New research infuses equity principles into the algorithm development
process

Date:
July 30, 2021
Source:
NYU Tandon School of Engineering
Summary:
Researchers have found a new approach to incorporating the larger
web of relevant data for predictive modeling for individual and
community health outcomes.



FULL STORY ==========================================================================
In the U.S., the place where one was born, one's social and economic background, the neighborhoods in which one spends one's formative years,
and where one grows old are factors that account for a quarter to 60% of
deaths in any given year, partly because these forces play a significant
role in occurrence and outcomes for heart disease, cancer, unintentional injuries, chronic lower respiratory diseases, and cerebrovascular diseases
-- the five leading causes of death.


========================================================================== While 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 purely clinical settings and 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 (NYU GPH), in a new perspective, "Machine learning
and algorithmic fairness in public and population health," in Nature
Machine Intelligence, aim to activate the machine learning community to
account for "macro" factors and their impact on health. Thinking outside
the 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 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. She noted, on the one hand, the large body of work
on AI and machine learning implementation in healthcare in areas like
image analysis, radiography, and pathology, and on the other the strong awareness and advocacy focused on such areas as structural racism,
police brutality, and healthcare disparities that came to light around
the COVID-19 pandemic.

"Our goal is to take that work and the explosion of data-rich machine
learning in healthcare, and create a holistic view beyond the clinical
setting, incorporating data about communities and the environment."
Chunara, along with her doctoral students Vishwali Mhasawade and Yuan
Zhao, at NYU Tandon and NYU GPH, respectively, leveraged the Social
Ecological Model, a framework for understanding how the health, habits
and behavior of an individual are affected by factors such as public
policies at the national and international level and availability of
health resources within a community and neighborhood. 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 organized existing work into a taxonomy of the types of
tasks for which machine learning and AI are used that span prediction, interventions, identifying effects and allocations, to show examples
of how a multi-level perspective can be leveraged. In the piece, the
authors also show how the same framework is applicable to considerations
of data privacy, governance, and best practices to move the healthcare
burden from individuals, toward improving 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 using "causal multi-level fairness," the
larger web of relevant data for assessing fairness of algorithms. This
work builds on the field of "algorithmic fairness," which, to date,
is limited by its 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 and provided
the settings under which it is applicable. They also illustrated their
method 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,"
said Chunara. "Our work presents a framework for thinking not only
about equity in algorithms but also what types of data we use in them." ========================================================================== Story Source: Materials provided by
NYU_Tandon_School_of_Engineering. Note: Content may be edited for style
and length.


========================================================================== Journal Reference:
1. Vishwali Mhasawade, Yuan Zhao, Rumi Chunara. Machine learning and
algorithmic fairness in public and population health. Nature
Machine Intelligence, 2021; DOI: 10.1038/s42256-021-00373-4 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/07/210730104308.htm

--- up 12 weeks, 22 hours, 45 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)