IEEE Technology and Society Magazine - March 2021 - 74

Opinion

The inequitable selection of participants as well
as inconsistent presentation of demographic data
has been a prevalent issue in pharmaceutical trials long before COVID-19 [31]. Prior research has
shown that demographic differences often translate
into differences in physical outcomes for individuals
and communities. For example, immune profiles differ from person to person as a result of genetic, evolutionary, and environmental factors such as age,
ethnicity, comorbidities, geographic location, nutrition, and so on [32], [33]. Hence, these dimensions
should be properly accounted for when designing
ML-based methodologies. We must be particularly
cautious to ensure that data limitations do not lead
us to develop biased algorithms that use readily
available and seemingly effective but in fact, problematic proxies, such as using healthcare costs as a
proxy to assess health needs, which was found to be
racially biased [34]. These kinds of AI applications
risk not only making inaccurate predictions, but also
reinforcing systemic injustices.
Although the private sector is playing a critical role
in devising interventions against COVID-19, its lack
of transparency may reinforce biases and inequalities. Most of the vaccine candidates that utilized ML
in their development came from corporations that
made little information on their ML approach available, making it difficult for independent researchers to inspect the biases that might come from the
collection and measurement of data as well as the
evaluation, aggregation, and deployment processes
[35]. Research conducted in private sector largely
relies on data such as knowledge graphs which are
not publicly accessible, and proprietary algorithms
hinder the discovery, evaluation, and rectification of
algorithmic biases [34].
All in all, given that AI is playing an increasingly
important role in expediting the discovery and development of medical solutions, it is particularly important to ensure that the data gathered is representative
of global populations and that appropriate mechanisms to audit algorithms and dissect any possible
biases are in place. Otherwise, solutions developed
and tested in ways that are subject to systemic, clinical, and algorithmic biases may have unintended
consequences in vulnerable populations.

Epidemics
The modeling of epidemics is relevant for understanding potential infection trajectories and to

inform operational planning. Epidemiological models need careful fine-tuning to regional variables to
capture age, socioeconomic status, and cultural
norms. One of the most frequently used approaches
is susceptible-infected-recovered (SIR) modeling,
in which models are usually designed based on
regional statistics capturing age, sex, and potentially other variables if data are available. However,
these approaches often average over demographic
and societal structures, meaning that differences in
behavior between different groups are missed. Furthermore, developing epidemiological models in
certain settings, and applying them to others with
different cultural norms, can result in incorrect predictions. In fact, we have already seen examples of
models failing to capture and explain differences in
COVID-19 transmission trends between European
and African contexts [36], [37]. If models are not
developed in collaboration with regional experts,
local populations are at a severe risk of being
left behind.
Disparities in modeling efforts have also grown
out of differences in data availability. For example, agent-based approaches to modeling disease
spread, while able to better account for heterogeneous transmission dynamics, often require detailed
data inputs. These can be challenging to acquire
in under-served and ill-documented settings such
as informal settlements. Similarly, many modeling
approaches are designed to assess the potential
for certain policy interventions, yet many of these
interventions, such as social distancing or household quarantine, are impossible in highly crowded
spaces where individuals need to leave their homes
for water or aid collection.
In addition, many data sets which have served
as important indicators for possible disease spread
and recovery, as well as tools to aid in SARS-CoV-2
transmission mitigation efforts, are reliant on the
-
access to certain technology. For example, Google
mobility data [38] has provided valuable insights
into the movement patterns of individuals and has
been used in several modeling efforts [39], [40].
However, this fails to capture portions of society
which do not have access to the mobile technology
used in creating such data sets. Newly developed
tools to fight the pandemic can also be reliant on
technology which is unavailable to certain members
of society such as contact tracing applications which

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IEEE Technology and Society Magazine - March 2021

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