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NTIA Asks Right Questions on Consumer Privacy Law, Misses Mark By Not Addressing a Nationwide Baseline Standard

Last week the National Telecommunications and Information Administration (NTIA) published a Request for Comment (RFC) to solicit ideas on a federal privacy framework. All the right issues are on the table, but one big piece is missing: endorsing a legislative floor that prohibits exploitive and intrusive data collection and use. CDT will file comments recommending strong federal privacy legislation that creates a clear, targeted, and enforceable baseline in the U.S.

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Face Recognition Principles are a Step Forward But Congress Needs to Act

Companies are eager to deploy face tracking for their own ends, but FRTs have the potential to significantly alter our day-to-day existence in the public square. Companies and retailers should provide much more detail about their biometric data practices, and as Congress and the White House begin to discuss the contours of a federal baseline privacy law, facial recognition technologies deserve special attention.

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Endangering Student Privacy in the Name of School Safety

In the wake of tragedy, there is an understandable desire by policymakers to act with urgency. Unfortunately, this can lead to untested policy that may jeopardize the very people it is intended to protect. A recent example is Florida’s Marjory Stoneman Douglas High School Public Safety Act. Policymakers and education practitioners should be mindful of the risk of amassing and integrating large amount of data without proving its effectiveness could pose to the wellbeing of children. Limited and appropriate data sharing can play a role in supporting school safety, but it should not come at the expense of the students it is intended to protect.  

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Double Dose of FTC Comments Discuss Remedial Measures and Algorithms in Advertising

The FTC sought comment on a wide range of issues, and for this initial go-around, CDT submitted comments on two key questions: (1) the FTC’s remedial authority to deter unfair and deceptive conduct in privacy and data security matters; and (2) the implications associated with the use of algorithmic decision tools, artificial intelligence, and predictive analytics.

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On Managing Risk in Machine Learning Projects

Written by CDT summer intern Galen Harrison. The white paper “Beyond Explainability,” published by the Future of Privacy Forum and Immuta, is an attempt to sketch out how, organizationally, one can manage risk in a machine learning (ML) project. The FPF template seems appropriate for most, but not all, ML projects. When considering whether to form a process modeled after this template, practitioners should carefully consider the scope and setting of their ML operations and whether they share this template’s main concerns.

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CDT Expands Student Privacy Focus

CDT is expanding our focus on student privacy. Our goal is to provide a balanced voice and solutions-oriented resources around core issues to help educators and policymakers navigate the complex digital world. We’ll be leveraging our existing expertise on privacy & data in other sectors, including how to address bias in digital decision-making up front to prevent unintended consequences.

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