Skip to content

Category

explainable ai

2,232 papers

#explainable ai Open access Sep 2026

UMOSU and UMOKWAI: An Ontological and Epistemic-Operational Model for the Classification of Reality and Knowledge

UMOSU (Unified Model of Social Universe) / UMOKWAI (Unified Model of Knowledge With AI) This paper presents UMOSU (Unified Model of Social Universe), an ontological model designed to classify phenomena according to the components that are necessary for their manifestation, and UMOKWAI (Unified Model of Knowledge With A...

Marco Falsetti · 0 citations
#explainable ai Open access Sep 2026

Replication package: When a Deed Is Not a Market Sale: Foreclosure Transfers, Statutory Consideration, and Repeat-Sales House-Price Measurement

Version 1.0.1 corrects one bibliography entry: the companion paper, Loschi (2026), Who Gets the House? Financing-Associated Price Gaps in New York City, is now cited as published on Zenodo (version 2.3, 10.5281/zenodo.22921991) rather than as unpublished. The code zip and manuscript PDF were rebuilt accordingly; the RE...

Pablo Loschi · 0 citations
#explainable ai Open access Sep 2026

Using AI to Organize and Publish Slavery Research Online: The Cowenhoven Project

The Cowenhoven Project (cowenhoven.org) is a multi-part digital humanities project combining archival research, genealogical scholarship, and data science in an effort to recover the names of people enslaved in New York and New Jersey by the Van Kouwenhoven-Conover family. In this presentation, I explain how I was able...

Christopher A. Barnes · 0 citations
#explainable ai Open access Sep 2026

Blood biomarkers and artificial intelligence: toward precision medicine in Alzheimer’s disease

The integration of blood-based biomarkers and artificial intelligence (AI) is revolutionizing the diagnostic landscape of Alzheimer’s disease (AD). In this paper, we synthesize recent advancements in the application of machine learning (ML) on blood biomarker analysis, evaluating their diagnostic efficacy and clinical...

Lin-Hong Xu, Jian-Bo Tu, Ji-Peng Zhang et al. · 0 citations
#explainable ai Book Sep 2026

Cognitive and Neural Interfaces in Bridging Brain, Body, and Virtual Worlds

The neural interface, coupled with wearable artificial intelligence (AI) and cognitive computing, is reshaping the way human beings interact with digital ecosystems, especially in immersive metaverse worlds. This chapter explains that cognitive and neural interfaces may be an epochal nexus among the human brain, physio...

A. Ashwini, Alvin Ancy A., Joel Livin A. et al. · 0 citations
#explainable ai Open access Sep 2026

Evaluation and screening of rice (Oryza sativa L.) genotypes for drought tolerance under reproductive-stage stress

Drought stress at the reproductive-stage is a major constraint limiting rice productivity in rainfed ecosystems. The present study evaluated 450 rice (Oryza sativa L.) genotypes along with 8 checks under normal and reproductive-stage drought stress conditions during kharif 2020 at Bihar Agricultural University, Sabour,...

K. Abhishek, S. P. Singh, K. Mankesh et al. · 0 citations
#explainable ai Open access Sep 2026

Enhancing Financial Decision Quality in Smart Organizations through Explainable AI and Real Time Analytics with AI Governance Maturity

This study investigates the links among explainable AI capability, real time financial analytics capability, and financial decision quality within smart organizations while assessing how AI governance maturity moderates those links. Drawing on organizational information processing theory and dynamic capabilities theory...

VINH VO MINH, HANG WEI YUAN · 0 citations
#explainable ai Review Open access Sep 2026

Artificial Intelligence-Assisted FT-IR Spectral Sensing: From Molecular Fingerprints to Quantitative and Interpretable Chemical Information

Overall, AI can extend FT-IR from qualitative fingerprinting to quantitative and predictive spectral sensing, provided that future studies emphasize method-centered validation, reproducibility, transferability, and chemically meaningful explanation rather than relying solely on classification accuracy and regression er...

Doyeon Im, Kyung Hwan Lim, Gayoung Seo et al. · 0 citations
#data science Open access Sep 2026

What a Crossref record shows when a paper is retracted: one month of retraction deposits (v2)

**A field-level audit of one month of retraction deposits (v2)** Author: Trafalgar Law (independent). Data pulled from the public Crossref REST API on 2026-09-23. Sample and reproduction query included. This is a metadata audit, not an accusation against any publisher or author. ## Why On 2026-09-19 the record for a fi...

Trafalgar Law · 0 citations
#explainable ai Open access Sep 2026

GiriNeoSeizure™ CDSS — Neonatal Seizures: Stabilise, Confirm, Treat & Reassess (Edition 1, Version 2.0)

GiriNeoSeizure™ CDSS is a single-file, offline, interactive clinical decision-support system for neonatal seizures. It is part of the NeonatAIlogy™ series in the GAIR Innovation Registry and is governed by the GIRISH™ Prompting Architecture. The tool turns the 2023 ILAE Task Force guideline, Cochrane evidence on antise...

Girish Gupta · 0 citations
#explainable ai Open access Sep 2026

m-mdy-m/cdin: cdin v0.1.0-beta.7

[0.1.0-beta.7] — 2026-09-23 Largest release so far: 102 commits, 218 files changed, +21,760 / −149. Headline features are full UTF-8/RTL/Arabic shaping support, a theme system with 10 built-in themes, an optional-plugin system, a Lua test suite, Docker images, and a complete documentation website. The beta cycle contin...

Genix, rxi, Kasra M. Hosseini et al. · 0 citations

From tech blogs

See all →
Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

Microsoft Research Blog Oct 6, 2026

What AI gets wrong and what failure teaches us

Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity.  The post What AI gets wrong and what failure teaches us 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.