3 Questions: Artificial intelligence for health care equity

"...Unfortunately, however, bias not only affects how individuals perceive the world around them, it also influences the datasets we use to build models. Observational datasets that store patient features and outcomes often reflect the underlying bias of health care providers; e.g., certain treatments may be preferentially offered to those who have high socioeconomic status. In short, algorithms can inherit our own biases. Making personalized medicine a reality is therefore predicated on our ability to develop and deploy unbiased tools that learn the patient-specific decisions from observational clinical data. Central to the success of this endeavor is the development of methods that can identify algorithmic bias and suggest mitigation strategies when bias is identified." (MIT News, 23 March 2021)

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