The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning
It addresses ethical pitfalls for researchers and practitioners in high-stakes societal contexts, but is incremental as it focuses on risk identification rather than new solutions.
The paper tackles the problem of ethical risks in using machine learning to analyze social media data for crisis events, identifying and examining these risks to promote fairer and reliable designs.
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.