explainable ai

A Qualitative Model for Reasoning about Path and Support

This paper introduces a hybrid qualitative model designed for Camelot Jr, a block-puzzle game that requires constructing multi-level bridges to connect two avatars stationed on separate towers, and integrates a mathematical center-of-mass stability logic to guide the qualitative solver.

Reproducibility is not construct validity: LLM measurement of institutionally situated communication

High annotation reproducibility does not necessarily imply that an LLM-inferred measure captures the construct it is intended to measure. We test this distinction using a dataset from the European Commission's AI Act consultation, linking structured survey responses to free-text consultation submissions from the same s...

Toward Socially Accountable Data Science Education: Proposing a Conceptual Framework for Integrating Explainable AI (XAI) and Accountability Principles

As artificial intelligence systems increasingly serve as a gateway for access to information, economic opportunity, and civic life, higher education programs training AI developers must evolve to prepare practitioners who are not only technically proficient in building AI solutions but also socially accountable in thei...

Just Explain It to Yourself: On Integrating Self-Explanations into Explainable AI

Diagnostic accuracy of AI-assisted versus independent physician interpretation for bone fractures: a systematic review and meta-analysis

We systematically evaluated the diagnostic performance of artificial intelligence (AI)-assisted interpretation versus independent physician assessment for fracture detection. Adhering to PRISMA-DTA guidelines, we searched PubMed and Web of Science for original studies published up to September 17, 2025. Quality was ass...

Effects of Generative AI, Digital Leadership, and Teacher Professional Development on Multi-dimensional Student Development

Generative artificial intelligence (GAI), digital leadership, and teacher professional development affect multi-dimensional student growth in Xinzheng, China, senior high schools. Although the level of digital infrastructure investment is high, a large number of inland schools suffer an input-output paradox, in which t...

Morphology-Aware Lightweight Deep Learning for Explainable Rice Variety Classification

This research shows a lightweight convolutional neural network (CNN) framework for automatic rice variety classification. The proposed model is multi-branch modules of Inception style, res connection, channel – spatial attention mechanism to improve feature extraction without increasing the architecture compact and eff...

HUMAN - AI COLLABORATION IN SECURE SYSTEM DEVELOPMENT: DECISION PROCESSES AND ORGANIZATIONAL IMPLICATIONS

Artificial intelligence is increasingly embedded in secure software development, yet its impact on security outcomes depends less on technical capability than on the socio-technical integration of its outputs. This paper examines human - AI collaboration, focusing on the critical interface between automated decision-su...

Bridging Legal, Financial, and Data Governance in Enterprise AI: Emerging Trends

A conceptual crosswalk is proposed that maps controls across domains onto shared AI lifecycle stages, exposing redundancy and gaps and set out a research agenda for integrated governance that treats legal, financial, and data controls as a single accountable system rather than three parallel ones.

AI Chatbot Service Adoption Among C2C Second-Hand Sellers: An Integrated UTAUT-TTAT Framework

An integrated dual mediation model is constructed that reconciles conflicting psychological evaluations of intelligent systems, deepens the understanding of paradoxical adoption behaviors, and offers practical implications for optimizing intelligent services within C2C second-hand ecosystems.

Human-In-The-Loop Decision Systems: Advances and Future Directions

It is argued that treating the human and the model as a single joint cognitive system is the central design principle for the next generation of decision systems.

Hybrid Machine Learning Model for Phishing Detection Using Multi-Modal Data

A hybrid machine learning model that leverages multi-modal data—including textual URL features, HTML content, webpage screenshots, and metadata—to achieve robust and accurate phishing detection is proposed, highlighting the efficacy of multi-modal fusion in overcoming limitations of unimodal approaches.