Reply to Swartz et al.: Challenges and opportunities for identifying forced labor using satellite-based fishing vessel monitoring

We appreciate Swartz et al. (1) for highlighting several key considerations for interpreting our results (2). While we discuss many of these in our paper, we are grateful to further highlight our work’s strengths, limitations, and future opportunities. A major challenge with understanding fisheries labor abuses is a lack of data. Automatic identification system (AIS) is only used by a subset of the global fishing fleet. However, AIS is valuable for monitoring certain types of fishing vessels, especially those that are large (∼52 to 85% carry AIS) (3) and those fishing on the high seas (∼80% carry AIS) (4). Mandating AIS and unique identifiers on fishing vessels and publishing vessel registries would facilitate more inclusive AIS-based analyses (5)

Satellites can reveal global extent of forced labor in the world’s fishing fleet

“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

Satellites can reveal global extent of forced labor in the world's fishing fleet.

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