Big data and artificial intelligence are increasingly shaping the healthcare sector, with applications ranging from outbreak detection to analyzing medical records for improved outcomes. However, these advancements also raise concerns about fairness, accuracy, and the changing role of humans in health systems.
A new course at New York University’s School of Global Public Health aims to address these challenges by preparing students for the evolving landscape. The course, titled “Data, AI and the People’s Health,” is designed and taught by Rumi Chunara, director of the NYU Center for Health Data Science. It is being offered for the first time this fall and brings together graduate students from several NYU schools.
“This topic is especially timely now because the amount of data that is informing AI and our health has grown dramatically,” says Chunara, an associate professor at both NYU School of Global Public Health and NYU Tandon School of Engineering. “We talk about data quality and how to improve it, what it means statistically, and cite research that people have done about real AI systems or products and the implications of those systems.”
In class sessions, students learn key concepts in statistics and computer science while working with health-related datasets. They also examine case studies related to algorithmic fairness—such as facial recognition technologies that initially failed to accurately identify people across different genders or skin tones.
Another case study discussed involves a health insurance company using a prediction algorithm to determine which patients should receive extra care management. The algorithm was found to prioritize cost over illness severity when making decisions.
“We walk through how the company designed it, what the issues were, and then how insurers have updated their processes,” Chunara explains. “All of our examples are based on real experiences of today’s AI deployments and give students a hands-on look as to challenges that they might encounter in the workplace, or ideas they can bring to their employer. The good thing is that since this course captures recent happenings, we get to discuss how these issues get fixed, and we relate that to the underlying mechanics.”
Chunara emphasizes that students develop a set of skills combining statistical analysis, computer science knowledge, and public health principles—what she describes as a critical “toolbox.” This toolbox enables them not only to build new technologies but also evaluate existing ones for potential problems before they arise.
“My hope is that this course makes students more critical thinkers—not just with what they’re building, but what they encounter. And not just critical thinkers, but critical solution makers,” says Chunara.
