The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning

TL;DR

This paper argues that while machine learning applied to social media during crisis events can speed detection and response, it introduces substantial ethical risks across data, models, and societal context. It conducts a sociotechnical critique, detailing risks such as limited representativeness, misinformation, privacy violations, algorithmic bias, technology access gaps, and lack of transparency. It synthesizes prior crisis informatics work with contemporary ML challenges and offers concrete future directions, including ethics-by-design, datasheets, model cards, bias auditing, and stakeholder-focused evaluation. The work aims to sensitize researchers and practitioners to prioritize equity and privacy, ensuring that crisis technologies support vulnerable populations without exacerbating existing inequalities or eroding trust.

Abstract

Social media platforms provide a continuous stream of real-time news regarding crisis events on a global scale. Several machine learning methods utilize the crowd-sourced data for the automated detection of crises and the characterization of their precursors and aftermaths. Early detection and localization of crisis-related events can help save lives and economies. Yet, the applied automation methods introduce ethical risks worthy of investigation - especially given their high-stakes societal context. This work identifies and critically examines ethical risk factors of social media analyses of crisis events focusing on machine learning methods. We aim to sensitize researchers and practitioners to the ethical pitfalls and promote fairer and more reliable designs.