A Causal Explanatory Model of Bayesian-belief Networks for Analysing the Risks of Opening Data

Ahmad Luthfi, Marijn Janssen, Joep Crompvoets

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

6 Citations (Scopus)
24 Downloads (Pure)

Abstract

Open government data initiatives result in the expectation of having open data available. Nevertheless, some potential risks like sensitivity, privacy, ownership, misinterpretation, and misuse of the data result in the reluctance of governments to open their data. At this moment, there is no comprehensive overview nor a model to understand the mechanisms resulting in risk when opening data. This study is aimed at developing a Bayesian-belief Networks (BbN) model to analyse the causal mechanism resulting in risks when opening data. An explanatory approach based on the four main steps is followed to develop a BbN. The model presents a better understanding of the causal relationship between data and risks and can help governments and other stakeholders in their decision to open data. We use the literature review base to quantify the probability of risk variables to give an illustration in the interrogating process. For the further study, we recommend using expert’s judgment for quantifying the probability of the risk variables in opening data.
Original languageEnglish
Title of host publicationProceedings of Business Modeling and Software Design - 8th International Symposium, BMSD 2018
PublisherSpringer
Pages289-297
Number of pages9
Volume319
ISBN (Print)9783319942131
DOIs
Publication statusPublished - 2018
Event8th International Symposium on Business Modeling and Software Design, BMSD 2018 - Vienna, Austria
Duration: 2 Jul 20184 Jul 2018

Publication series

NameLecture Notes In Business Information Processing
PublisherSpringer Verlag
ISSN (Print)1865-1348

Conference

Conference8th International Symposium on Business Modeling and Software Design, BMSD 2018
CountryAustria
CityVienna
Period2/07/184/07/18

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Bayesian-belief Networks
  • Causality
  • Explanatory model
  • Misinterpretation
  • Misuse
  • Open data
  • Ownership
  • Privacy
  • Relationship
  • Risks
  • Sensitivity

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