About UNICC
The United Nations International Computing Centre (UNICC) has over 50 years of experience as the largest strategic partner for digital solutions and cybersecurity within the United Nations system. We design and deploy transformational digital tools and programmes to support over 90 partners in fulfilling their mandates.Activities by UNICC
From Total Cost of Ownership to Social Impact: A Frugal AI Framework to Measure AI Portfolios
Global Not ApplicableA research and framework development initiative produced in partnership between the Frugal AI Hub at the University of Cambridge and UNICC. The paper proposes a standardised, three-level framework for measuring AI portfolio value: Level 1 quantifies Total Cost of Ownership (TCO) and Frugal AI efficiency metrics; Level 2 introduces ROI linking financial benefits to TCO; and Level 3 aligns AI performance with UN Sustainable Development Goals (SDGs) to measure social impact. The framework is applicable to any organisation seeking transparent and equitable AI portfolio valuation.
Activity Type AI Tools/SolutionsResearch/Reports/AssessmentsDetection of Xenophobic Language and Misinformation in Media Content
Global United States
The Detection of Xenophobic Language and Misinformation in Media Content project was a collaborative effort conducted between UNICC, UNESCO, IOM, and New York University SPS Capstone participants from 2024. The rise of xenophobic language and misinformation in media narratives, particularly those involving migrants, refugees, and displaced communities, has prompted the need for tools that promote balanced, fact-based journalism that respects the rights and dignity of vulnerable populations. While negligent content amplifies harmful stereotypes and false narratives, manually screening for such content is costly, slow, and prone to error. In this context, UNICC collaborated with the NYU School of Professional Studies (students & faculty) to develop a comprehensive data labeling approach aimed at categorizing information based on its tone and intent. Together, we established the following classification criteria: “toxic” : Content containing generally harmful or offensive language intended to provoke or hurt. “severe_toxic” : Highly aggressive or extreme language with intense hostility or derogatory tone. “obscene” : Language that includes vulgar or sexually explicit content inappropriate for public discourse. “threat” : Statements expressing intentions to cause harm or incite violence against individuals or groups. “insult” : Content that demeans or ridicules someone based on personal characteristics or affiliations. “identity_hate” : Hate speech targeting individuals or groups based on identity markers like race, ethnicity, religion, or nationality. The project aimed to build an AI-based media analysis tool to identify and mitigate xenophobic language, misinformation, and harmful narratives in media coverage to address these ethical challenges of reporting on human mobility by fostering informed and unbiased journalism. The primary goal is to create a robust AI tool that leverages advanced language models to detect harmful content, ensure ethical reporting, and support media outlets in providing balanced narratives about vulnerable communities.
Nuance Matters: An interesting observation was how detecting xenophobia is context-sensitive. Many terms must be interpreted with context and not just keyword matching. Data Labeling Challenges: These challenges arose due to the uneven distribution of content types. For instance, common labels like "toxic" were well-represented, while rare but important labels like "threat" had limited examples. This imbalance made it harder for the AI to learn and accurately detect less frequent but critical content types, requiring special attention during training and evaluation to ensure balanced model performance. Future Plans for Expansion: Moving forward, we are focused on enhancing the model's capabilities by expanding its architecture. This ongoing evolution reflects our commitment to continuous improvement and to maximizing the impact of our project.
RiMAP VLA: A Virtual Legal Assistant for Scaling Humanitarian Law and Policy Analysis on the UNHCR Rights Mapping and Analysis Tool (RIMAP)
Global Not Applicable
UNHCR developed a Virtual Legal Assistant (VLA) powered by Retrieval Augmented Generation (RAG) into its Rights Mapping and Analysis Platform (RiMAP). This platform support UNHCR country editors by efficiently collecting, processing, and analyzing vast amounts of legal documents across all UN countries and territories. This enables legal research and analysis processes, including extraction, translation, summarization, and drafting.
Once data is manually collected and indexed in a library accessible to the AI assistant, a first draft of responses to questions can be generated. This draft is created by extracting relevant paragraphs, translating them, and summarizing the content to produce an accurate response with sources. This draft serves as the foundation for the legal analysis that country editors can verify and further edit.
The library of sources includes government publications, laws and regulations, policies, academic research, and UNHCR reports. By leveraging AI, the VLA allows UNHCR to significantly reduce the time spent on legal research and drafting, ensuring the timely and comprehensive completion of RiMAP within a reduced timeframe.
The VLA is an AI-powered chatbot that utilizes resources contained within RiMAP. This public-facing chatbot provides easily accessible and user-friendly information on the rights of forcibly displaced and stateless persons. This initiative marks a significant step towards making legal data more accessible to a diverse range of stakeholders, including universities, legal aid organizations, governments, development actors, civil society groups, and, most importantly, forcibly displaced and stateless persons themselves.
AI Safety Evaluation and Assurance for AI Solutions
Global Not ApplicableA two-semester academic–institutional capstone (Fall 2025–Spring 2026) in which NYU graduate students co-designed with UNICC a multi-perspective AI Safety Evaluation Lab — including a structured risk taxonomy, automated evaluation pipelines, and a three-provider 'Council of Experts' deliberative architecture — to assess AI tools before deployment in the UN ecosystem.
Activity Type AI Tools/SolutionsTrainings/WorkshopsResearch/Reports/Assessments