The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning. / Kraft, Angelie; Usbeck, Ricardo.
Data-driven Resilience Research 2022: Proceedings of the International Workshop on Data-driven Resilience Research 2022. Hrsg. / Natanael Arndt; Sabine Gründer-Fahrer; Julia Holze; Michael Martin; Sebastian Tramp. Band 3376 Sun Site Central Europe (RWTH Aachen University), 2023. (CEUR Workshop Proceedings; Band 3376).Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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RIS
TY - CHAP
T1 - The Ethical Risks of Analyzing Crisis Events on Social Media with Machine Learning
AU - Kraft, Angelie
AU - Usbeck, Ricardo
N1 - Conference code: 1
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - crisis informatics
KW - ethics
KW - machine learning
KW - risks
KW - social media
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85156108349&partnerID=8YFLogxK
M3 - Article in conference proceedings
AN - SCOPUS:85156108349
VL - 3376
T3 - CEUR Workshop Proceedings
BT - Data-driven Resilience Research 2022
A2 - Arndt, Natanael
A2 - Gründer-Fahrer, Sabine
A2 - Holze, Julia
A2 - Martin, Michael
A2 - Tramp, Sebastian
PB - Sun Site Central Europe (RWTH Aachen University)
T2 - 2022 International Workshop on Data-Driven Resilience Research - D2R2 2022
Y2 - 6 July 2022 through 6 July 2022
ER -