Data equity is a growing movement for more responsible data work, from analytics and data visualization to data science and machine learning to data-driven decision-making. To start with the basics: equity seeks to ensure fair treatment, equality of opportunity, and fairness in access to information and resources for all, according to the Ford Foundation. Data Equity is a set of principles and practices to guide anyone who works with data (especially data related to people) through every step of a data project through a lens of justice, equity, and inclusivity. And equity is not just an end goal, but also a framing for all data work from start to finish. As the authors of Data Feminism say, “equity is both an outcome and a process.”
Related Guides & Resources
Despite good intentions, data is sometimes collected, analyzed, and used in ways that can replicate or even amplify existing injustices and inequalities.
Resources from data.org, Funders Collaborative Hub and +5