A Data-Sharing Model to Secure Borders Using an Artificial-Intelligence-Based Risk Engine and Big-Data Concepts
- 1 Department of Enterprise Engineering, University of Rome "Tor Vergata", Italy
The primary aim of this research is to develop a framework for data management and sharing that will enable countries to share complex data about known and unknown high-risk passengers to streamline border-control security processes through the use of big data analytics and Artificial Intelligence (AI). A total of 15 semi-structured interviews were used to gather qualitative data. A thematic analysis approach was used to analyze the data and the interview data were coded using NVivo 11 qualitative-data-analysis software. Five aggregate dimensions were developed, comprising nine themes and nine sub-themes, based on 39 codes that emerged from the data. This research has several theoretical and practical contributions. Primarily, the development of an AI-based risk engine will not only improve how borders are enforced but will also lead to the integration of new technology for border control, thus boosting securitization, decreasing human factors/error, and minimizing border-related crime, and helping to manage healthcare issues.
Copyright: © 2022 Mohammad S. Al Rousan and Benedetto Intrigila. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Risky Passengers
- Border Security
- Big Data
- Artificial Intelligence