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

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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.

Original languageEnglish
Title of host publicationData-driven Resilience Research 2022 : Proceedings of the International Workshop on Data-driven Resilience Research 2022
EditorsNatanael Arndt, Sabine Gründer-Fahrer, Julia Holze, Michael Martin, Sebastian Tramp
Number of pages11
Volume3376
PublisherSun Site Central Europe (RWTH Aachen University)
Publication date2023
Publication statusPublished - 2023
Externally publishedYes
Event2022 International Workshop on Data-Driven Resilience Research - D2R2 2022: Data Week Leipzig 2022 - Neues Rathaus/City Hall, Martin-Luther-Ring 4-6, Leipzig, Germany
Duration: 06.07.202206.07.2022
Conference number: 1
https://2022.dataweek.de/d2r2-22/

Bibliographical note

The authors acknowledge the financial support by the Federal Ministry for Economic Affairs and Energy of Germany in the project CoyPu ?project number 01MK21007[G]).

Publisher Copyright:
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

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