Featured Journal Content
PNAS – Proceedings of the National Academy of Sciences of the United States of America
[Accessed 26 Dec 2020]
Satellites can reveal global extent of forced labor in the world’s fishing fleet
Gavin G. McDonald, Christopher Costello, Jennifer Bone, Reniel B. Cabral, Valerie Farabee, Timothy Hochberg, David Kroodsma, Tracey Mangin, Kyle C. Meng, and Oliver Zahn
PNAS first published December 21, 2020. https://doi.org/10.1073/pnas.2016238117
Forced labor in fisheries is increasingly recognized as a human rights crisis. Until recently, its extent was poorly understood and no tools existed for systematically detecting forced labor risk on individual fishing vessels on a global scale. Here we use satellite data and machine learning to identify these high-risk vessels and find widespread risk of forced labor in the world’s fishing fleet. This information provides new opportunities for unique market, enforcement, and policy interventions. This also provides a proof of concept for how remotely sensed dynamic individual behavior can be used to infer forced labor abuses.
While forced labor in the world’s fishing fleet has been widely documented, its extent remains unknown. No methods previously existed for remotely identifying individual fishing vessels potentially engaged in these abuses on a global scale. By combining expertise from human rights practitioners and satellite vessel monitoring data, we show that vessels reported to use forced labor behave in systematically different ways from other vessels. We exploit this insight by using machine learning to identify high-risk vessels from among 16,000 industrial longliner, squid jigger, and trawler fishing vessels. Our model reveals that between 14% and 26% of vessels were high-risk, and also reveals patterns of where these vessels fished and which ports they visited. Between 57,000 and 100,000 individuals worked on these vessels, many of whom may have been forced labor victims. This information provides unprecedented opportunities for novel interventions to combat this humanitarian tragedy. More broadly, this research demonstrates a proof of concept for using remote sensing to detect forced labor abuses.