Events
Privacy Week SoDa Symposium: “Balancing Statistical and Non-Statistical Uses of Federal Data: Privacy, Governance, and Public Trust”
Event Start Date: Wednesday, January 28, 2026 - 2:00 pm
Event End Date: Wednesday, January 28, 2026 - 3:30 pm
Location: Virtual
Contact: SoDa Center
The U.S. federal government has long maintained a clear line between the statistical and non-statistical uses of the public’s information. The former includes purposes such as producing the Consumer Price Index; the latter includes determinations, such as about a specific household eligibility for a program. This functional separation has guided federal data practice for 50 years, and this safeguard is encoded in federal laws such as U.S. Code Title 13 and Confidential Information Protection and Statistical Efficiency Act (CIPSEA).
This Privacy Day webinar examines the origins of this boundary (statistical vs. non-statistical purposes), how it is enforced today, and what it will take to preserve this crucial principle in an evolving federal data landscape. This event is co-sponsored by the American Statistical Association’s Privacy and Confidentiality Committee and the UMD SoDa Center. There will be two presentations with Q&A sessions after:
Presentation 1: The Evolution and Interpretation of “Statistical Purposes”
Presented by: Sallie Ann Keller, Chief Scientist and Associate Director for Research and Methodology, U.S. Census Bureau; Michael B. Hawes, Senior Statistician for Scientific Communication, U.S. Census Bureau
Abstract: Data subjects are often told their information will be used for “statistical purposes,” and statistical agencies are legally required to use these data “for statistical purposes only,” but what does this actually mean? In today’s data-driven world, statistics is a far-reaching and expansive discipline, actively used across virtually all scientific fields, and extensively leveraged, with myriad daily implications, both large and small, for the average person. With statistics being so broad a discipline, one might expect that the term “statistical purposes” (intuitively, those actions taken in pursuit of the generation, use, or interpretation of statistics), would be similarly expansive. We find in practice the definition has both been narrowed and expanded over time. In this presentation, we explore how the term “statistical purposes” has been ambiguously defined in law and regulation, and how it has been interpreted in practice by the U.S. federal statistical system. We will analyze how the legal and ethical guardrails of “statistical purposes” align with the core objectives and mission of a statistical agency.
Presentation 2: The (Real and Imagined) Bounds of Statistical Purpose
Presented by: Alexandra Wood, Visiting Assistant Professor of Artificial Intelligence, Policy, and Society, Department of Political Science, Purdue University
Abstract: Statistical purpose is a fundamental yet under-explored concept embedded in regulatory frameworks for privacy, data protection, and statistical confidentiality. As a special case of purpose limitation, it bounds the scope of permissible processing activities and implicates regulatory requirements distinct from those applicable to processing for other purposes. Originally intended to protect statistical integrity in the context of official statistics, the concept of statistical purpose has increasingly been applied in broader contexts. However, there is a notable lack of a consistent definition or clear guidance for determining when information is being processed for statistical purposes. In the absence of such guidance, a very wide range of interpretations of statistical purpose has emerged, often founded in differing assumptions about informational risk. This talk examines these interpretations, the assumptions they rely on, and their implications for policy and practice. Drawing on insights from statistical policy, ethics, and privacy research, it offers recommendations for clarifying and strengthening statistical purpose provisions in law and guidance.
Speaker(s): Sallie Ann Keller, Michael B. Hawes, Alexandra Wood