Race Against the Machine Learning Courses

Abstract

Despite the rapid integration of AI in healthcare, a critical gap exists in current machine learning courses: the lack of education on identifying and mitigating bias in datasets. This oversight risks perpetuating existing health disparities through biased AI models. Analyzing 11 prominent online courses, we found only 5 addressed dataset bias, often dedicating minimal time compared to technical aspects. This paper urges course developers to prioritize education on data context, equipping learners with the tools to critically evaluate the origin, collection methods, and potential biases inherent in the data. This approach fosters the creation of fair algorithms and the incorporation of diverse data sources, ultimately mitigating the harmful effects of bias in healthcare AI. While this analysis focused on publicly available courses, it underscores the urgency of addressing bias in all healthcare machine learning education. Early intervention in algorithm development is crucial to prevent the amplification of dataset and model bias, ensuring responsible and equitable AI implementation in healthcare.

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