Applying Algorithmic Accountability Frameworks with Domain-specific Codes of Ethics - A Case Study in Ecosystem Forecasting for Shellfish Toxicity in the Gulf of Maine.

Ecological forecasts are used to inform decisions that can havesignificant impacts on the lives of individuals and on the healthof ecosystems. These forecasts, or models, embody the ethics oftheir creators as well as many seemingly arbitrary implementationchoices made along the way. They can contain implementationerrors as well as reflect patterns of bias learned when ingestingdatasets derived from past biased decision making. Principles andframeworks for algorithmic accountability allow a wide range ofstakeholders to place the results of models and software systemsinto context. We demonstrate how the combination of algorithmicaccountability frameworks and domain-specific codes of ethics helpanswer calls to uphold fairness and human values, specifically indomains that utilize machine learning algorithms. This helps avoidmany of the unintended consequences that can result from deploy-ing "black box" systems to solve complex problems. In this paper,we discuss our experience applying algorithmic accountability prin-ciples and frameworks to ecosystem forecasting, focusing on a casestudy forecasting shellfish toxicity in the Gulf of Maine. We adaptexisting frameworks such as Datasheets for Datasets and ModelCards for Model Reporting from their original focus on personallyidentifiable private data to include public datasets, such as thoseoften used in ecosystem forecasting applications, to audit the casestudy. We show how high level algorithmic accountability frame-works and domain level codes of ethics compliment each other,incentivizing more transparency, accountability, and fairness inautomated decision-making systems.