Abstract

As artificial intelligence (AI) continues to rapidly expand into existing healthcare infrastructure – e.g., clinical decision support, administrative tasks, and public health surveillance – it is perhaps more important than ever to reflect on the broader purpose of such systems. While much focus has been on the potential for this technology to improve general health outcomes, there also exists a significant, but understated, opportunity to use this technology to address health-related disparities. Accomplishing the latter depends not only on our ability to effectively identify addressable areas of systemic inequality and translate them into tasks that are machine learnable, but also our ability to measure, interpret, and counteract barriers in training data that may inhibit robustness to distribution shift upon deployment (i.e., new populations, temporal dynamics). In this talk, we will discuss progress made along both of these dimensions. We will begin by providing background on the state of AI for promoting health equity. Then, we will present results from a recent clinical phenotyping project and discuss their implication on prevailing views regarding language model robustness in clinical applications. Finally, we will showcase ongoing efforts to proactively address systemic inequality in healthcare by identifying and characterizing stigmatizing language in medical records.