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

Conference paper (2018)

Authors

A. Luthfi Universitas Islam Indonesia,
Joep Crompvoets Katholieke Universiteit Leuven
Copyright: © 2018 A. Luthfi, M.F.W.H.A. Janssen, Joep Crompvoets
DOI: https://doi.org/10.1007/978-3-319-94214-8_20
Privacy Relationship Causality Open data Sensitivity Risks Ownership Misuse Bayesian-belief Networks Explanatory model Misinterpretation
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Published Date

2018

Language

English

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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.