Legal and ethical considerations for demand-driven data collection and AI-based analysis in flood response

被引：0

作者：

Gilga, Carolin ^{[1
]}

Hochwarter, Christoph ^{[2
]}

Knoche, Luisa ^{[3
]}

Schmidt, Sebastian ^{[5
,8
]}

Ringler, Gudrun ^{[4
]}

Wieland, Marc ^{[6
]}

Resch, Bernd ^{[5
,7
,8
]}

Wagner, Ben ^{[9
,10
,11
]}

机构：

[1] Univ Kassel, Inst Business Law, Kassel, Germany

[2] Inst Empir Social Res IFES, Vienna, Austria

[3] German Fed Agcy Tech Relief THW, Bonn, Germany

[4] Johanniter Osterreich Ausbildung & Forsch gGmbH J, Vienna, Austria

[5] Univ Salzburg, Dept Geoinformat Z GIS, Salzburg, Austria

[6] German Aerosp Ctr DLR, German Remote Sensing Data Ctr DFD, Oberpfaffenhofen, Germany

[7] Harvard Univ, Ctr Geog Anal, Cambridge, MA USA

[8] Univ Austria, Geosocial Artificial Intelligence, IT U Interdisciplinary Transformat, Linz, Austria

[9] Delft Univ Technol, AI Futures Lab Rights & Justice, Delft, Netherlands

[10] Inholland Univ Appl Sci, ARC Res Ctr, The Hague, Netherlands

[11] Univ Austria, Human Rights & Technol, IT U Interdisciplinary Transformat, Linz, Austria

来源：

INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION | 2025年 / 122卷

关键词：

Disaster response; Artificial intelligence; Ethics; Data privacy;

D O I：

10.1016/j.ijdrr.2025.105441

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

During a disaster, the timely provision of customised and relevant data is of utmost importance. In the case of floods, data from remote sensing (satellite-based or airborne) is often used, but in recent years data from social media platforms has also been increasingly utilised. Focusing on these data sources, this study provides an in-depth assessment of requirements by emergency responders. Furthermore, the paper sheds light on the legal and ethical considerations that need to be taken into account during data collection and processing. A particular focus lies on the use of artificial intelligence (AI) for data analysis in disaster response. Topics such as privacy preservation and AI-informed decision making are highlighted throughout the paper. The investigation was carried out based on expert interviews with scientists, an extensive literature review, and workshops with emergency responders.

引用

页数：13

共 78 条

[1] Fake News Identification on Twitter with Hybrid CNN and RNN Models [J].

Ajao, Oluwaseun ;

Bhowmik, Deepayan ;

Zargari, Shahrzad .

SMSOCIETY'18: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON SOCIAL MEDIA AND SOCIETY, 2018, :226-230

[2]

AlZu'bi S, 2019, 2019 SIXTH INTERNATIONAL CONFERENCE ON SOCIAL NETWORKS ANALYSIS, MANAGEMENT AND SECURITY (SNAMS), P494, DOI [10.1109/SNAMS.2019.8931856, 10.1109/snams.2019.8931856]

[3] Spatio-temporal mining of keywords for social media cross-social crawling of emergency events [J].