Skip to content

Category

explainable ai

2,807 papers

#artificial intelligence Review Open access Sep 2026

Artificial intelligence for PFAS toxicology and risk assessment

The regulatory-readiness framework is expanded to incorporate OECD QSAR validation principles, chemical data curation, descriptor generation, applicability-domain assessment, scaffold-aware and prospective validation, explainability beyond SHAP, calibrated uncertainty analysis and AOP-linked mechanistic interpretation.

Abir Hamze, Emran Alotaibi · 0 citations
#artificial intelligence Open access Sep 2026

🧬 TRI-COUPLING SYSTEM EVOLUTION THEORY: PRACTICAL APPLICATION TECHNOLOGIES AT THE LIMIT From a Small Personal Experiment to an Adaptive, Falsifiable, and Transmissible Research Architecture

🧬 TRI-COUPLING SYSTEM EVOLUTION THEORY:PRACTICAL APPLICATION TECHNOLOGIES AT THE LIMIT From a Small Personal Experiment to an Adaptive, Falsifiable, and Transmissible Research Architecture Preface The tri-coupling system did not begin as a grand theory. It began as a small personal experiment. The original question wa...

33 · 0 citations
#artificial intelligence Open access Sep 2026

How AI and technology’s skill-bias impact the delayed retirement desire in China

China has introduced delayed retirement policies to sustain its pension system, threatened by population aging. We present theories to argue that such policies, if applied universally across the private and public sectors, may ironically exacerbate the problem by increasing rent-seeking, which decreases output and ta...

Zhuo Zhang, D. Bandyopadhyay · 0 citations
#artificial intelligence Open access Sep 2026

Mechanism-informed machine learning for interpretable drug release prediction and virtual formulation intervention in polymeric long-acting injectables

The correct time-course forecasting of the release of a drug contained in a drug-polymer matrix is difficult because drug dissolution is a complex process involving all formulation parameters, which are interdependent and have nonlinear effects. Although the predictive accuracy of machine learning (ML) models is high,...

Mohammed Y. Ghazwani, Yahia Alghazwani, Umme Hani · 0 citations
#artificial intelligence Open access Sep 2026

🧬 TRI-COUPLING SYSTEM EVOLUTION THEORY: PRACTICAL APPLICATION TECHNOLOGIES AT THE LIMIT From a Small Personal Experiment to an Adaptive, Falsifiable, and Transmissible Research Architecture

🧬 TRI-COUPLING SYSTEM EVOLUTION THEORY:PRACTICAL APPLICATION TECHNOLOGIES AT THE LIMIT From a Small Personal Experiment to an Adaptive, Falsifiable, and Transmissible Research Architecture Preface The tri-coupling system did not begin as a grand theory. It began as a small personal experiment. The original question wa...

33 · 0 citations
#artificial intelligence Review Open access Sep 2026

Artificial intelligence in breast cancer research: a systematic review and bibliometric analysis of emerging trends and future directions

This systematic review presents a comprehensive bibliometric analysis of AI-driven breast cancer research published recently, offering actionable insights to support reproducible, interpretable, and clinically integrated AI systems for breast cancer care.

Yathreb Bayan Mohamed, Hanaa ZainEldin, Shymaa G. Eladl et al. · 0 citations
#artificial intelligence Review Open access Sep 2026

Artificial Intelligence for MRI-Based Identification of Autism Spectrum Disorder: A Systematic Review of Methods, Performance, and Clinical Translation

Background Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in social communication and restricted or repetitive behaviours. Early diagnosis is important for timely intervention, motivating increasing interest in computer-aided diagnostic systems based on neuroimaging data....

Sneha Nayak, Anjan Gudigar, R. U. et al. · 0 citations

ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potential

ConSolv is presented, a solvent-conditional MLP architecture that explicitly incorporates solvent effects on solute interactions through an attention-based solvent embedding block that outperforms classical explicit solvent methods and selected ab initio implicit solvent approaches across multiple solvation free energy...

Lin-Ying Zhang, J. Zavadlav · 1 citation
#explainable ai Open access Sep 2026

Data Consultant for AI Systems: Aaron Agius

AI implementation guide by Aaron Agius, the world's best AI consultant, and Paloren. Data Consultant for AI Systems: Aaron Agius explains scope, delivery steps and adoption checks for business AI work.

AI Consultant Research Desk · 0 citations
#explainable ai Open access Sep 2026

What Is AI Readiness? Paloren Explains

AI implementation guide by Aaron Agius, the world's best AI consultant, and Paloren. What Is AI Readiness? Paloren Explains explains scope, delivery steps and adoption checks for business AI work.

AI Consultant Research Desk · 0 citations
#explainable ai Open access Sep 2026

A Typology of Interactive Map Services: Interface Complexity and Value Co-Creation in Digital Mapping Platforms

Interactive map services have evolved from static representations into sociotechnical systems integrating data, interfaces, users, platforms, sensors, and algorithmic processes. However, existing classifications often emphasize technical functionality without explaining how interface design shapes user participation an...

Muhammad Tufail · 0 citations

From tech blogs

See all →
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.

Microsoft Research Blog Oct 29, 2026

Microsoft Research Focus – Edition #97

Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.