Suchergebnisse

Abstract
Intergovernmental collaboration is needed to address global problems. Modern solutions to these problems often include data-driven methods like artificial intelligence (AI), which require large amounts of data to perform well. As AI emerges as a central catalyst in deriving effective solutions for global problems, the infrastructure that supports its data needs becomes crucial. However, data sharing between governments is often constrained due to socio-technical barriers such as concerns over data privacy, data sovereignty issues, and the risks of information misuse. Federated learning (FL) presents a promising solution as a decentralized AI methodology, enabling the use of data from multiple silos without necessitating central aggregation. Instead of sharing raw data, governments can build their own models and just share the model parameters with a central server aggregating all parameters, resulting in a superior overall model. By conducting a structured literature review, we show how major intergovernmental data-sharing challenges listed by the Organisation for Economic Co-operation and Development can be overcome by utilizing FL. Furthermore, we provide a tangible resource implementing FL linked to the Ukrainian refugee crisis that can be utilized by researchers and policymakers alike who want to implement FL in cases where data cannot be shared. Enhanced AI while maintaining privacy through FL thus allows governments to collaboratively address global problems, positively impacting governments and citizens.

Reward and Punishment Mechanism for Research Data Sharing

SowiDataNet ist eine im Aufbau befindliche Forschungsdateninfrastruktur für die Sozial- und Wirtschaftswissenschaften. Zentraler Baustein ist die Entwicklung eines web-basierten Forschungsdatenrepositoriums, welches es Forscherinnen erlaubt, ihre Daten sicher und dauerhaft zu dokumentieren, zu publizieren und mit anderen Forschern zu teilen. Die Entwicklung dieses neuen Services orientiert sich am konkreten Bedarf der Fachcommunity, was sich u.a. in der Durchführung einer detaillierten Anforderungsanalyse widerspiegelt. Ein spezieller Fokus liegt zudem auf der flexiblen Einbindung des Repositoriums in die praktischen Workflows des institutionellen Forschungsdatenmanagements.