Zobrazeno 1 - 10
of 193
pro vyhledávání: '"PFOHL, STEPHEN"'
Autor:
Asiedu, Mercy Nyamewaa, Haykel, Iskandar, Dieng, Awa, Kauer, Kerrie, Ahmed, Tousif, Ofori, Florence, Chan, Charisma, Pfohl, Stephen, Rostamzadeh, Negar, Heller, Katherine
Artificial Intelligence (AI) for health has the potential to significantly change and improve healthcare. However in most African countries, identifying culturally and contextually attuned approaches for deploying these solutions is not well understo
Externí odkaz:
http://arxiv.org/abs/2409.12197
Autor:
Pfohl, Stephen R., Cole-Lewis, Heather, Sayres, Rory, Neal, Darlene, Asiedu, Mercy, Dieng, Awa, Tomasev, Nenad, Rashid, Qazi Mamunur, Azizi, Shekoofeh, Rostamzadeh, Negar, McCoy, Liam G., Celi, Leo Anthony, Liu, Yun, Schaekermann, Mike, Walton, Alanna, Parrish, Alicia, Nagpal, Chirag, Singh, Preeti, Dewitt, Akeiylah, Mansfield, Philip, Prakash, Sushant, Heller, Katherine, Karthikesalingam, Alan, Semturs, Christopher, Barral, Joelle, Corrado, Greg, Matias, Yossi, Smith-Loud, Jamila, Horn, Ivor, Singhal, Karan
Publikováno v:
Nature Medicine (2024)
Large language models (LLMs) hold promise to serve complex health information needs but also have the potential to introduce harm and exacerbate health disparities. Reliably evaluating equity-related model failures is a critical step toward developin
Externí odkaz:
http://arxiv.org/abs/2403.12025
Autor:
Tsai, Katherine, Pfohl, Stephen R., Salaudeen, Olawale, Chiou, Nicole, Kusner, Matt J., D'Amour, Alexander, Koyejo, Sanmi, Gretton, Arthur
We study the problem of domain adaptation under distribution shift, where the shift is due to a change in the distribution of an unobserved, latent variable that confounds both the covariates and the labels. In this setting, neither the covariate shi
Externí odkaz:
http://arxiv.org/abs/2403.07442
Autor:
Asiedu, Mercy, Dieng, Awa, Haykel, Iskandar, Rostamzadeh, Negar, Pfohl, Stephen, Nagpal, Chirag, Nagawa, Maria, Oppong, Abigail, Koyejo, Sanmi, Heller, Katherine
With growing application of machine learning (ML) technologies in healthcare, there have been calls for developing techniques to understand and mitigate biases these systems may exhibit. Fair-ness considerations in the development of ML-based solutio
Externí odkaz:
http://arxiv.org/abs/2403.03357
Autor:
Eisenstein, Jacob, Nagpal, Chirag, Agarwal, Alekh, Beirami, Ahmad, D'Amour, Alex, Dvijotham, DJ, Fisch, Adam, Heller, Katherine, Pfohl, Stephen, Ramachandran, Deepak, Shaw, Peter, Berant, Jonathan
Reward models play a key role in aligning language model applications towards human preferences. However, this setup creates an incentive for the language model to exploit errors in the reward model to achieve high estimated reward, a phenomenon ofte
Externí odkaz:
http://arxiv.org/abs/2312.09244
Autor:
Singhal, Karan, Tu, Tao, Gottweis, Juraj, Sayres, Rory, Wulczyn, Ellery, Hou, Le, Clark, Kevin, Pfohl, Stephen, Cole-Lewis, Heather, Neal, Darlene, Schaekermann, Mike, Wang, Amy, Amin, Mohamed, Lachgar, Sami, Mansfield, Philip, Prakash, Sushant, Green, Bradley, Dominowska, Ewa, Arcas, Blaise Aguera y, Tomasev, Nenad, Liu, Yun, Wong, Renee, Semturs, Christopher, Mahdavi, S. Sara, Barral, Joelle, Webster, Dale, Corrado, Greg S., Matias, Yossi, Azizi, Shekoofeh, Karthikesalingam, Alan, Natarajan, Vivek
Recent artificial intelligence (AI) systems have reached milestones in "grand challenges" ranging from Go to protein-folding. The capability to retrieve medical knowledge, reason over it, and answer medical questions comparably to physicians has long
Externí odkaz:
http://arxiv.org/abs/2305.09617
Autor:
Singhal, Karan, Azizi, Shekoofeh, Tu, Tao, Mahdavi, S. Sara, Wei, Jason, Chung, Hyung Won, Scales, Nathan, Tanwani, Ajay, Cole-Lewis, Heather, Pfohl, Stephen, Payne, Perry, Seneviratne, Martin, Gamble, Paul, Kelly, Chris, Scharli, Nathaneal, Chowdhery, Aakanksha, Mansfield, Philip, Arcas, Blaise Aguera y, Webster, Dale, Corrado, Greg S., Matias, Yossi, Chou, Katherine, Gottweis, Juraj, Tomasev, Nenad, Liu, Yun, Rajkomar, Alvin, Barral, Joelle, Semturs, Christopher, Karthikesalingam, Alan, Natarajan, Vivek
Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically
Externí odkaz:
http://arxiv.org/abs/2212.13138
Autor:
Alabdulmohsin, Ibrahim, Chiou, Nicole, D'Amour, Alexander, Gretton, Arthur, Koyejo, Sanmi, Kusner, Matt J., Pfohl, Stephen R., Salaudeen, Olawale, Schrouff, Jessica, Tsai, Katherine
We address the problem of unsupervised domain adaptation when the source domain differs from the target domain because of a shift in the distribution of a latent subgroup. When this subgroup confounds all observed data, neither covariate shift nor la
Externí odkaz:
http://arxiv.org/abs/2212.11254
Autor:
Zhang, Haoran, Dullerud, Natalie, Roth, Karsten, Oakden-Rayner, Lauren, Pfohl, Stephen Robert, Ghassemi, Marzyeh
Deep learning models have reached or surpassed human-level performance in the field of medical imaging, especially in disease diagnosis using chest x-rays. However, prior work has found that such classifiers can exhibit biases in the form of gaps in
Externí odkaz:
http://arxiv.org/abs/2203.12609
Autor:
Pfohl, Stephen R., Xu, Yizhe, Foryciarz, Agata, Ignatiadis, Nikolaos, Genkins, Julian, Shah, Nigam H.
A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of health inequities that may follow from the use of model-guided decision
Externí odkaz:
http://arxiv.org/abs/2202.01906