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explainable ai

2,810 papers

#explainable ai Open access Sep 2026

PREreview of "The widening evaluation gap in medical large language model research 2023 to 2026"

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/22734670. What the paper claims This is an evidence map of 11,628 PubMed-indexed records on generative language models in healthcare, January 2023 to June 2026, built to answer one...

Evgeny V. Arsentyev · 0 citations
#artificial intelligence Open access Sep 2026

Clinical usability of an explainable AI decision support tool and evaluation of multimodal models in NSCLC

In a large international real-world study of non-small cell lung cancer, a multimodal explainable AI model outperformed established biomarkers for immunotherapy outcome prediction and improved physician decision-making.

A. Prelaj, V. Mišković, Matteo Sacco et al. · 1 citation
#artificial intelligence Review Sep 2026

AI transparency and bias mitigation in customer service: how ethical culture shapes customer trust

AI transparency and bias mitigation are reframed as governance and organizational control mechanisms, rather than mere technical features, which demonstrates that organizational ethical culture acts as a key informal context, shaping whether customers view these mechanisms as credible signals of fairness and accountabi...

M. Ehsan, Abid Hussain, Jing Song et al. · 0 citations
#explainable ai Open access Sep 2026

"Mortality Prediction in Heart Failure using Explainable AI: Development and Validation of the EXACT-HF Model in a Nationwide Multi-Ethnic Asian Cohort"

In external validation, interpretable models substantially outperformed established HF risk scores for short- and long-term mortality and simultaneously localised correctable therapeutic gaps, supporting a prediction-to-action tool deployable with ten bedside variables.

S. M. Jauhari, Wai-Lok Yeung, T. Li et al. · 0 citations
#artificial intelligence Open access Jul 2026

Self-Verifying Anomaly Detection using Explainable AI for Cybersecurity of DER Networks

An XAI-based anomaly detection framework tailored for DER networks (ExCYDER) that distinguished between coherent and inconsistent alerts without compromising detection accuracy, demonstrating that integrated verification within XAI-based ADS enhances interpretability, auditability, and operational robustness for DER-fo...

D. Popoola, S. Bhattacharya, M. Govindarasu · 1 citation
#explainable ai Open access Sep 2026

Climate Risk Prediction System

This paper presents ClimateGuard, a localized, explainable, multi-hazard climate risk prediction system integrating satellite-derived geospatial indicators, real-time meteorological data, and machine learning for climate hazard forecasting. The system combines Random Forest and XGBoost models with environmental and dis...

Zainab Travadi · 0 citations
#explainable ai Review Open access Sep 2026

Artificial Intelligence in Forensic Medicine and Medicolegal Practice: Applications, Limitations, and Readiness for Implementation

Machine learning and deep learning approaches may improve efficiency, reproducibility, and pattern recognition in several forensic workflows, particularly postmortem imaging, skeletal age estimation, mass spectrometry-based toxicology, biometric identification, digital pathology, and natural language processing of medi...

Emre Yuluğ · 0 citations
#explainable ai Open access Sep 2026

Channel Theory for Polynomial Continued Fractions: Asymptotic Channels, the ξ₀ = 2/√β₂ Identity, and a Bridge Conjecture

Correction notice (v1.5). This version demotes a claim. Clause (iii) of Theorem 3.7 -- the match between the CC-channel connection-coefficient ratio and the Riemann--Hilbert datum of P_V/D_5^(1) at the V_quad tau parameter -- was stated as part of a theorem through v1.4. It is not a theorem of this paper and is recorde...

papanokechi papanokechi · 0 citations
#explainable ai Open access Sep 2026

Data Issues in AI Applications for Public Health — An Application of the C-E-A Framework: Compliance, Efficacy, and Autonomy

Data concepts relevant to AI applications in public health — such as availability, relevance, and representativeness — are usually explained in their general, basic sense for a broad readership of engineers and public health professionals alike, without being organized by what kind of practical failure each one represe...

Min Wu · 0 citations
#explainable ai Open access Sep 2026

Riemann zeta function: Split-Zero arithmetic comparison and research readers

This collection records a literature-linked research programme on the Riemann zeta function and its explicitly constructed geometric, arithmetic and fluid interfaces. The September 12 calculation edition adds a complete reader for the Split-Zero programme: scalar algebra, support fibres, Rees filtrations, the actual ar...

KokunoYumeto · 0 citations
#explainable ai Open access Sep 2026

Intelligent and Non-Intelligent Supervisory Data-Driven Tools: A General Legal Framework for Proportionate Oversight and Reciprocal Control within the Theory of Data-Driven Tools, with Application to AI-Assisted Competition Law Enforcement

The expanding use of intelligent and non-intelligent data-driven tools across technology-mediated environments has improved operational efficiency while increasing the legal significance of errors in data acquisition, processing, state detection, and automated execution. Building on the “Theory of Intelligent and Non-I...

Mohammad Mahdi Varposhti Borujeni · 0 citations
#explainable ai Review Open access Sep 2026

Geoai-Driven Tourism Planning: A Framework And Educational Implications

The GeoAI Tourism Intelligence Framework (GTIF), a conceptual framework integrating geospatial big data, AI algorithms, and spatial analytics to support evidence-based destination planning and management, is presented.

Huseyin Kaya · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Oct 30, 2026

Q&A: Hey AI, who should I vote for this year?

MIT professors Adam Berinsky and Chara Podimata discuss a new project to track how AI chatbots are responding to questions about the 2026 U.S. midterm elections.

Microsoft Research Blog Oct 30, 2026

From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence

Evaluating Microsoft Copilot at enterprise scale requires test data that captures the full complexity of real organizations, yet real enterprise data contains sensitive information that cannot be used for evaluation under any circumstances. The post From synthetic enterprise data evaluations to real Microsoft Copilot AI confidence appeared first on Microsoft Research.

GPT-Lab Oct 30, 2026

Black October? The Week AI Changed the Cybersecurity Conversation

Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.

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