A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models

1 code implementation • 18 Mar 2024 • Stephen R. Pfohl, Heather Cole-Lewis, Rory Sayres, Darlene Neal, Mercy Asiedu, Awa Dieng, Nenad Tomasev, Qazi Mamunur Rashid, Shekoofeh Azizi, Negar Rostamzadeh, Liam G. McCoy, Leo Anthony Celi, Yun Liu, Mike Schaekermann, Alanna Walton, Alicia Parrish, Chirag Nagpal, Preeti Singh, Akeiylah Dewitt, Philip Mansfield, Sushant Prakash, Katherine Heller, Alan Karthikesalingam, Christopher Semturs, Joelle Barral, Greg Corrado, Yossi Matias, Jamila Smith-Loud, Ivor Horn, Karan Singhal

Our contributions include a multifactorial framework for human assessment of LLM-generated answers for biases, and EquityMedQA, a collection of seven datasets enriched for adversarial queries.

Towards Expert-Level Medical Question Answering with Large Language Models

1 code implementation • 16 May 2023 • Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Le Hou, Kevin Clark, Stephen Pfohl, Heather Cole-Lewis, Darlene Neal, Mike Schaekermann, Amy Wang, Mohamed Amin, Sami Lachgar, Philip Mansfield, Sushant Prakash, Bradley Green, Ewa Dominowska, Blaise Aguera y Arcas, Nenad Tomasev, Yun Liu, Renee Wong, Christopher Semturs, S. Sara Mahdavi, Joelle Barral, Dale Webster, Greg S. Corrado, Yossi Matias, Shekoofeh Azizi, Alan Karthikesalingam, Vivek Natarajan

Large language models (LLMs) have catalyzed significant progress in medical question answering; Med-PaLM was the first model to exceed a "passing" score in US Medical Licensing Examination (USMLE) style questions with a score of 67. 2% on the MedQA dataset.

MedQA MMLU +2

The Equitable AI Research Roundtable (EARR): Towards Community-Based Decision Making in Responsible AI Development

no code implementations • 14 Mar 2023 • Jamila Smith-Loud, Andrew Smart, Darlene Neal, Amber Ebinama, Eric Corbett, Paul Nicholas, Qazi Rashid, Anne Peckham, Sarah Murphy-Gray, Nicole Morris, Elisha Smith Arrillaga, Nicole-Marie Cotton, Emnet Almedom, Olivia Araiza, Eliza McCullough, Abbie Langston, Christopher Nellum

This paper reports on our initial evaluation of The Equitable AI Research Roundtable -- a coalition of experts in law, education, community engagement, social justice, and technology.

Decision Making