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.

虽然世界渔船队的强迫劳动已被广泛记录，但其程度仍然未知。以前不存在远程识别可能在全球范围内从事这些滥用行为的个体渔船的方法。通过结合人权从业者的专业知识和卫星船只监测数据，我们表明据报使用强迫劳动的船只与其他船只的行为方式系统性地不同。我们通过使用机器学习从 16,000 艘工业延绳钓船、鱿鱼钓船和拖网渔船中识别出高风险船只，从而利用这一洞察力。我们的模型揭示了 14% 到 26% 的船只是高风险的，并且还揭示了这些船只在哪里捕鱼以及它们访问了哪些港口的模式。57,000 到 100,000 人在这些船上工作，其中许多人可能是强迫劳动的受害者。这些信息为应对这一人道主义悲剧的新干预措施提供了前所未有的机会。更广泛地说，这项研究证明了使用遥感检测强迫劳动滥用的概念证明。