The risk of racial bias while tracking influenza-related content on social media using machine learning.

Journal: Journal of the American Medical Informatics Association : JAMIA

PMID: 33484133

Abstract

OBJECTIVE: Machine learning is used to understand and track influenza-related content on social media. Because these systems are used at scale, they have the potential to adversely impact the people they are built to help. In this study, we explore the biases of different machine learning methods for the specific task of detecting influenza-related content. We compare the performance of each model on tweets written in Standard American English (SAE) vs African American English (AAE).

Authors

Brandon Lwowski

Department of Information Systems and Cyber Security, University of Texas at San Antonio, San Antonio, Texas, USA.
Anthony Rios

Department of Computer Science, University of Kentucky, 329 Rose Street, Lexington, KY 40506, USA. Electronic address: anthony.rios1@uky.edu.

Keywords

Black or African American Datasets as Topic Humans Influenza Vaccines Influenza, Human Machine Learning Neural Networks, Computer Racism Social Media Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (33484133)

The risk of racial bias while tracking influenza-related content on social media using machine learning.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals