explainable ai

Prostate Commander – PROSTATEx validation

Preregistration of a diagnostic accuracy study (STARD 2015) of Prostate Commander, a rule-based, explainable implementation of PI-RADS 2.1 for prostate MRI (AI used only for gland segmentation; ADC from measured b-values; rule-based lesion detection and categorisation). Index test: frozen software (11 Oct 2026, SHA-256...

Structured PREreview of "HE-Guardrail: A Homomorphic Guardrail Against Jailbreak Attacks for Encrypted Large Language Model Inference"

This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/23289740. Does the introduction explain the objective of the research presented in the preprint? Yes The introduction makes the problem clear early on: because the server...

Towards Trustworthy Intelligent Tutoring Systems for Nursing Education: Integrating Nurse-Informed Design with a Frugal Neuro-Symbolic Architecture

Nursing education is increasingly expected to provide personalised, scalable, and clinically meaningful learning experiences while responding to educator shortages, growing student numbers, limited placement opportunities, and rapidly evolving healthcare environments. These pressures have led to the adoption of technol...

Explainable deep learning for predicting lignin removal efficiency in deep eutectic solvent-based biomass fractionation

An extended theory of planned behavior model of generative AI use intention for music composition among Chinese music students

Findings suggest that educators could support students in developing calibrated judgments of GenAI reliability, evaluating its suitability for specific composition tasks, and addressing AI-related anxiety while preserving authorship and creative agency.

Beyond Technocentrism in AI-Supported Special Education: The PAISEN Model of Teacher-Mediated Inclusive Learning

Artificial intelligence (AI) offers new possibilities for inclusive teaching and learning for students with special educational needs (SEN), yet AI-in-SEN research remains largely technocentric, often describing system capabilities without explaining how outputs become pedagogically meaningful. This conceptual paper de...

Evaluating and explaining LLM classification for automatic screening in Systematic Reviews

Evidence synthesis methods, such as systematic reviews (SR), require research teams to find, select, evaluate, and synthesize all available evidence to answer a specific research question in a reproducible and transparent, bias-reduced manner. Early in the process, teams must screen all unique titles and abstracts gath...

Provenance-Aware Hybrid Search in AI-Mediated Digital Libraries

A provenance-aware hybrid search framework for digital libraries that connects digital library content and metadata with hybrid retrieval and AI synthesis, provenance-aware interface design, and user interaction is proposed.

Development and psychometric evaluation of the BERTA Artificial Intelligence Addiction Scale (B-AIAS)

Artificial intelligence (AI) technologies are increasingly integrated into daily life. While offering significant benefits, their excessive use may trigger problematic patterns resembling behavioral addictions. Despite these concerns, no standardized instrument currently exists to assess AI-related addictive tendencies...

Examining Racial and Gender Bias in CLIP via Explainability-Based Visual Attention Analysis

Vision-language models (VLMs) such as CLIP are increasingly integrated into automated decision-making systems where unexamined social biases can directly harm racial and gender minorities through discriminatory outcomes and unjust content censorship. Despite strong multimodal performance, CLIP's susceptibility to demog...

Higher A-Homotopy Vanishing for Coxeter Chamber Graphs

We prove that the standard Cayley graph of every finite-rank Coxeter system has trivial A-homotopy groups in all degrees at least two. The argument replaces the Coxeter group by the right-angled Coxeter group that retains exactly its commuting relations. All short closed walks lift to the resulting cover, while every f...

Scalable and Explainable AI Architectures for Intelligent Decision-Support Systems: Systematic Literature Review Protocol

Protocol of a systematic literature review, registered before the searches are run. The review maps how AI-based decision-support systems integrate data pipelines, machine-learning and relational inference, explainability, decision-support interaction, and human oversight, to ground a reference architecture for scalabl...