Write It Like You See It: Detectable Differences in Clinical Notes by Race Lead to Differential Model Recommendations

Author:

Adam Hammaad1,Yang Ming Ying1,Cato Kenrick2,Baldini Ioana3,Senteio Charles4,Celi Leo Anthony5,Zeng Jiaming6,Singh Moninder3,Ghassemi Marzyeh7

Affiliation:

1. Massachusetts Institute of Technology, Cambridge, MA, USA

2. Columbia University, New York, NY, USA

3. IBM Research, Yorktown Heights, NY, USA

4. Rutgers University, New Brunswick, NJ, USA

5. Massachusetts Institute of Technology & Beth Israel Deaconess Medical Center, Cambridge, MA, USA

6. IBM Research, Cambridge, MA, USA

7. Massachusetts Institute of Technology & Vector Institute, Cambridge, MA, USA

Funder

Helmholtz Professorship

CIFAR Azreili Global Scholars

National Institute of Health

MIT Jameel Clinic

National Institute of Nursing Leadership

MIT-IBM Watson AI Lab

Publisher

ACM

Reference64 articles.

1. Julius A Adebayo . 2016. FairML : ToolBox for diagnosing bias in predictive modeling. Ph. D. Dissertation . Massachusetts Institute of Technology . Julius A Adebayo. 2016. FairML : ToolBox for diagnosing bias in predictive modeling. Ph. D. Dissertation. Massachusetts Institute of Technology.

2. Skin color in dermatology textbooks: An updated evaluation and analysis

3. Black box fairness testing of machine learning models

4. Hospitalization for Congestive Heart Failure

5. Emily Alsentzer , John R Murphy , Willie Boag , Wei-Hung Weng , Di Jin , Tristan Naumann , and Matthew B A McDermott . 2019. Publicly Available Clinical BERT Embeddings. (April 2019 ). arXiv:1904.03323 [cs.CL] Emily Alsentzer, John R Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew B A McDermott. 2019. Publicly Available Clinical BERT Embeddings. (April 2019). arXiv:1904.03323 [cs.CL]

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