Harmful Algae Forecasting through an Ocean Data Justice Lens

Abstract

Forecasting systems for harmful algal blooms (HABs) are becoming more common, as HAB monitoring is increasingly networked and aggregated at national and global scales. Ocean forecasting programs in other fields have had unintended consequences and out-of-scope uses. The field of Data Justice provides a perspective for understanding unintended harm caused by the application of data technologies generally and is now being applied to environmental fields. With the proliferation of artificial intelligence algorithms and widespread environmental surveillance, it is timely to turn the Data Justice lens toward environmental applications such as the prediction of HABs. We surveyed three global data repositories underpinning HAB monitoring and prediction efforts: the Ocean Biodiversity Information System, the Harmful Algae Event Database, and AlgaeBase, as well as a literature corpus and the ocean forecasting literature. The patterns we found reflect and potentially reinforce the existing economic and political relations that underpin global ocean stresses, with monitoring and knowledge concentrated in high-GDP, northern North Atlantic nations, and biases toward the visibility of taxa relevant to those regions. Principles from Data Justice research, such as from Design Justice and Algorithmic Accountability, provide guidance for centering equity and access, while building global data and forecast systems for HABs.

Keywords: Data Justice; Forecasting; Global; Harmful Algal Bloom; Ocean Digital Twin; Ocean Forecasting.