About IOM
IOM works to help ensure the orderly and humane management of migration, to promote international cooperation on migration issues, to assist in the search for practical solutions to migration problems and to provide humanitarian assistance to migrants in need, including refugees and internally displaced people.Activities by IOM
HRDD Research Agent: AI-powered human rights research across countries and sources
Global SwitzerlandConducts deep AI-powered research for Human Rights Due Diligence (HRDD) reports on countries, identifying and synthesizing evidence of human rights violations from multiple sources.
Activity Type AI Tools/SolutionsAI Document Processor
Global ZimbabweThe AI Document Processor automates the processing and renaming of scanned documents, including consent forms, passport copies, and certificates of Migrants heading to the UK. The solution was built to support the e-Filing process by automating the renaming of scanned client documents. Manual renaming was slow, repetitive, and prone to errors. This tool improves efficiency and accuracy by streamlining the task and reducing human error.
Activity Type AI Tools/SolutionsMongolia Cyber-Crime Investigations: Mongolia's first AI training to fight trafficking online
Asia and the Pacific MongoliaA year-long project to equip Mongolian law-enforcement officials with AI-based and open-source intelligence (OSINT) techniques to investigate human-trafficking cases in the digital era. Launched in July 2023 in collaboration with the Bali Process Regional Support Office (RSO) and the Coordination Council of Crimes Prevention of Mongolia, and supported by the IOM Development Fund, the project delivered the first artificial-intelligence-based cyber-crime investigation training to law enforcement in Mongolia. Through a unique multi-workshop format, the same officers from six law-enforcement and intelligence agencies built skills across three OSINT sessions over six months (Jan–Jun 2024), covering search-engine techniques, officer privacy and safety, digital-footprint analysis, semi-automated and automated data scraping, and the use of AI-generated tools (e.g. ChatGPT), with each technique applied to trafficking-in-persons case studies. The work responds to traffickers' growing misuse of social media, dating apps and false job advertisements, and aligns with Mongolia's Vision-2050 and Digital Nation strategies and the IOM Strategic Plan's emphasis on protecting migrants through evidence-based, innovative solutions.
Activity Type Trainings/WorkshopsTechnical AssistanceDetection 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.