https://people.eecs.berkeley.edu/~emmapierson/
(please reshare)
We seek applicants with experience in language modeling who are excited about high-impact applications in the health and social sciences!
More info in thread
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(please reshare)
We seek applicants with experience in language modeling who are excited about high-impact applications in the health and social sciences!
More info in thread
1/3
Despite recent results, SAEs aren't dead! They can still be useful to mech interp, and also much more broadly: across FAccT, computational social science, and ML4H. 🧵
Despite recent results, SAEs aren't dead! They can still be useful to mech interp, and also much more broadly: across FAccT, computational social science, and ML4H. 🧵
To the NIH: health inequality remains a vital topic to support the health of all Americans. As we prove, failing to account for it biases estimates for everyone.
To the NIH: health inequality remains a vital topic to support the health of all Americans. As we prove, failing to account for it biases estimates for everyone.
In the meantime, 🧵 below and 🔗 here: arxiv.org/abs/2506.04419 !
In the meantime, 🧵 below and 🔗 here: arxiv.org/abs/2506.04419 !
📄: arxiv.org/abs/2412.16406
My answer today in Nature.
We will not be cowed. We will keep using AI to build a fairer, healthier world.
www.nature.com/articles/d41...
My answer today in Nature.
We will not be cowed. We will keep using AI to build a fairer, healthier world.
www.nature.com/articles/d41...
1) Geospatial trends: Cavalier King Charles Spaniels are common in Manhattan; the opposite is true for Yorkshire Terriers.
We're releasing a new dataset, MIGRATE: annual flows between 47 billion pairs of US Census areas. MIGRATE is:
- 4600x more granular than existing public data
- highly correlated with external ground-truth data
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We're releasing a new dataset, MIGRATE: annual flows between 47 billion pairs of US Census areas. MIGRATE is:
- 4600x more granular than existing public data
- highly correlated with external ground-truth data
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Our method, HypotheSAEs, produces interpretable text features that predict a target variable, e.g. features in news headlines that predict engagement. 🧵1/
Our method, HypotheSAEs, produces interpretable text features that predict a target variable, e.g. features in news headlines that predict engagement. 🧵1/
What features of a headline predict engagement?
What features of a clinical note predict whether a patient will develop cancer?
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What features of a headline predict engagement?
What features of a clinical note predict whether a patient will develop cancer?
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If you're a graduate student, come learn about ML/AI and its uses throughout economics.
Apply by March 28. The application and more info can be found here: www.chicagobooth.edu/research/cen...
If you're a graduate student, come learn about ML/AI and its uses throughout economics.
Apply by March 28. The application and more info can be found here: www.chicagobooth.edu/research/cen...
Free access link: rdcu.be/d6aul
Longer version on my website: shorturl.at/muwts
Free access link: rdcu.be/d6aul
Longer version on my website: shorturl.at/muwts
85% of equity-related LLM papers focus on *harms*.
But also vital are the equity-related *opportunities* LLMs create: detecting bias, extracting structured data, and improving access to health info.
85% of equity-related LLM papers focus on *harms*.
But also vital are the equity-related *opportunities* LLMs create: detecting bias, extracting structured data, and improving access to health info.
tomorrow, Dec. 15, at 4:30pm on some ongoing work on modeling multi-stage selection problems in clinical settings. Work done with (high school senior!) Sophia Lin, Bonnie Berger, and @emmapierson.bsky.social. I hope to see you there!
tomorrow, Dec. 15, at 4:30pm on some ongoing work on modeling multi-stage selection problems in clinical settings. Work done with (high school senior!) Sophia Lin, Bonnie Berger, and @emmapierson.bsky.social. I hope to see you there!