Harmful Algae Forecasting through an Ocean Data Justice Lens.

Area of Science:

• Environmental Science and Data Science
• Oceanography and Ecology
• Data Justice and Algorithmic Accountability

Background:

• Harmful algal bloom (HAB) monitoring and forecasting are expanding globally through networked data.
• Existing ocean forecasting systems have demonstrated unintended consequences and out-of-scope applications.
• The field of Data Justice offers a framework for analyzing harm from data technologies, increasingly applied to environmental issues.

Purpose of the Study:

• To apply a Data Justice lens to the development of HAB prediction systems.
• To identify potential biases and inequities in global HAB data repositories and forecasting efforts.
• To provide guidance for equitable and accessible global data and forecast systems for HABs.

Main Methods:

• Surveyed three major global data repositories: Ocean Biodiversity Information System, Harmful Algae Event Database, and AlgaeBase.
• Analyzed a literature corpus and the broader ocean forecasting literature.
• Examined patterns in data collection, knowledge concentration, and taxonomic visibility.

Main Results:

• HAB monitoring and knowledge are concentrated in high-GDP, northern North Atlantic nations.
• Data patterns reflect and potentially reinforce existing global economic and political inequalities in ocean health.
• Biases exist in the visibility of algal taxa, favoring those relevant to specific regions.

Conclusions:

• Current HAB data systems may perpetuate global ocean stresses and inequities.
• Principles from Data Justice, Design Justice, and Algorithmic Accountability are crucial for equitable HAB forecasting.
• Centering equity and access is essential for building inclusive global data and forecast systems for HABs.