The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning
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.
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@inproceedings{DBLP:conf/d2r2/KraftU22,
author = {Angelie Kraft and
Ricardo Usbeck},
editor = {Natanael Arndt and
Sabine Gr{\"{u}}nder{-}Fahrer and
Julia Holze and
Michael Martin and
Sebastian Tramp},
title = {The Ethical Risks of Analyzing Crisis Events on Social Media with
Machine Learning},
booktitle = {Proceedings of the International Workshop on Data-driven Resilience
Research 2022 co-located with Data Week Leipzig 2022 {(DATAWEEK} 2022),
Leipzig, Germany, July 6, 2022},
series = {{CEUR} Workshop Proceedings},
volume = {3376},
publisher = {CEUR-WS.org},
year = {2022},
url = {https://ceur-ws.org/Vol-3376/paper01.pdf},
timestamp = {Sun, 04 Aug 2024 19:44:12 +0200},
biburl = {https://dblp.org/rec/conf/d2r2/KraftU22.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}