explainable ai

Foundations of Human-Centric AI and Machine Learning for Construction 6.0

The foundations of Human-Centric Artificial Intelligence and Machine Learning as enablers of Construction 6.0, emphasizing the integration of automation, cognitive systems, and human expertise are examined. The chapter outlines how Artificial Intelligence is transforming architecture, engineering, and construction thro...

A context-aware multimodal multi-agent deep reinforcement learning framework for autonomous personalized education

This paper introduces the Context-Aware Multi-Agent Deep Reinforcement Learning (CA-MA-DRL) framework for personalised digital education, shifting from passive analytics to autonomous decision-making agents. The framework integrates Multimodal Learning Analytics with advanced coordination mechanisms, fusing heterogeneo...

Human-Centric AI-Driven Sustainable and Resilient Design AI for Energy-efficient and Carbon-neutral Buildings

This chapter examines how Human-Centric Artificial Intelligence is transforming sustainable and resilient design in the built environment. With buildings responsible for nearly 40% of global energy use and 30% of greenhouse gas emissions, the chapter underscores the need for energy-efficient and carbon-neutral architec...

Physical‑Intuition‑Driven Nonlinear Ignition‑Threshold Scaling and Shell‑Aspect‑Ratio Regime Separation for 1D ICF via TreeSHAP Explainable AI

Laser-driven inertial-confinement-fusion (ICF) achieves thermonuclear ignition via spherical capsule compression, yet high-fidelity multi-dimensional radiation-hydrodynamic simulations demand prohibitive computational resources. The ignition-threshold-factor (ITF) quantifies ignition margins to constrain target-design...

Toward the Alignment of Understandability, Reasoning, and Explainability in TEL

An Integrated Explainable AI and Universal Design for Learning (UDL) Framework for Inclusive Special Education Systems

Algorithmic Bias in AI Marketing: Implications for Consumer Equity and Brand Reputation

Artificial intelligence (AI) is increasingly embedded in targeting, personalization, pricing, recommendation, and content-generation systems, intensifying concerns about algorithmic bias and its consequences for consumers and brands. This critical literature review synthesizes peer-reviewed and applied research publish...

TrustmeWatcher: An Application for Workplace Micro-Sensing and Explainable Well-Being Feedback

The result is a deployed application and study workflow that integrates activity review, study data collection, privacy control, local prediction, and a participant-facing interface for XAI evaluation.

Toward AI Trustworthiness: Finding Analytically Proven Forward-Invariant Sets for AI-Controlled Systems

Neural-network (NN) controllers are increasingly used in nonlinear control systems, but their highly nonlinear behavior makes them difficult to explain and verify, raising trustworthiness concerns in safety- and mission-critical applications. A key step toward certifiable trustworthiness is to find a Forward-Invariant...

A Unified and Interpretable Geometric Optimization Framework for Intelligent Automated Implant Planning in CBCT from Single to Consecutive Multiple Missing Teeth

A unified explainable framework for cone-beam computed tomography (CBCT) that handles both single-tooth and consecutive multiple missing teeth and offers an interpretable and clinically trustworthy solution for computer-aided implant dentistry.

Performance analysis of vertical patterned glass BIPV in zero energy buildings using explainable AI

The Architecture of AI Governance in Banking: Bridging Compliance and Explainability Gaps

A coordinated governance strategy integrating technical validation, regulatory alignment, traditional risk management, documentation, and necessary human oversight can enhance accountability while addressing the real-world constraints of complex AI systems in regulated banking settings.