Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity

● Socioecological inequity must be understood to improve environmental data science. ● The Systemic Equity Framework and Wells-Du Bois Protocol mitigate inequity. ● Addressing irreproducibility in machine learning is vital for bolstering integrity. ● Future directions include policy enforcement and systematic programming. Socioecological inequity in environmental data science—such as inequities deriving from data-driven approaches and machine learning (ML)—are current issues subject to debate and evolution. There is growing consensus around embedding equity throughout all research and design domains—from inception to administration, while also addressing procedural, distributive, and recognitional factors. Yet, practically doing so may seem onerous or daunting to some. The current perspective helps to alleviate these types of concerns by providing substantiation for the connection between environmental data science and socioecological inequity, using the Systemic Equity Framework, and provides the foundation for a paradigmatic shift toward normalizing the use of equity-centered approaches in environmental data science and ML settings. Bolstering the integrity of environmental data science and ML is just beginning from an equity-centered tool development and rigorous application standpoint. To this end, this perspective also provides relevant future directions and challenges by overviewing some meaningful tools and strategies—such as applying the Wells-Du Bois Protocol, employing fairness metrics, and systematically addressing irreproducibility; emerging needs and proposals—such as addressing data-proxy bias and supporting convergence research; and establishes a ten-step path forward. Afterall, the work that environmental scientists and engineers do ultimately affect the well-being of us all.