As the world faces simultaneous disasters and burgeoning risks, humanitarian actors need to develop more efficient ways of delivering aid to vulnerable populations. One current trend involves the use of Artificial Intelligence (AI) and Machine Learning (ML) to process large amounts of data quickly to inform – and even autonomously undertake – decision-making processes. While these processes have the potential to facilitate faster and better anticipatory humanitarian action, they can pose unforeseen challenges if left unregulated and unchecked.
In this post, Christopher Chen, Associate Research Fellow at the Centre for Non-Traditional Security Studies, explores the promise and perils of using artificial intelligence and machine learning in the context of anticipatory humanitarian action. Building on insights gleaned from a data governance and protection workshop co-hosted by the S. Rajaratnam School of International Studies and the ICRC, he highlights some of the implications of the use of new technologies in humanitarian action and how the principle of ‘do no harm’ can be applied in a digital age.
