Disasters, Interdisciplinarity, and AI

Abstract

This case study looks at how AI and interdisciplinary practice can change disaster relief for the better from a social justice point of view. The case uses the 2023 Nepal Jajarkot earthquake as an example to show that disasters are not "natural" but rather tragedies created by systemic racial, caste, gender, and geographic inequalities. In this case, survivors were left without help, shelter, or infrastructure. The case shows how AI can help with better communication, better coordination of responses, and fairer distribution of resources by using examples from different countries (like Google Person Finder during the 2015 Nepal earthquake and AI analysis of social media during Hurricane Harvey) and theoretical work in the disaster studies literature. It is also crying out for cross-disciplinary approaches that bring together engineering, communication, and the humanities to address data ethics, cultural context, and community involvement. The study borrows concepts from intersectionality theory, participatory design, and politics of knowledge production to chart the way AI can be used in a manner that empowers instead of marginalizing. Discussion questions and theme reflections then lead students to interrogate the pitfalls and potential of AI in disaster contexts.