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Author: Caleb J. Colon-Rodriguez
Publisher: US Department of Health and Human Services; Office of Minority Health
Year: 2023
Summary: This article discusses the potential causes and costs of algorithmic and AI bias in healthcare. It outlines why a model could become biased (lack of diversity in the data, assumptions by people creating models, etc.) but also outlines a case study where this happened in real life. This is a valuable resource for data professionals because it ties these ethics considerations to a real incident that affected thousands of women. Most importantly, it ends with a short list of suggestions that data professionals can implement to address the challenges of algorithmic bias.