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Health Information Technologies (HIT) collect, process, store, and communicate health information in an electronic environment. HIT are touted as a cost-reducing, efficiency-increasing, and medical error-preventing tool. HIT can empower patients by giving them a channel to communicate with providers, access medical records, and access more credible health information. However, scholars and critics are increasingly concerned that HIT can worsen medical racism and structural inequalities. This study aims to identify instances of racial discrimination resulting from the use of HIT, broadly conceived. In this project, we look into racism in HIT, providing examples. For the poster, we focus on technologies (such as oximeters and machine learning algorithms) that perform differently on melanated skin. A look at the ISIC image database confirmed that skin color was not among the search criteria for images of skin lesions. We conclude with policy recommendations from the Algorithmic Justice League, an organization that addresses racial bias in AI.


This poster was presented at the 38th Semi-Annual Dr. Janet Liou-Mark Honors & Undergraduate Research Poster Presentation, May 4, 2023. Mentor: Prof. David Lee (Humanities).

Available for download on Friday, August 30, 2030