Responsible AI for African Peace and Security: Machine Learning, NLP and Large Language Models for Regional Early Warning and the African Standby Force

                @article{CRSIJ26000486,
                  author = {Dr. Alexander Odartey Lamptey},
                  title = {Responsible AI for African Peace and Security: Machine Learning, NLP and Large Language Models for Regional Early Warning and the African Standby Force},
                  journal = {Cosmo Research and Science International Journal},
                  year = {2026},
                  volume = {2},
                  number = {2},
                  pages = {972-988},
                  issn = {3108-1584},
                  url = {https://cosmorsij.com/published/CRSIJ26000486.pdf},
                  abstract = {Artificial intelligence (AI), machine learning (ML), natural language processing (NLP), large language models (LLMs), and network analytics may strengthen African regional peace and security institutions by improving the synthesis of fragmented information, identifying patterns in conflict and political violence, supporting early warning, and informing planning for peace support operations and counter-terrorism. Yet their value depends less on technological novelty than on the quality, legitimacy, interoperability, and governance of the data and institutions through which they are used. This policy paper examines how data-driven analytical capabilities could be responsibly integrated into the African Union’s Regional Mechanisms and the African Standby Force (ASF), with attention to armed conflict, political violence, violent extremism, and counter-terrorism. Using qualitative document analysis, the paper synthesises relevant scholarship, African and international policy frameworks, and public institutional material. An illustrative case study of ECOWAS’s Early Warning and Response Network (ECOWARN) explores the practical relationship between field reporting, human-security indicators, regional risk analysis, and AU–regional coordination. It develops a proposed Regional Conflict Analytics and Early Warning Framework organised around six functions: shared data standards; multilingual and multimodal analysis; human-validated analytical products; regional interoperability; responsible governance; and continuous learning and evaluation. The paper argues that AI should support, not replace, political judgement, command responsibility, community knowledge, and legal review. It recommends a phased pilot focused initially on non-operational early warning and situational awareness, with clear safeguards against discriminatory profiling, unverified allegations, automated targeting, and opaque risk scoring. The paper contributes a practical governance and implementation agenda for African-led, rights-respecting use of data analytics in regional peace and security, while using ECOWARN to ground the recommendations in an existing regional early-warning architecture. (2024a, 2024b; Tabassi, 2023; UNESCO, 2021)},
                  keywords = {African Standby Force, regional mechanisms, machine learning, natural language processing, large language models, conflict early warning, counter-terrorism, political violence, responsible AI},
                  month = {September}
        }