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explainable ai

2,810 papers

#explainable ai Open access Sep 2026

FROM SKILLS VERIFICATION TO WORKFORCE INTELLIGENCE A RESPONSIBLE AI FRAMEWORK FOR COMPETENCY-BASED WORKFORCE DECISION-MAKING

Artificial intelligence (AI) is increasingly used to support recruitment, skills analysis, learning, performance management and workforce planning. Yet many workforce decisions still depend heavily on static qualifications, curriculum vitae data, self-reported experience and periodic assessments. These sources remain v...

Rita Ebele Maduanusi · 0 citations
#explainable ai Open access Sep 2026

Two Different States Carry Exactly the Same Load ── In the linear model, a flow of 3000 vehicles per hour occurs at densities 41.4590 and 108.5410 per km, at speeds of 72.3607 and 27.6393 km/h ── a factor of 2.6180 ── and the maximum 3750 occurs at half the free-flow speed ── [Paper 462]

A road is thought to carry more the faster it runs. This paper shows that two states carry the same flow at speeds differing by 2.6180──and the maximum flow occurs at half the free-flow speed. No new theorem or law is claimed. Scope of this paper (scope note): No new theorem or law is claimed──the fundamental diagram o...

Yuuki Yamagishi · 0 citations
#explainable ai Open access Sep 2026

Predicting utilization of emergency contraceptives in ethiopia and identifying its predictors using machine learning

Abstract Despite policy support, inappropriate use of emergency contraception in Ethiopia contributes to high rates of unintended pregnancy and maternal mortality. Traditional statistical analyses have struggled to identify complex predictors. This study used machine learning and Explainable AI to improve the predictio...

Muluken Belachew Mengistie, Abraham Keffale Mengistu, Wubete Lule Ayalew et al. · 0 citations
#explainable ai Open access Sep 2026

Structured PREreview of "An assessment of normalization and differential expression methods for miRNA-seq analysis using a realistic benchmark dataset"

This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/22737224. Does the introduction explain the objective of the research presented in the preprint? Yes It explains the problem properly, namely the need for reliable method...

Namra Basharat · 0 citations
#generative ai Dataset Open access Sep 2026

“Broken and Repair” case studies: Developing GenAI-age critical evaluation and creative problem-solving skills in communication education

Communication education increasingly requires students to work across two linked capabilities: critically evaluating professional communication materials and using generative AI (GenAI) with judgment, transparency, and ethical care. This article presents a “Broken and Repair” case-study assessment, developed in a postg...

Triparna Gandhi, Tom Whitford · 0 citations
#explainable ai Review Open access Sep 2026

Improving Public-Sector Service Delivery through ServiceNow-Based ITSM Workflow Automation: An AI-Driven Approach to Reducing Manual Case Handling and Response Time in Government Benefit Systems

ServiceNow-based ITSM and AI-enabled workflow capabilities can improve operational efficiency, workflow development, and service delivery in public-sector benefits administration and future research should independently evaluate deployed ServiceNow systems across technical efficiency, workforce, fairness, governance, a...

Sindhu Bhargavi Vajja · 0 citations
#explainable ai Open access Sep 2026

A Photocount Reading of the Interpolating Function, and a No-Go for Deriving Its Normalisation from a Clock

A companion paper removed the MOND acceleration scale as an independent parameter and found that 155 SPARC rotation curves then select, with nothing fitted, the member n = 2 of the family mu_n(Y) = 1 - (1+Y)^(-n), which is kappa = 1/2. This paper reports what happened when we tried to DERIVE that integer, and one struc...

Carl P. Zimmerman · 0 citations
#explainable ai Open access Sep 2026

A Photocount Reading of the Interpolating Function, and a No-Go for Deriving Its Normalisation from a Clock

A companion paper removed the MOND acceleration scale as an independent parameter and found that 155 SPARC rotation curves then select, with nothing fitted, the member n = 2 of the family mu_n(Y) = 1 - (1+Y)^(-n), which is kappa = 1/2. This paper reports what happened when we tried to DERIVE that integer, and one struc...

Carl P. Zimmerman · 0 citations
#explainable ai Open access Sep 2026

Developing and Validating an AI Literacy Instrument for Elementary School Students: A Psychometric Study Using the ADDIE Framework

AbstractArtificial intelligence (AI) literacy has emerged as a critical competency in elementary education as learners increasingly engage with AI-mediated and technology-rich learning environments. This study developed and validated a multidimensional AI literacy instrument for elementary school students using a resea...

Mhel Cedric D. Bendo, Torang Siregar · 0 citations
#explainable ai Open access Sep 2026

The Observational Incompleteness Framework

The Observational Incompleteness Framework is an AI-based research programme deriving quantum-mechanical structure from the premise that observation is a proper subsystem of a deterministic whole. The archive contains three kinds of content: Technical papers (papers/) — the authoritative technical content, including th...

Alex Maybaum · 0 citations
#explainable ai Open access Sep 2026

Two Different States Carry Exactly the Same Load ── In the linear model, a flow of 3000 vehicles per hour occurs at densities 41.4590 and 108.5410 per km, at speeds of 72.3607 and 27.6393 km/h ── a factor of 2.6180 ── and the maximum 3750 occurs at half the free-flow speed ── [Paper 462]

A road is thought to carry more the faster it runs. This paper shows that two states carry the same flow at speeds differing by 2.6180──and the maximum flow occurs at half the free-flow speed. No new theorem or law is claimed. Scope of this paper (scope note): No new theorem or law is claimed──the fundamental diagram o...

Yuuki Yamagishi · 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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