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Dan Jurafsky | Stanford HAI

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peopleFaculty

Dan Jurafsky

Jackson Eli Reynolds Professor in Humanities, and Professor of Computer Science, Stanford University

External Bio
Latest Work
Labeling AI-Generated Content May Not Change Its Persuasiveness
Isabel Gallegos, Dr. Chen Shani, Weiyan Shi, Federico Bianchi, Izzy Benjamin Gainsburg, Dan Jurafsky, Robb Willer
Quick ReadJul 30
policy brief

This brief evaluates the impact of authorship labels on the persuasiveness of AI-written policy messages.

Demographic Stereotypes in Text-to-Image Generation
Federico Bianchi, Pratyusha Kalluri, Esin Durmus, Faisal Ladhak, Myra Cheng, Debora Nozza, Tatsunori Hashimoto, Dan Jurafsky, James Zou, Aylin Caliskan
Quick ReadNov 30
policy brief

This brief tests a variety of ordinary text prompts to examine how major text-to-image AI models encode a wide range of dangerous biases about demographic groups.

Foundation Models and Copyright Questions
Peter Henderson, Xuechen Li, Dan Jurafsky, Tatsunori Hashimoto, Mark A. Lemley, Percy Liang
Quick ReadNov 02
policy brief

This brief warns that fair use may not fully shield U.S. foundation models trained on copyrighted data and calls for combined legal and technical safeguards to protect creators.

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All Related

Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words
Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky
May 22, 2022
Research
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Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words

Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words

Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky
May 22, 2022

Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words

Your browser does not support the video tag.
Research
Richer Countries and Richer Representations
Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky
May 01, 2022
Research
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Richer Countries and Richer Representations

Richer Countries and Richer Representations

Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky
May 01, 2022

Richer Countries and Richer Representations

Your browser does not support the video tag.
Research
Diversifying History: A Large-Scale Analysis of Changes in Researcher Demographics and Scholarly Agendas
Stephan Risi, Mathias W. Nielsen, Emma Kerr, Emer Brady, Lanu Kim, Daniel A. McFarland, Dan Jurafsky, James Zou, Londa Schiebinger
Jan 01, 2022
Research
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Diversifying History: A Large-Scale Analysis of Changes in Researcher Demographics and Scholarly Agendas

Diversifying History: A Large-Scale Analysis of Changes in Researcher Demographics and Scholarly Agendas

Stephan Risi, Mathias W. Nielsen, Emma Kerr, Emer Brady, Lanu Kim, Daniel A. McFarland, Dan Jurafsky, James Zou, Londa Schiebinger
Jan 01, 2022

Diversifying History: A Large-Scale Analysis of Changes in Researcher Demographics and Scholarly Agendas

Your browser does not support the video tag.
Research
Assessing the accuracy of automatic speech recognition for psychotherapy
Adam Miner, Albert Haque, Jason Fries, Scott Fleming, Denise Wilfley, Terence Wilson, Arnold Milstein, Dan Jurafsky, Bruce Arnow, Stewart Agras, Fei-Fei Li, Nigam Shah
Dec 28, 2020
Research

Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring.

Assessing the accuracy of automatic speech recognition for psychotherapy

Adam Miner, Albert Haque, Jason Fries, Scott Fleming, Denise Wilfley, Terence Wilson, Arnold Milstein, Dan Jurafsky, Bruce Arnow, Stewart Agras, Fei-Fei Li, Nigam Shah
Dec 28, 2020

Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring.

Research