Data is often considered an objective source of truth, but health care data reflects underlying health disparities. Populations with socioeconomic barriers to accessing care are underrepresented or misrepresented in our systems.
Advanced analytics and artificial intelligence (AI) can turn complex data into actionable insights, but we must first understand the shortcomings of data and the social implications of deploying models trained on biased data. Marginalized populations that are left out of data will not reap the benefits of AI. In this cheat sheet, we discuss the ways data reflects health disparities and how to address the root causes.
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Addressing Discrimination in Artificial Intelligence