explainable ai

Agentic AI at the Edge: Self-Explaining Autonomous Framework for IoT Incident Response

A fully local, autonomous incident-response framework for edge gateways that combines a compact classifier that routes traffic by confidence, a tool-using language-model agent restricted to vetted mitigation actions, and an Explanation Engine grounded in the agent's recorded observations is presented.

Real-Time RAN Observability at the Far Edge: AI on the Fronthaul

This work recovers DU-side scheduling behavior and radio-side execution, including the per-beam and per-layer beamforming weights carried on the C-plane, separating measured quantities from those conditioned on an array hypothesis, and ground an eleven-agent platform in which a language model only interprets measuremen...

EIO-Agents: The Missing Semantic Layer for AI Agent Evaluation

This work introduces EIO-Agents, an open specification for interoperable AI agent evaluation built on two layers: EIO provides that missing semantic contract, while PER preserves the resulting evaluation as a portable and verifiable system of record.

Behavior-Mining, Generative Conversations, and Collaborative Advisory: the Future of Travel and Tourism Recommender Systems

It is claimed that future TTRSs, in addition to offering personalized information filtering, should become more flexible advisors that support decision making, integrating multiple data types and AI techniques, from data mining to natural language processing.

BARE-AI: Bit-Flip Attack Resilience in AI Hardware through Built-in Performance Monitors

BARE-AI, a runtime framework that detects, localizes, and mitigates BFAs during inference, and introduces AI Performance Counters, lightweight hardware monitors in the accelerator datapath that capture per-layer activation statistics such as sparsity, entropy, kurtosis, and spectral shift.

Learning to Explain Solutions of Optimal Control Problems

The results show that the GNN model can accurately predict the optimal values of the manipulated variables, and application of explainable AI algorithms reveals equality and inequality constraints that are the most important for predicting the optimal solution.

Mapping AI Ethics Integration in Postgraduate Health Professions Education: A Scoping Review Through the Lenses of Principlism and Transformative Learning.

This study maps how AI ethics is taught within postgraduate/CPD HPE, drawing on principlism, an ethical framework espousing the principles of autonomy, beneficence, non-maleficence and justice and transformative learning theory (TLT), which explains how critical reflection transforms professional assumptions, perspecti...

We Need Explanation Cards to Connect Explanation Algorithms to the Real World

Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short. First, the meaning of algorithmic explanations is often not what one might intuitively expect, so expert knowledge is required to interpret them correctly. Second, recent work has...

FlowCF: Sparse Counterfactual Explanations for Mixed-Type Tabular Data using Flow Matching

In the field of Explainable AI (XAI), counterfactual (CF) explanations interpret a model's decision by suggesting the changes to the input that would lead to a more favourable outcome. To be useful in practice, such an explanation should change few features and change them as little as possible, properties known as spa...

From Research Gaps to Theoretical Opportunities: Theory-Oriented GenAI for Research Opportunity Evaluation

A theory-oriented agentic AI system that helps researchers identify potential theorizing opportunities by incorporating established theorizing approaches into literature exploration and evaluation and demonstrates the system through an end-to-end analysis of human oversight of agentic AI systems in organizations.

ARTIFICIAL INTELLIGENCE IN MEDICAL EDUCATION AND CLINICAL PRACTICE: A NARRATIVE REVIEW

AI delivers its greatest value when deployed as a collaborative instrument that augments rather than replaces clinical judgment, which depends on sustained investment in digital literacy, explainable systems, clinician involvement in design, and equitable institutional infrastructure.

Strategically timed UAS data acquisition for wheat yield prediction using explainable AI: A multi-year phenology-aligned study