Working, dreaming
Former fellow at @hks_digital★ affiliate to @bkcharvard ★ Feminist and all that comes with it
Dessa vez escrevi sobre identidades para agentes, porque identificá-los e como podem ser agentes de preservação da privacidade.
Fico gradicida se me derem likes e deixarem comentários <3
Vamos gente! Campanha da PrograMaria acaba hoje, daqui a 7 horas! Falta pouco:
Dynamic pricing seems to ask: “What are the conditions right now?” Surveillance pricing asks: “Who are you, and how much can we extract from you?”
https://thetyee.ca/Analysis/2026/05/05/Browsing-History-Grocery-Bill/
I feel like this piece in @thetyee is close to @pluralistic 's heart
Be incredibly skeptical of anyone offering privacy advice or promoting specific applications and services, especially those promoting software that relies on servers based in hostile jurisdictions (this goes double for VPNs and communication apps).
There is so much terrible advice floating around right now.
📣 New Paper! w/@HeidyKhlaaf + @sarahbmyers
We put the narrative on AI risks & NatSec under a microscope, finding the focus on hypothetical AI bioweapons is warping policy and ignoring real & serious harms of current AI use in surveillance, targeting, etc.
Instead of crafting solutions for hypothetical harms, we advocate focusing on already existing and very significant safety issues--namely AI’s reliance on PII & the vulns created by foundation models' use in NatSec.
Discussions regarding the dual use of foundation models and the risks they pose have overwhelmingly focused on a narrow set of use cases and national security directives-in particular, how AI may enable the efficient construction of a class of systems referred to as CBRN: chemical, biological, radiological and nuclear weapons. The overwhelming focus on these hypothetical and narrow themes has occluded a much-needed conversation regarding present uses of AI for military systems, specifically ISTAR: intelligence, surveillance, target acquisition, and reconnaissance. These are the uses most grounded in actual deployments of AI that pose life-or-death stakes for civilians, where misuses and failures pose geopolitical consequences and military escalations. This is particularly underscored by novel proliferation risks specific to the widespread availability of commercial models and the lack of effective approaches that reliably prevent them from contributing to ISTAR capabilities. In this paper, we outline the significant national security concerns emanating from current and envisioned uses of commercial foundation models outside of CBRN contexts, and critique the narrowing of the policy debate that has resulted from a CBRN focus (e.g. compute thresholds, model weight release). We demonstrate that the inability to prevent personally identifiable information from contributing to ISTAR capabilities within commercial foundation models may lead to the use and proliferation of military AI technologies by adversaries. We also show how the usage of foundation models within military settings inherently expands the attack vectors of military systems and the defense infrastructures they interface with. We conclude that in order to secure military systems and limit the proliferation of AI armaments, it may be necessary to insulate military AI systems and personal data from commercial foundation models.
datum (n=1)
Sarah Jamie Lewis
Meredith Whittaker
mcc