Towards a responsible machine learning approach to identify forced labor in fisheries

TL;DR

This paper presents a positive-unlabeled learning algorithm to identify vessels potentially engaged in forced labor, achieving high classification accuracy and revealing that up to 28% of vessels may operate under forced labor conditions.

Contribution

The study introduces a novel machine learning approach using vessel data to estimate and identify forced labor in fisheries, aiding responsible monitoring efforts.

Findings

89% of forced labor cases correctly identified

98% of compliant vessels correctly classified

up to 28% of vessels may use forced labor

Abstract

Many fishing vessels use forced labor, but identifying vessels that engage in this practice is challenging because few are regularly inspected. We developed a positive-unlabeled learning algorithm using vessel characteristics and movement patterns to estimate an upper bound of the number of positive cases of forced labor, with the goal of helping make accurate, responsible, and fair decisions. 89% of the reported cases of forced labor were correctly classified as positive (recall) while 98% of the vessels certified as having decent working conditions were correctly classified as negative. The recall was high for vessels from different regions using different gears, except for trawlers. We found that as much as ~28% of vessels may operate using forced labor, with the fraction much higher in squid jiggers and longlines. This model could inform risk-based port inspections as part of a broader monitoring, control, and surveillance regime to reduce forced labor. * Translated versions of the English title and abstract are available in five languages in S1 Text: Spanish, French, Simplified Chinese, Traditional Chinese, and Indonesian.