From the institutes, labs and civil-society groups.

By Paige Garvin, FPF Communications Intern The Future of Privacy Forum hosted its third annual DC Privacy Forum: Advancing Principled Data Protection, AI, and Digital Governance Practices on June 10th, 2026. This year’s Forum gathered government officials, academics, civil society representatives, and privacy professionals to discuss developments in AI governance, privacy regulation, youth online safety, […]

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Sold to the public as a foreign surveillance tool, Section 702 is the law has let intelligence agencies spy on millions of Americans’ private conversations without a warrant. Despite years of revelations about this law's misuse, Congress has repeatedly reauthorized Section 702 without meaningful reform. Until this month, that is, when it finally lapsed in a major victory for privacy. In our latest EFFector newsletter , we're covering the expiration of Section 702 and what happens next . JOIN OUR

A new report shows how AI systems are already failing LGBTQ+ users. The problem is: it may also the best way to fix moderation issues that traditional systems never managed to address.

While the US debates whether to regulate AI at all, Brazil has built the most detailed AI-and-elections rulebook of any democracy and the gap between the two is becoming a headache for companies

An exclusive survey of AI workers in Kenya reveals how automated management affects their livelihoods. Unions and advocacy groups are beginning to fight back.

Matt Garman argues that junior employees are as necessary as ever. But AWS now sells agents that can recruit, code, and process claims. Will the balance hold?

Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, which appears as a straight line on a log-log plot. We can view scaling laws as a framework for describing the relationship between compute, loss, model size and data; at its core, it is about how to allocate precious compute optimally between $N$