What Are the Sources of Algorithmic Bias in Cancer?
Several factors contribute to algorithmic bias in cancer, including biased training data, flawed feature selection, and lack of diversity in the research team. Biased training data can result from historical inequities in healthcare, where certain groups have less access to medical services, thus being underrepresented in the data. Flawed feature selection may involve variables that are not relevant to all groups, and a lack of diverse perspectives can lead to blind spots in the design and implementation of algorithms.
