  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.
