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AI-Powered Multi-Stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform

Muzakkiruddin Ahmed Mohammed, Adeeba Tarannum, Eileen Devereux Dailey, Marla Johnson, Mert Can Cakmak, John Talburt

TL;DR

The GSI D-Hub is presented, a data-driven coordination platform that applies explainable artificial intelligence for transparent matchmaking among deployers, solution providers, and financiers to reduce information asymmetries and improve data quality.

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.

AI-Powered Multi-Stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform

TL;DR

The GSI D-Hub is presented, a data-driven coordination platform that applies explainable artificial intelligence for transparent matchmaking among deployers, solution providers, and financiers to reduce information asymmetries and improve data quality.

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.
Paper Structure (11 sections, 1 equation, 1 figure, 5 tables)

This paper contains 11 sections, 1 equation, 1 figure, 5 tables.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Research Methodology and System Design
  4. Methodological Framework
  5. Synthetic Data Construction and Validation
  6. System Architecture and Data Model
  7. Algorithmic Matching Model
  8. Functional Features and Interaction Design
  9. Validation and Results
  10. Evaluation Results and Insights
  11. Conclusion

Figures (1)

  • Figure 1: GSI D-Hub platform architecture. The presentation layer provides role-specific interfaces, visualization, interactive components, and notifications. The business logic layer implements problem and solution management, an explainable matching engine, deal management, and an AI assistant. The data layer supports structured storage for problems, solutions, matches, deals, and analytics. The integration and infrastructure layer provides frontend libraries, visualization components, and cloud-based infrastructure, while external systems offer AI/ML and data services.