Abstract

Data Science is transforming social science research and ethical dimensions should not be compromised. Researchers can now design studies based on repurposing existing administrative and opportunity data without the consent or awareness of those providing the data. Discussions about ethics need to be a natural part of every research project, especially when norms from multiple disciplines (data science, social science) may require integration. Professional Ethical guidelines, such as the American Statistical Association's Ethical Guidelines for Statistical Practice, are intended to help statistics practitioners make decisions ethically. How does one apply these guidelines to their research and day-to-day statistical practice and evaluate the relative impacts of decisions made? One approach is an ethical checklist, completed at each research stage and shared publicly throughout the study to help researchers identify and frame potential concerns over the project's life—the timely consideration, review, and communication, of ethical decisions. A key part of this checklist is the assessment of implicit biases. Ethical principles describe the implementation of everyday practices around documentation and transparency for data, methods, and communication. These practices are determined through ongoing discussion, questioning, and constructive criticism that features the chosen ethical practice standards. We will discuss the history of these ethical principles and our experiences implementing them into our research. Stephanie Shipp is the Interim Director for the Social and Decision Analytics Division, Biocomplexity Institute, University of Virginia. Dr. Shipp's work spans topics related to using all data to advance policy, the science of data science, community analytics, and innovation. She leads and engages in local, state, and federal projects to assess data quality and the ethical use of new and traditional data sources. She is a member of the American Statistical Association's Committee on Professional Ethics.