Fair automated assessment of noncompliance in cargo ship networks
نویسندگان
چکیده
Abstract Cargo ships navigating global waters are required to be sufficiently safe and compliant with international treaties. Governmental inspectorates currently assess in a rule-based manner whether ship is potentially noncompliant thus needs inspection. One of the dominant characteristics this assessment ‘colour’ flag flying, where countries positive reputation have so-called ‘white flag’. The colour may disproportionately influence inspector, causing more frequent stricter inspections flying non-white flag, resulting confirmation bias historical inspection data. In paper, we propose an automated approach for noncompliance, realising two important contributions. First, reduce by using fair classifiers that decorrelate from risk classification returned model. Second, extract mobility patterns cargo network, allowing us derive meaningful features classification. Crucially, these model behaviour ship, rather than its static properties. Our shows both higher overall prediction performance improved fairness respect flag. Ultimately, work enables better target ships, thereby improving maritime safety environmental protection.
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ژورنال
عنوان ژورنال: EPJ Data Science
سال: 2022
ISSN: ['2193-1127']
DOI: https://doi.org/10.1140/epjds/s13688-022-00326-w