AI-Powered Multi-Stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform
- URL: http://arxiv.org/abs/2603.06584v1
- Date: Sun, 25 Jan 2026 22:27:01 GMT
- Title: AI-Powered Multi-Stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform
- Abstract summary: This paper presents the Global Solutions Initiative (GSI) D-Hub, a data-driven coordination platform.<n>The system integrates structured data models, interpretable algorithms, and synthetic data pipelines to reduce information asymmetries.<n>Results show that explainable recommendations and contextual dashboards enhance trust, usability, and decision confidence.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Digital platforms increasingly support collaboration across organizations, yet many remain constrained by fragmented data and limited transparency. This paper presents the Global Solutions Initiative (GSI) D-Hub, a data-driven coordination platform that applies explainable artificial intelligence (AI) for transparent matchmaking among deployers, solution providers, and financiers. The system integrates structured data models, interpretable algorithms, and synthetic data pipelines to reduce information asymmetries and improve data quality. Using a design-science approach, the platform was developed and validated with stakeholders from development, technology, and finance sectors. Results show that explainable recommendations and contextual dashboards enhance trust, usability, and decision confidence. The study contributes to data mining and data governance research by demonstrating how explainable, verifiable algorithms can enable scalable, trustworthy digital ecosystems for public collaboration.
Related papers
- A Survey on the Green Development of Large Models: From Resource-Efficient Architectures to Hardware-Software Co-Design [71.32556809242666]
The rapid expansion of large-scale AI models has led to significant performance breakthroughs across diverse domains.<n>It has also raised critical concerns regarding computational costs, energy consumption, and environmental sustainability.<n>This survey aims to offer a holistic roadmap toward sustainable, scalable, and socially responsible development of large models.
arXiv Detail & Related papers (2026-07-10T04:02:22Z) - Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
arXiv Detail & Related papers (2026-01-15T18:55:03Z) - DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery [1.0713846107735632]
DataScribe is an AI-native, cloud-based materials discovery platform.<n>It unifies experimental and computational data through machine-actionable knowledge graphs.<n>By embedding optimization engines, machine learning, and unified access to public and private scientific data directly within the data infrastructure, DataScribe functions as a general-purpose application-layer backbone for laboratories of any scale.
arXiv Detail & Related papers (2026-01-12T19:59:39Z) - A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers [251.23085679210206]
Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research.<n>This survey reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate.<n>We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge.
arXiv Detail & Related papers (2025-08-28T18:30:52Z) - A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks [49.90474228895655]
Cloud-edge-terminal collaborative intelligence (CETCI) is a fundamental paradigm within the artificial intelligence of things (AIoT) community.<n>CETCI has made significant progress with emerging AIoT applications, moving beyond isolated layer optimization to deployable collaborative intelligence systems.<n>This survey describes foundational architectures, enabling technologies, and scenarios of CETCI paradigms, offering a tutorial-style review for CISAIOT beginners.
arXiv Detail & Related papers (2025-08-26T08:38:01Z) - Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted [0.0]
Business Semantics Centric, AI Agents Assisted Data System (BSDS)<n>BSDS redefines data systems as dynamic enablers of business success.<n>System includes curated data linked to business entities, knowledge base for context-aware AI agents, and efficient data pipelines.
arXiv Detail & Related papers (2025-06-05T19:06:06Z) - Data-Driven Breakthroughs and Future Directions in AI Infrastructure: A Comprehensive Review [0.0]
This paper presents a comprehensive synthesis of major breakthroughs in artificial intelligence (AI) over the past fifteen years.<n>It identifies key inflection points in AI' s evolution by tracing the convergence of computational resources, data access, and algorithmic innovation.
arXiv Detail & Related papers (2025-05-22T15:12:48Z) - Blockchain-Enabled Accountability in Data Supply Chain: A Data Bill of Materials Approach [16.31469678670097]
We introduce Data Bill of Materials" (DataBOM) to capture the dependency relationship between different datasets and stakeholders by storing specific metadata.
We demonstrate a platform architecture for providing blockchain-based DataBOM services, present the interaction protocol for stakeholders, and discuss the minimal requirements for DataBOM metadata.
arXiv Detail & Related papers (2024-08-16T05:34:50Z) - Best Practices and Lessons Learned on Synthetic Data [83.63271573197026]
The success of AI models relies on the availability of large, diverse, and high-quality datasets.
Synthetic data has emerged as a promising solution by generating artificial data that mimics real-world patterns.
arXiv Detail & Related papers (2024-04-11T06:34:17Z) - On Responsible Machine Learning Datasets with Fairness, Privacy, and Regulatory Norms [56.119374302685934]
There have been severe concerns over the trustworthiness of AI technologies.
Machine and deep learning algorithms depend heavily on the data used during their development.
We propose a framework to evaluate the datasets through a responsible rubric.
arXiv Detail & Related papers (2023-10-24T14:01:53Z) - A framework for fostering transparency in shared artificial intelligence
models by increasing visibility of contributions [0.6850683267295249]
This paper presents a novel method for deriving a quantifiable metric capable of ranking the overall transparency of the process pipelines used to generate AI systems.
The methodology for calculating the metric, and the type of criteria that could be used to make judgements on the visibility of contributions to systems are evaluated.
arXiv Detail & Related papers (2021-03-05T11:28:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.