The Federal Data Strategy Data Ethics Framework defines Data Ethics as "... the norms of behavior that promote appropriate judgments and accountability when acquiring, managing, or using data, with the goals of protecting civil liberties, minimizing risks to individuals and society, and maximizing the public good. Remaining a leader in data ethics requires individuals, agencies, and cross-agency communities to acknowledge that legal compliance does not guarantee ethical behavior. Therefore, federal leaders and data users must embrace a culture of ongoing discussion, engagement, and learning. Instead of looking at issues from a single perspective, ethical decision making is best achieved by taking a holistic approach and widening the context to weigh the greater implications of data use." Read more HERE.
Provides an overview of ethical principles and guidelines for research involving human subjects.
A tool developed by the Royal Society to help organizations identify and address ethical considerations in data projects.
Provides guidance on ethical decision-making in data science and research projects.
A structure for examining the ethical components of each stage of the data science research workflow from the Academic Data Science Alliance.
A toolkit from members of the Data Ethics Club designed to facilitate reflection on and discussion about a project's potential ethical risks.
Includes resources in three sections: demographics and population data, statistics related to particular issues, and results of public opinion and perception surveys.
The American Statistical Association's ethical guidelines.
StemEquity's synthesis of a set of basic tenets for the developing field of Quantitative Critical Theory.
A set of tools, guides, and example projects focused on using data to advance social justice.
The Urban Institute's guidelines for diversity, equity, and inclusion in data visualizations.
The Algorithmic Justice League uses art and research to advocate for ethical and equitable artificial intelligence.
