax administration, particularly in automating Value-Added Tax (VAT) collection through programmable smart contracts.[1] This article systematically investigates architectures for smart contract-enabled VAT compliance, addressing the critical need for reliable, transparent, and efficient tax reporting mechanisms in decentralized fi...
Explainable Artificial Intelligence (XAI) has traditionally relied on post‑hoc approximations such as SHAP and LIME to interpret complex models. While these methods have been influential, their assumptions and limitations are increasingly e[REDACTED]sed by modern AI paradigms, including large language models (LLMs), diffusion systems, and causal reasoning frameworks. This article asks three cen...
Explainable AI (XAI) systems generate explanations to justify model decisions, yet current standardization efforts lack coherent specifications for explanation formats. This article establishes a rigorous framework for XAI interoperability, defining mandatory components for explanation formats that ensure technical compatibility and functional validity across diverse deployment contexts. We ana...
Explainability compliance in artificial intelligence systems demands rigorous evaluation methodologies that can verify whether AI models adhere to predefined specification criteria. This article introduces specification‑based testing (SBT) as a systematic approach to assess AI transparency, focusing on how well model outputs conform to declared functional and ethical constraints. We outline a r...
Explainable artificial intelligence (XAI) seeks to make model decisions transparent, yet existing approaches often produce explanations that are themselves opaque or unverified. Formal verification offers a rigorous mathematical framework to certify that an explanation accurately reflects the underlying model computation. This article investigates how formal methods can be applied to XAI to gen...
The rapid deployment of artificial intelligence systems across high‑risk domains has prompted regulators to demand greater transparency and accountability. The European Union’s Artificial Intelligence Act (EU AI Act) introduces a comprehensive framework for trustworthy AI, with particular emphasis on explicability obligations for high‑risk AI systems. This article dissects the technical specifi...