Abstract The rapid integration of artificial intelligence into knowledge work demands new frameworks for human-AI collaboration that go beyond opaque black-box decision-making. Recent advances in explainable AI (XAI) offer tools to make model behavior transparent, thereby fostering trust, accountability, and shared understanding. This article investigates how explainability mechanisms can be ...
Government agencies are increasingly looking to artificial intelligence (AI) to modernize procurement workflows, strengthen fraud detection pipelines, and improve the delivery of public services while operating under tight budgetary constraints. Recent surveys reveal that more than 65 % of public‑sector technology officers consider open source AI components essential for achieving cost‑efficien...
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The rapid deployment of automated decision-making systems in high-stakes domains demands robust mechanisms for [REDACTED]g user trust. This article introduces the Trust Architecture, a systematic framework for designing AI systems that earn explainability-based trust through alignment of explanation quality, decision stakes, and user context. We formulate three research questions concerning met...
Explainability in artificial intelligence remains a critical barrier to adoption in safety‑critical domains such as healthcare, finance, and autonomous systems. While many commercial platforms tout built‑in interpretability, they often lock users into proprietary ecosystems and obscure the underlying model internals. This article presents a fully open source stack that enables reproducible, aud...
The rise of AI-driven gig platforms has dramatically altered informal labor ecosystems, creating new shadow market dynamics that traditional economic models fail to capture. This article investigates how platform design choices directly reshape worker vulnerability, income stability, and social protection gaps in emerging economies. We demonstrate that platform-mediated work arrangements are no...
Government procurement processes are vulnerable to corruption, inefficiency, and opaque decision‑making. This article presents an explainable artificial intelligence (XAI) framework for detecting corruption patterns in public procurement datasets, focusing on invoice analysis and contract anomaly detection. Using a combination of statistical feature extraction, graph‑based anomaly scoring, and ...
Post-deployment monitoring of explainable AI (XAI) systems has emerged as a critical concern for maintaining trustworthy AI behaviors over time [1]. While pre-deployment validation establishes baseline explanation quality, it does not guarantee sustained performance when models encounter distribution shifts, concept drift, or evolving user expectations [2]. This article addresses the research g...
The integration of artificial intelligence (AI) into educational environments is reshaping how l[REDACTED]g is delivered, assessed, and accessed. Recent advances in adaptive l[REDACTED]g systems, automated grading, and AI-driven analytics promise significant improvements in personalization, efficiency, and equity. However, the extent to which these technologies can universally transform educati...
Accurate and reproducible evaluation of explanation fidelity is essential for advancing XAI research. While several metrics have been proposed, no standardized benchmark framework exists that enables systematic comparison across methods. This article presents an open-source benchmark suite designed to assess explanation quality across multiple XAI techniques. Drawing on recent literature [1], w...