explainable ai

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

Artificial intelligence driven framework for enhancing sustainability and resilience in communal cattle insurance in Lupane District Zimbabwe

Communal cattle farming in Sub-Saharan Africa continues to face destabilization from non-linear climate variability, yet insurance penetration remains negligible. In Lupane District, Zimbabwe, uptake is exceptionally low at approximately 0.01%, despite recurrent droughts that threaten household livelihoods. This study...

ECHO: Explainable Co-editing with Human-in-the-loop Operations for Presentation Slide Refinement

Authoring and refining presentation slides is time-consuming in academic and professional settings. Although generative AI lowers the barrier to creating initial drafts, its black-box, one-way workflow often limits fine-grained control. A formative study with 10 frequent presentation authors identified trial-and-error...

Teaching Data Cleaning in Machine Learning: A Misconception-Driven Pedagogical Framework for Explainable and Ethical Data Preprocessing

This paper proposes a four-phase instructional sequence — Motivational Grounding, Mechanism Diagnosis, Algorithm-Context Mapping, Algorithm-Context Mapping, and Explainability and Ethical Reflection — operationalized through a multi-dimensional Instructor Cleaning Strategy Matrix, embedding explainability and ethical r...