Abstract
This chapter will focus on some of the technical aspects of addressing health equity associated with using Artificial Intelligence (AI) and Machine-Learning (ML) in healthcare systems and applications. We will examine this issue from both technical and sociological perspectives, describing the impact of algorithmic bias through specific examples of AI algorithms that have been demonstrated to produce biased outcomes in healthcare, and in other arenas that have important implications for healthcare. We will review a variety of analytic methodologies that can be used to address sources of algorithmic bias, including an assessment of the considerations for selecting the best method in different analytic contexts. We will conclude with an examination into the broader societal impact of algorithmic bias and enumerate on the environmental scan we conducted of organizations actively involved in addressing algorithmic bias and AI justice. We will describe some of the projects and efforts that are particularly relevant in the context of addressing health disparities.
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Acknowledgements
The authors would like to thank our colleagues Irene Dankwa-Mullan and Fernando Suarez Saiz at IBM Watson Health for their contributions to our efforts planning and preparing this chapter.
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Park, Y., Singh, M., Koski, E., Sow, D.M., Scheufele, E.L., Bright, T.J. (2022). Algorithmic Fairness and AI Justice in Addressing Health Equity. 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_15
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