Bolstering environmental data science with equity-centered approaches

Environmental data science and machine learning (ML) are increasingly vital for addressing ecological challenges. However, these technologies can inadvertently perpetuate biases present in their training data, leading to socioecological inequities. The field faces issues such as data integrity, algorithmic bias, and model overfitting, which necessitate a deeper understanding and more equitable approaches.