Characterizing Disinformation Risk to Open Data in the Post-Truth Era

Abstract

Curated, labeled, high-quality data is a valuable commodity for tasks such as business analytics and machine learning. Open data is a common source of such data—for example, retail analytics draws on open demographic data, and weather forecast systems draw on open atmospheric and ocean data. Open data is released openly by governments to achieve various objectives, such as transparency, informing citizen engagement, or supporting private enterprise. Critical examination of ongoing social changes, including the post-truth phenomenon, suggests the quality, integrity, and authenticity of open data may be at risk. We introduce this risk through various lenses, describe some of the types of risk we expect using a threat model approach, identify approaches to mitigate each risk, and present real-world examples of cases where the risk has already caused harm. As an initial assessment of awareness of this disinformation risk, we compare our analysis to perspectives captured during open data stakeholder consultations in Canada.

Individual Perspectives on Data Sharing: Human Factors Impacting the Digital Economy

“…Wirtz et al (2016) describe identify "perceived organizational transparency" as a driver of personal assessments on whether or not to share open data: if data custodians believe the government is not transparent, they are less likely to make government open data available. This chilling effect from political leadership has been recognized as a disinformation risk in the post-truth era (Colborne and Smit, 2020).…”

“…Hall (2013) reports that researchers fear that without proper context for the data they share, there is a possibility that it could be misused and politicized inappropriately (e.g. Colborne & Smit, 2020).…”

Open Government Data as a Reform and Ecosystem : A conceptual framework for evolution and health

“…This group of problems can also be reformulated as risks towards OGD, such as technology (e.g., outdated data collection methods and IT-vulnerabilities), management (e.g., insufficient investment in skills and funding), and environment (e.g., lack of governance experts and immature OGD knowledge) risks (Wang, Zhao, Zhao, and Chu, 2019). OGD is also open to risks from post-truth thinking, which can lead to the withdrawal of datasets, dilution of data with bias, tweaks of data to make "corrections", reduce spending to reduce the volume of high-quality data, obscure or obfuscate the location of data access, and not adding or updating data (Colborne and Smit, 2020). Impediments can be a problem in an OGD reform and an OGD ecosystem.…”