TY - GEN
T1 - A Causal Explanatory Model of Bayesian-belief Networks for Analysing the Risks of Opening Data
AU - Luthfi, Ahmad
AU - Janssen, Marijn
AU - Crompvoets, Joep
N1 - 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.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Bayesian-belief Networks
KW - Causality
KW - Explanatory model
KW - Misinterpretation
KW - Misuse
KW - Open data
KW - Ownership
KW - Privacy
KW - Relationship
KW - Risks
KW - Sensitivity
UR - http://resolver.tudelft.nl/uuid:3896b5d1-60a3-4287-86ad-6d325840a418
UR - http://www.scopus.com/inward/record.url?scp=85049667766&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-94214-8_20
DO - 10.1007/978-3-319-94214-8_20
M3 - Conference contribution
AN - SCOPUS:85049667766
SN - 9783319942131
VL - 319
T3 - Lecture Notes In Business Information Processing
SP - 289
EP - 297
BT - Proceedings of Business Modeling and Software Design - 8th International Symposium, BMSD 2018
PB - Springer
T2 - 8th International Symposium on Business Modeling and Software Design, BMSD 2018
Y2 - 2 July 2018 through 4 July 2018
ER -