Distributed morality, privacy, and social media in natural disaster response
Paul Hayes and
Stephen Kelly
Technology in Society, 2018, vol. 54, issue C, 155-167
Abstract:
In this article we note that natural disasters are a destructive force of natural evil that will likely have even greater deleterious effects moving into the future. Whilst natural disasters have catastrophic potential, the advent of social media means that statutory emergency managers have a source of real time information updates to assist decision making in natural disaster response. However, social media feeds do not contain purely relevant information, therefore the task of navigating them in crisis scenarios can be an unwieldy one. As researchers involved in the development of a system that monitors social media for information pertaining to natural disasters (the EU FP 7 funded Slándáil project), we propose that the delegation of this morally loaded task to an autonomous computational artefact can potentially help harness the power of distributed morality and can empower heterogeneous organisations to overcome the phenomenon Luciano Floridi refers to as the tragedy of the Good Will.
Keywords: Machine learning; Text processing; Ethics; Privacy; Emergency management; Natural disaster (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0160791X17302932
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:54:y:2018:i:c:p:155-167
DOI: 10.1016/j.techsoc.2018.05.003
Access Statistics for this article
Technology in Society is currently edited by Charla Griffy-Brown
More articles in Technology in Society from Elsevier
Bibliographic data for series maintained by Catherine Liu ().