Today, The Sedona Conference® (TSC) announced that its US Biometric Systems Privacy Primer has been published for public comment.
The Primer has been released by TSC’s Working Group 11 on Data Security and Privacy Liability (WG11). It provides a general introduction to biometric systems and a summary of existing U.S. laws regulating the collection, use, and sharing of the biometric information these technologies collect.
This Primer is written as a resource for lawyers, judges, legislators, and other policymakers. It provides a general guide to the relationships among the technical, legal, and policy aspects of biometric systems—with a particular focus on the privacy and related concerns these systems may raise.
As Part II explains, the Primer focuses primarily on biometric recognition systems (which include both identity verification and identification systems) by private organizations. While the Primer generally limits its discussion to private-sector applications, it acknowledges—and in several places, analyzes—the overlap between public and private applications, including the risks raised by what the group terms “function creep.”
The 39-page Primer (available here for free download) provides an overview of biometric recognition systems, discusses the benefits and drawbacks of biometric systems, provides the current U.S. biometric privacy legal landscape and discusses several general considerations organizations should consider when selecting or designing biometric recognition systems.
The Primer is open for public comment through April 8, 2024. Questions and comments may be sent to comments@sedonaconference.org. The drafting team will carefully consider all comments received, and determine what edits are appropriate for the final version. You know the drill. 😉
So, what do you think? Are you concerned about the privacy risks posed by biometric systems? Please share any comments you might have or if you’d like to know more about a particular topic.
Image created using GPT-4’s Image Creator Powered by DALL-E, using the term “robot getting a facial recognition scan”.
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