Proceedings of the National Academy of Sciences (PNAS) issued a report focusing on how satellites reveal global extent of forced labor in the world’s fishing fleet.
According to the report, forced labor in fisheries is increasingly recognized as a human rights crisis.
Although, 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.
However, due to the satellite data and machine learning, individuals are from now on able to detect these high-risk vessels, while identify the major risk of forced labor within global’s fishing fleet.
As explained, satellite information offers new opportunities for this market, enforcement, and policy interventions.
At the same time, it provides a proof of concept for how remotely sensed dynamic individual behavior can be used to infer forced labor abuses.
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.
…as researchers stated.
What is more, the report revealed that between 14% and 26% of vessels were high-risk, and shown that patterns of where these vessels fished and which ports they visited.
In fact, between 57,000 and 100,000 individuals worked on these vessels, many of whom may have been forced labor victims.
We find that fishing vessels using forced labor behave differently than the rest of the global fishing fleet. Our approach to identifying individual vessels with a high risk of forced labor could improve existing monitoring efforts. Countries, enforcement bodies, and international agencies could use this model to conduct more targeted vessel inspections. Use of AIS or vessel monitoring system devices and detailed vessel registries could be further mandated to provide more accurate risk assessment for more vessels
For the records, behind the study were Gavin G. McDonald, Christopher Costello, Jennifer Bone, Reniel B. Cabral, Valerie Farabee, Timothy Hochberg, David Kroodsma, Tracey Mangin, Kyle C. Meng, and Oliver Zahn.
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