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The program will bring students to Brown from June 1 to July 30 to conduct original research: it offers a stipend of $6,300, travel costs to and from Providence, and campus housing. 

Through an interactive story and searchable database, the team hopes to help researchers, journalists, policymakers, advocates, and the public better understand the systems that enable this harm.

Working to move LLM evaluations into the hands of the communities that actually use them, Victor will be part of a cohort dedicated to researching how we can program AI to use data that doesn’t breach our privacy.

Brown CS community members are returning from the event with five accepted papers, and the work spans research areas ranging from activism to data maintenance to AI and mental health.

The leader of Brown’s NSF-funded AI research institute talks AI doom scenarios, the frontier labs’ “slowdown”, and making AI safer.

The course tackles cybersecurity issues including deepfakes, copyright, privacy, national security and decision-making.

A new project (“Where are the data workers behind AI?”) from the Center for Technological Responsibility, Reimagination and Redesign is an investigative attempt to make the data annotation and labeling industry transparent.

 

 

In a new blog post, Emily Hong, who co-led the CNTR AISLE Legislation Lab with Dre Boyd-Weatherly, takes a moment to reflect on the lab’s journey of success, growth, and iteration thus far.

A recent Brown+Beyond event welcomed alums and friends to a discussion with two campus AI leaders, Michael Littman and Brenda Rubenstein.

The need is clear: AI is rapidly permeating journalism, increasing dependency on a few Big Tech companies who control much of the infrastructure underpinning commercial AI. Many in the industry have raised alarms about what this means for editorial independence, labor, and the long-term viability of journalism itself.