English
Arabic
Chinese (Simplified)
Dutch
Esperanto
French
German
Greek
Italian
Japanese
Korean
Portuguese
Russian
Spanish
UrduWhile data on such “macro” factors is critical to tracking and predicting health outcomes for individuals and communities, analysts who apply machine-learning tools to health outcomes tend to rely on “micro” data constrained to the purely clinical setting, driven by healthcare data and processes inside the hospital, leaving factors that could shed light on healthcare disparities in the dark.
Researchers at the NYU Tandon School of Engineering and NYU School of Global Public Health in a new perspective, “Machine Learning in Public and Population Health,” in Nature Machine Intelligence, that aims to activate the machine learning community regarding to account for “macro” factors and their impact on health. Thinking outside that clinical box and beyond the strict limits of individual factors, Rumi Chunara, associate professor of computer science and engineering at NYU Tandon, and of biostatistics at the NYU School of Global Public Health (NYU GPH), found a new approach to incorporating the larger web of relevant data for predictive modeling for individual and community health outcomes.
“Research of what causes and reduces equity shows that to avoid creating more disparities it is essential to consider upstream factors as well,” explained Chunara, who is also appointed at the NYU School of Global Public Health. She notes that there is a large body of work on AI and machine learning implementation in healthcare in areas like image analysis, radiography, and pathology while strong awareness and advocacy focused on such areas as structural racism, police brutality, and healthcare disparities came to light around the COVID-19 pandemic.
“Our goal is to take that work and the explosion of machine learning in healthcare, which is data rich, and take it beyond the clinical setting, to communities and the environment.”
Chunara along with her doctoral students Vishwali Mhasawade and Yuan Zhao, at NYU Tandon and NYU GPH, respectively, leverage the Social Ecological Model, a framework for understanding how the health of an individual is affected by factors such as public policies at the national and international level; availability of health resources within a community and neighborhood that affect the habits and behavior of individuals themselves. The team shows how principles of this model can be used in algorithm development to show how algorithms can be designed and used more equitably.
The researchers organize existing work into a taxonomy of the types of tasks spanning prediction, interventions, identifying effects and allocations, machine learning and AI are used in, to show examples of how a multi-level perspective can be leveraged. In the Perspective piece, the authors also show how the same framework is applicable when thinking about data privacy and governance, and thinking about health more broadly than only within healthcare approach is important, and best practices to move the burden from individuals, which can improve equity.
As an example of such approaches, members of the same team recently presented at the AAAI/ACM Conference on Artificial Intelligence, Ethics and Society, a new approach to incorporating the larger web of relevant data for assessing fairness of algorithms called “Causal Multi-Level Fairness”. This work builds on the field of “algorithmic fairness,” which to-date are limited by their exclusive focus on individual-level attributes such as gender and race.
In this work Mhasawade and Chunara formalized a novel approach to understanding fairness relationships using tools from causal inference, synthesizing a means by which an investigator could assess and account for effects of sensitive macro attributes and not merely individual factors. They developed the algorithm for their approach, provided the settings under which it is applicable and illustrated it on data showing how predictions based merely on data points associated with labels like race, income and gender are of limited value if sensitive attributes are not accounted for, or are accounted for without proper context.
“As in healthcare, algorithmic fairness tends to be focused on labels — men and women, Black versus white, etc. — without considering multiple layers of influence from a causal perspective to decide what is fair and unfair in predictions,” she said. “Our work presents a framework for thinking not only about equity in algorithms but also what types of data we use in them.”
The study, “Machine Learning and Algorithmic Fairness in Public and Population Heath” is available at https://www.nature.com/
About the New York University Tandon School of Engineering
The NYU Tandon School of Engineering dates to 1854, the founding date for both the New York University School of Civil Engineering and Architecture and the Brooklyn Collegiate and Polytechnic Institute. A January 2014 merger created a comprehensive school of education and research in engineering and applied sciences as part of a global university, with close connections to engineering programs at NYU Abu Dhabi and NYU Shanghai. NYU Tandon is rooted in a vibrant tradition of entrepreneurship, intellectual curiosity, and innovative solutions to humanity’s most pressing global challenges. Research at Tandon focuses on vital intersections between communications/IT, cybersecurity, and data science/AI/robotics systems and tools and critical areas of society that they influence, including emerging media, health, sustainability, and urban living. We believe diversity is integral to excellence, and are creating a vibrant, inclusive, and equitable environment for all of our students, faculty and staff. For more information, visit engineering.nyu.edu.
###
