Abstract
The use of Artificial Intelligence (AI), Machine Learning (ML) and advanced analytics can yield important contributions to our understanding of how current systems and practices contribute to health disparities. They can also inform the development of equitable interventions, policies and decision-making in clinical care. This can only happen if we understand and address the biases in our healthcare system today, and how they are reflected in the data we use to develop and train AI systems. In this chapter, we will provide an overview of healthcare data sources and describe the ways in which the different types of data can be biased. We will discuss the impact of biased data, citing specific examples of how biased data has led to erroneous results or decisions, with particular focus on health equity and disparities. We will then describe strategies and techniques to both improve data prospectively, and to mitigate biases in how we use and interpret existing data to inform decision-making in healthcare.
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Koski, E., Saiz, F.S., Park, Y., South, B.R., Scheufele, E.L., Dankwa-Mullan, I. (2022). Addressing Health Equity: Sources, Impact and Mitigation of Biased Data. In: Kiel, J.M., Kim, G.R., Ball, M.J. (eds) Healthcare Information Management Systems. Health Informatics. Springer, Cham. https://doi.org/10.1007/978-3-031-07912-2_26
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