OBJECTIVES: To understand whether and how equity is considered in artificial intelligence/machine learning governance processes at academic medical centers.
There is growing attention and evidence that healthcare AI is vulnerable to racial bias. Despite the renewed attention to racism in the United States, racism is often disconnected from the literature on ethical AI. Addressing racism as an ethical iss...
OBJECTIVE: Existing approaches to fairness evaluation often overlook systematic differences in the social determinants of health, like demographics and socioeconomics, among comparison groups, potentially leading to inaccurate or even contradictory c...
IMPORTANCE: Machine learning has potential to transform cancer care by helping clinicians prioritize patients for serious illness conversations. However, models need to be evaluated for unequal performance across racial groups (ie, racial bias) so th...
Hundreds of millions of people now interact with language models, with uses ranging from help with writing to informing hiring decisions. However, these language models are known to perpetuate systematic racial prejudices, making their judgements bia...
Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. At an international conference in 2022, experts gathered to discuss strategies for reducing bias in he...
This scoping review examined racial and ethnic bias in artificial intelligence health algorithms (AIHA), the role of stakeholders in oversight, and the consequences of AIHA for health equity. Using the PRISMA-ScR guidelines, databases were searched b...
Text-to-image generative AI models such as Stable Diffusion are used daily by millions worldwide. However, the extent to which these models exhibit racial and gender stereotypes is not yet fully understood. Here, we document significant biases in Sta...