JD-PhD @medialab and @Harvard_Law. Computational lawyer.
Joined October 2022
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What happens when we fall in love with our robots?
In the @techreview, @patpat_mit and I discuss the challenges of regulating AI companions and explore new regulatory approaches that address the unique harms of addictive intelligence.
technologyreview.com/2024/08…
Benchmarks and risk assessments don't mean much when you can't be sure what model you're really using. Here we demonstrate a cryptographic approach to verifiable model attestations -- an exciting example of Regulation by Design for AI!
Paper: arxiv.org/pdf/2402.02675.pdf
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Proud to have helped lead the legal work on the Data Provenance Initiative that addresses some of the key technical and legal challenges in AI Training Data!
Some key legal insights below:
📢Announcing the🌟Data Provenance Initiative🌟
🧭A rigorous public audit of 1800+ instruct/align datasets
🔍Explore/filter sources, creators & license conditions
⚠️We see a rising divide between commercially open v closed licensed data
🌐: dataprovenance.org
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3) Fair use may not offer much protection when datasets were created for the sole purpose of training an AI model