IEEE Technology and Society Magazine - March 2021 - 75

may have certain smartphone and operating system
requirements.
We have recently seen a rise in the use of ML for
modeling the epidemiological trends of COVID-19
[41], [42], which might be heavily influenced by
data limitations and biases [6], [43]. Indeed, while
many classical modeling approaches can be interrogated mathematically and computationally, we
are still much less able to interpret the reasoning
behind the outputs of an AI system. Therefore,
while AI models may be powerful as prediction
tools, we need the ability to better explore models
which have the potential to significantly impact
people's lives.

Infodemics
The propagation of mis- and disinformation
around COVID-19 may act as yet another driver of
inequality, and online social media platforms represent a particularly rich environment for the spread of
infodemics. Research has shown that information is
disseminated on these platforms in a viral manner,
reproducing at a rate similar to that of a pandemic
(i.e., exponentially) [44]. Although the rate at which
true versus false claims are amplified appears to vary
across platforms [44], research on Twitter has found
that social media users may be more likely to share
false information because it is novel [45]. The reach
of misinformation may be further extended by the
automated recommendation algorithms underlying
many online social media platforms, which seek to
identify and promote popular or viral posts to maximize engagement and garner attention [46]. Despite
the widespread proliferation of misinformation, only
a fraction of this content is flagged and ultimately
removed or corrected [47].
Some of the most vulnerable populations in our
society-such as the elderly, minorities, and other
populations with low health literacy-may also be
the most susceptible to the infodemic [48]. On the
one hand, since much of the false information about
COVID-19 involves fake cures, rumors about invincibility, and/or ineffective preventive measures,
misinformation could lead these already-vulnerable populations to engage in unnecessarily risky
behavior. On the other hand, these populations
may be more likely to believe misinformation due
to a tendency toward " inequality-driven mistrust " in
which a historical legacy of discrimination and mistreatment at the hands of society and the medical

March 2021

community makes them more likely to question
official information and believe in conspiracy theories [49]. In sub-Saharan Africa, researchers have
argued that this skepticism extends to " distrust of
philanthropic institutions, distrust of developed
nations, and even distrust of leaders in their own
respective countries " [50].
Systematic fact-checking efforts to proactively
identify and correct false information may fail to
reach these vulnerable populations because these
populations may engage with misinformation
through private or interpersonal channels [51], and
in local languages or dialects [52]. This makes it
difficult for fact checkers to identify the relevant
pieces of false information and to reach these populations with corrected information. Many efforts to
combat the infodemic so far have focused on data
sources that are easy to mine with AI (e.g., Facebook, Twitter, and online news media), rather than
on offline channels such as radio or TV which may
act as primary sources of information in the developing world [53]. These efforts also tend to focus
on widely spoken languages such as English, Spanish, Arabic, French, and Portuguese, rather than on
under-represented regional languages for which
natural language processing tools may not yet be
available.
Another repercussion of the infodemic has been
the targeting and stigmatization of vulnerable groups.
For example, there has been a reported rise in hate
crimes [54] and online hate speech [55] against
Chinese and other Asian minorities. There has also
been a rise in xenophobic and antimigrant sentiment more generally [56], both because there is a
fear that arriving migrants will transmit the virus, and
because of the perceived strain that these migrants
will place on already-overburdened.

Discussion
AI applications have the potential to positively
contribute to the fight against COVID-19. However,
AI can also amplify the biases and inequalities that
have emerged or become more extreme during the
pandemic. As in any other application of AI, bias
can be introduced at multiple points throughout the
modeling pipeline. As COVID-19 furthers inequality
along every dimension of society, it is important to
identify and address AI applications which might
contribute to this trend.

IEEE Technology and Society Magazine - March 2021

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