Making Ethical Decisions Is Hard!

Abstract

In the early years of the “big data revolution,” big data was described by 4 or 5 v’s (or more) - volume, velocity, variety, veracity, and value. Although intended to describe data, the 5 v’s can provide insights into what makes ethical decision making hard. The volume of work, the expectation of speed, the variety of problems, the veracity or maybe more explicitly the provenance of the data, and the value of the work viewed from the diverse and sometimes competing perspectives of stakeholders can make ethically navigating the data science landscape challenging. As the field grows, the need for resources and tools has become more urgent. In this article, we will briefly examine the history of several ethical guidelines and frameworks.

Additional information

Notes on contributors

Stephanie Shipp

Stephanie Shipp leads the Social and Decision Analytics Division at the University of Virginia’s Biocomplexity Institute. Her research 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 ASA’s Committee on Professional Ethics. Serving on National Academies committees, Shipp evaluates and informs government science and engineering programs. She received her PhD in economics from George Washington University.

Donna LaLonde

Donna LaLonde is director of strategic initiatives and outreach at the American Statistical Association, where she works with colleagues to advance the association’s vision and mission and supports activities associated with presidential initiatives, professional development, and accreditation. Before joining the ASA, LaLonde was a faculty member at Washburn University and served in various administrative positions, including interim chair of the Education Department and associate vice president for academic affairs.

Wendy Martinez

Wendy Martinez is the senior mathematical statistician for data science in the U.S. Census Bureau Research and Methodology Directorate. Previously, she served as director of the Bureau of Labor Statistics Mathematical Statistics Research Center and worked in several research positions throughout the Department of Defense. Her research interests include computational statistics, exploratory data analysis, text analysis, and data visualization.