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.
The Business Council for Ethics of AI brings companies together in a trusted peer network to foster the global implementation of AI ethics in business. It promotes the application of UNESCO’s Recommendation on the Ethics of AI to real-world business decisions through the exchange of good practices across regions and sectors. The Council also serves as a bridge between business and other stakeholders, including policymakers, academia, civil society, and international institutions, to shape global AI governance through mutual learning and collaboration, working towards a competitive and responsible AI ecosystem that benefits all.
Activity Type Networks/Mentorship/ExchangeUNESCO, in collaboration with IRCAI, is developing an Online Repository of AI Tools for the Public Sector, Media and Judiciary, a multilingual web platform curating open-source and ethical AI tools and providing practical guidelines on their use for stakeholders in these three sectors. The repository will be a one-stop resource for the target stakeholders to identify, assess, and leverage AI tools of their choice to facilitate or enhance their operations, and help them address sector-specific challenges.
Activity Type AI Tools/SolutionsInfrastructure/Systems DevelopmentTechnical assistanceThe platform provides policy and decision makers with data, research, use cases, and best practices in the field of AI policy. It serves as a platform that highlights cooperation between International Organizations and makes information readily available on one centralized portal related to AI Governance.
Activity Type Research/Reports/AssessmentsAwareness/AdvocacyInfrastructure/Systems Development