Skip to content

Category

explainable ai

2,125 papers

#explainable ai Open access Oct 2026

Financing the Body, Forgetting the Process: A Demand-Side Pillar for Physical AI and Humanoid Robotics Incentives in Italy

In autumn 2026 the Italian government announced a package of measures for advanced robotics and Physical AI: a dedicated fund of 200-300 million euro in the 2027 budget law, the use of Innovation Agreements (Accordi per l'innovazione) as the delivery vehicle, a publicly controlled "chain leader" to aggregate the supply...

Marco Belardi · 0 citations
#explainable ai Open access Oct 2026

AI, DEEPFAKES, AND DEMOCRATIC ACCOUNTABILITY: THE ROLE OF RTI LAWS

With the advent of artificial intelligence and deepfake technologies, the modern information landscape is changing, giving rise to realistic synthetic text, images, audio, and videos. Such changes pose significant problems for democratic accountability, because they can skew political discourse, reduce trust in authent...

Pranita Choudhury and Nandini Saikia Lohit Kumar Saikia · 0 citations
#explainable ai Dataset Open access Oct 2026

Explainable Agricultural Water-Use Efficiency: Integrating Frontier Analysis with Explainable AI for Environmental Decision Support

This record contains the data, code, and results supporting the book chapter "Explainable Agricultural Water-Use Efficiency: Integrating Frontier Analysis with Explainable AI for Environmental Decision Support" by Morteza Yaqubi and Ali Maroosi, prepared for the Elsevier volume Environmental Intelligence: Artificial In...

Yaqubi Morteza, Maroosi Ali · 0 citations
#explainable ai Open access Oct 2026

PREreview of "Psychosexual Rehabilitation After Cancer: A Narrative Review of the Intervention Evidence and a Stepped-Care Model for Survivorship Practice"

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23121600. Summary of main findings and contribution This narrative review focuses on an important question: how psychosexual rehabilitation after cancer can be delivered at scale, r...

Ghadeer Asarwi · 0 citations
#explainable ai Open access Oct 2026

PREreview of "Self-Correcting Multimodal AI Agents for Reliable Autonomous Decision-Making"

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23121066. Peer Review Preprint Title: Self-Correcting Multimodal AI Agents for Reliable Autonomous Decision-Making Authors: Muhammad Umair Younus, Hammad Muneer, Danial Hameed, and...

Jr. Julian Rodriguez · 0 citations
#explainable ai Open access Oct 2026

Complete Guide: How to Get a Java Developer Job at Top Tech Companies

TechPanda Academy presents a complete guide for Java developers who want to get hired at top technology companies such as Amazon, Microsoft, Google and Meta. The guide explains the interview process step by step, from the recruiter screen and online assessment to the final interview loop, and shows what is tested at ea...

TechPanda Academy · 0 citations
#explainable ai Open access Oct 2026

The Influence of AI Governance Ethics and Algorithmic Transparency on Employee Trust and Responsible Use of AI-Powered Performance Management Systems: Evidence from Three Digital Loan Firms in Lagos, Nigeria

Abstract This study examines how AI governance ethics and algorithmic transparency shape employee trust in, and responsible use of, AI-powered performance management systems (AI-PMS), and whether AI literacy strengthens the transparency–trust link. A convergent parallel mixed-methods design was applied across three CBN...

Ahmed Olakunle Ogungbade, Osimokha Achief Godsent, Adetayo Olaitan Ayanleke et al. · 0 citations
#explainable ai Open access Oct 2026

2a Number Theory: Why FWA Explains Everything — Stone, Snail, Human, AI, Galaxy — Consciousness Levels as C = γ(E)·Δ

2a Number Theory: Why FWA Explains Everything — Stone, Snail, Human, AI, Galaxy — Consciousness Levels as C = γ(E)·ΔDescription / Abstract:This figure presents the FWA (Field-Wave Algebra) formalization of consciousness levels.Core equation: C = γ(E) · ΔDefinitions:K = structural complexityS = field stateδ = errorΔ = d...

Kolesnikov Igor · 0 citations
#explainable ai Open access Oct 2026

AI-FMS: System Development, Phase I Evaluation, and Human-AI Collaboration in FMS Video Review

Preprint. Not peer reviewed. Functional Movement Screen (FMS) assessment depends on trained human observation, yet practical video review can be constrained by transient viewing, remote access, repeated manual navigation, qualitative judgments, and the compression of movement into an ordinal 0-3 score. We developed AI-...

Haoran Zhu · 0 citations
#explainable ai Open access Oct 2026

Quality of Service Challenges and Solutions in Multi-Tenant Community Clouds: A Comprehensive Survey

Community cloud computing has emerged as an effective deployment model for organizations that share common security policies, governance frameworks, and regulatory requirements while benefiting from collaborative resource sharing. By enabling multiple organizations to utilize a common cloud infrastructure, community cl...

Anand Kumar H, Sunil Kumar, Mustafa Basthikodi · 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.