About UNU
The United Nations University (UNU) is a global think tank and postgraduate teaching organisation headquartered in Japan. The mission of the UN University is to contribute, through collaborative research and education, to efforts to resolve the pressing global problems of human survival, development and welfare that are the concern of the United Nations, its Peoples and Member States.Activities by UNU
17th International Conference on Theory and Practice of Electronic Governance (ICEGOV), TRACK 1: Trust and Ethical Digital Governance for the World We Want
GlobalThis Track invites papers that deal with issues related to advancing trust, ethical uses, transparency and accountability in digital governance, without inhibiting the development of new technologies for a better world we want. The track further welcomes empirical studies of government organisations’ work and experimentation with enhancing digital participation and inclusiveness in the governance of emerging technologies. The track welcomes alternative and creative methods and experimentations to reflect the voices and digital participation of all stakeholders from the global landscape (North-South, South-South, East-West, Tech-Gov, etc) in shaping governance considerations.
Activity Type Policy/Regulatory GuidanceResearch/Reports/AssessmentsAwareness/AdvocacyMulti-agent Multi-model Deep Research System
Global Japan
A multi-agent, multi-model system designed to automate the end-to-end process of deep research and report generation. The system addresses the traditionally labor-intensive and time-consuming nature of knowledge synthesis by orchestrating a sequential, human-in-the-loop workflow.
The architecture comprises a Planning Agent, which deconstructs a high-level user topic into a structured, editable research plan, and a Research Agent, which executes this plan by performing deep web research for each subtopic.
It addresses the limitations of traditional research workflows, which often suffer from algorithmic bias and informational silos, by leveraging multi-engine synthesis and an AI Judge to produce high-fidelity insights. The system ensures objectivity by comparing outputs from different search engines and consolidating them into a single, superior report.
Key Features:
Automated Planning: Breaks down topics into objectives and subtopics.
Deep Web Research: Executes multi-engine searches for broad coverage.
Cited Synthesis: Produces structured reports with references.
AI Judging: Compares multiple drafts and merges the best insights.
Automated Scoring: Quantifies report quality based on coverage and depth.
Iteration Tools: Allows re-synthesis without re-running full research.
Value Proposition:
Delivers comprehensive, unbiased, and actionable research outputs that surpass single-source approaches, making it ideal for academic, policy, and enterprise use cases.
Adaptability Beyond Research:
This architecture can be repurposed for other domains:
Policy Analysis: Aggregate and synthesize legislative data.
Market Intelligence: Compare insights from multiple industry sources.
Risk Assessment: Merge reports from different compliance engines.
Working Smarter with AI Assistants: A Practical Guide to Prompting and Beyond (UNU-EGOV)
Global PortugalA UNU-EGOV seminar introducing practical principles of prompt engineering and good practices for using AI assistants effectively and responsibly in public-sector contexts. Expected impact: improved understanding among public-sector professionals of how to use AI assistants, design better prompts, and apply AI resources responsibly in day-to-day work.
Activity Type Trainings/WorkshopsClassifcation of clouds and aerosols physics parameters using PINN & CNN model and Satellite active remote sensing images.
Global ChinaFinlandIndiaJapanPakistanSpainUnited StatesThis is an going work on the development of a Physics informed neural network (PINN) & CNN to better classify aerosols and cloud particles in the atmosphere from zero to 30 km using lidar and radar images from previous and current satellite remote sensing data. We are trying to improve the detection of the size, the shape and the type of atmospheric particles in order to enhance the effect of radiative forcing on the planet and the effect on health as some fine aerosols' particles can cause serious health issues, like lung cancer, asthma, respiratory infections, and heart disease.
Activity Type Research/Reports/Assessments