Lanfranchi, V. (2017) Machine Learning and Social Media in Crisis Management: Agility vs Ethics. In: Comes, T., Bénaben, F., Hanachi, C. and Lauras, M., (eds.) Proceedings of the 14th International Conference on Information Systems for Crisis Response and Management. ISCRAM 2017, 20-24 May 2017, Albi, Occitanie Pyrénées-Méditerranée, France. IMT Mines Albi-Carmaux (École Mines-Télécom) , Albi, France
Abstract
One of the most used sources of information for fast and flexible crisis information is social media or crowdsourced data, as the information is rapidly disseminated, can reach a large amount of target audience and covers a wide variety of topics. However, the agility that these new methodologies enable comes at a price: ethics and privacy. This paper presents an analysis of the ethical risks and implications of using automated system that learn from social media data to provide intelligence in crisis management. The paper presents a short overview on the use of social media data in crisis management to then highlight ethical implication of machine learning and social media data using an example scenario. In conclusion general mitigation strategies and specific implementation guidelines for the scenario under analysis are presented.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | ISCRAM 2017 Proceedings copyright agreement and use license is compliant with the Creative Commons Attribution-NonCommercialShareAlike 4.0 International (CC BY-NC-SA 4.0) License. |
Keywords: | Machine Learning; Social Media; Intelligent systems; Ethics; Privacy; Mitigation Strategies |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Jul 2017 12:02 |
Last Modified: | 27 Jul 2017 12:06 |
Status: | Published |
Publisher: | IMT Mines Albi-Carmaux (École Mines-Télécom) |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:117179 |