Compiled by Sarah Willrich.
Articles
The Censorship Machine: Silencing the National Security Workforce
Julia Curlee analyzed why so many purged national security officials choose to remain silent about their treatment. Curlee explained that the Trump administration has leverage to punish dissent in the government, private sector, and civil society, resulting in a system of self-censorship where former officials would rather stay quiet than risk their livelihoods.
The silence is now being put in writing. National security employees have always signed nondisclosure agreements covering classified information; in May, the administration proposed the
[first governmentwide agreement], covering nearly everything any federal employee learns on the job—down to “pre-decisional or deliberative material”—with obligations that do not end with federal service. Under a companion[suitability rule], refusing to sign is[grounds for removal]—and debarment from federal employment for up to three years.The arithmetic is not hard. The protections are gone, appeal forums are clogged, and the first to speak out will be the first fired.
Replacement Through Knowledge Acquisition
William Dinneen and Ben Vagle warned about the rapid development of replacement through knowledge acquisition (RKA), the process of artificial intelligence (AI) models extracting data from companies that use them, allowing AI providers to directly compete with those companies. Dinneen and Vagle explained that the legal remedies to RKA all have limitations, and suggested an approach that maintains optionality to respond as RKA’s impact becomes clearer.
The industries most vulnerable to RKA are those in which information shared with AI providers directly implicates the industries’ products or services—for instance, software or legal. In such industries, AI usage data from customers could most plausibly be employed to develop competing products or services. For example, employees of an enterprise software company might use AI models to write code or analyze data, disclosing critical information about enterprise software to AI labs. Similarly, in the legal industry, lawyers might employ AI models to execute legal workflows, revealing proprietary legal strategies. And in the financial services industry, AI models might reveal sensitive information about investment strategies. Moreover, if many employees within the same enterprise are using the same AI models simultaneously, the available information, once linked and synthesized, could reveal much more about the enterprise than any single employee has access to.
Podcasts
Lawfare Daily** : Censorship Incentives with Julia Curlee and Mike Feinberg: **Curlee and
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