Abstract: XAI (Explainable Artificial Intelligence) has matured into a cross-disciplinary field where domain-specific standards are essential for regulatory compliance, stakeholder trust, and operational safety. While generic XAI techniques provide post-hoc explanations, industry sectors have distinct governance requirements, data sensitivity constraints, and risk tolerance levels that demand t...
Explainable Artificial Intelligence (XAI) has emerged as a critical requirement for trustworthy AI systems, yet current approaches often treat explanations as afterthoughts rather than first-class outputs of the development process. This article proposes a specification framework for XAI that treats explainability requirements as formal specifications alongside functional requirements. We addre...
The rapid adoption of artificial intelligence (AI) systems across industries has created an urgent need for auditing practices that can effectively evaluate these complex models. Traditional auditing approaches often fall short when assessing AI due to their opacity and dynamic behavior. Explainable Artificial Intelligence (XAI) offers a pathway to bridge this gap by providing interpretable ins...
Explanation-centric human-AI workflows impose hidden operational costs that are often overlooked in productivity assessments. This article examines the cost structure of maintaining explanation quality in decision-support systems, focusing on trade-offs between explanation fidelity, latency, and human cognitive load. We analyze recent empirical studies from 2025-2026 to quantify three primary c...
As enterprise AI systems proliferate across regulated industries, the choice between inherently interpretable models and post-hoc explanation techniques for complex black-box models carries significant operational, compliance, and financial implications. This article presents a comparative analysis of the total cost of ownership (TCO) for interpretable models versus post-hoc explanation approac...
Explainable Artificial Intelligence (XAI) tools are increasingly deployed to provide transparency in machine l[REDACTED]g models, yet their economic viability remains poorly understood. This article analyzes the compute and engineering costs associated with generating explanations at scale across three prominent XAI methodologies: feature attribution, counterfactual generation, and prototype-ba...
Enterprise AI procurement faces a critical dilemma: build custom solutions for tailored explainability or buy off-the-shelf platforms with faster deployment but limited transparency. This article analyzes the economic trade-offs in AI sourcing decisions when explainability requirements are paramount, drawing on the IEEE 3119-2025 standard for AI procurement and recent empirical studies. Our ana...
As AI systems increasingly operate in production environments, ensuring the reliability of model explanations becomes critical for trust and accountability. This article presents a framework for monitoring explainability drift—the degradation of explanation quality over time—in deployed machine l[REDACTED]g models. We define explainability drift as a measurable divergence between expected and o...
As AI-driven predictive maintenance (PdM) systems become integral to smart manufacturing operations, ensuring the quality and reliability of their explanations is critical for safety, compliance, and operational trust. This article extends the AI observability framework to manufacturing AI systems, focusing on explanation quality monitoring in predictive maintenance contexts. We define a specia...
The Universal Intelligence Benchmark (UIB) proposes an eight-dimensional, cost-normalized framework for measuring intelligence across diverse AI systems. This article operationalizes the second UIB dimension — Embodied Intelligence (Dembodied) — defining it as the capacity for intelligent behavior arising from physical interaction with an environment, encompassing spatial reasoning, physics und...