Gavin McDonald

2papers

2 Papers

LGFeb 3, 2023
Towards a responsible machine learning approach to identify forced labor in fisheries

Rocío Joo, Gavin McDonald, Nathan Miller et al.

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.

LGNov 24, 2025
Closing Gaps in Emissions Monitoring with Climate TRACE

Brittany V. Lancellotti, Jordan M. Malof, Aaron Davitt et al.

Global greenhouse gas emissions estimates are essential for monitoring and mitigation planning. Yet most datasets lack one or more characteristics that enhance their actionability, such as accuracy, global coverage, high spatial and temporal resolution, and frequent updates. To address these gaps, we present Climate TRACE (climatetrace.org), an open-access platform delivering global emissions estimates with enhanced detail, coverage, and timeliness. Climate TRACE synthesizes existing emissions data, prioritizing accuracy, coverage, and resolution, and fills gaps using sector-specific estimation approaches. The dataset is the first to provide globally comprehensive emissions estimates for individual sources (e.g., individual power plants) for all anthropogenic emitting sectors. The dataset spans January 1, 2021, to the present, with a two-month reporting lag and monthly updates. The open-access platform enables non-technical audiences to engage with detailed emissions datasets for most subnational governments worldwide. Climate TRACE supports data-driven climate action at scales where decisions are made, representing a major breakthrough for emissions accounting and mitigation.