IEEE Technology and Society Magazine - March 2021 - 72

Opinion

they end up further worsening the situation [4].
To help address these issues, the United Nations
Development Programme (UNDP) has proposed a
comprehensive framework for providing socioeconomic support to countries and societies in the
face of COVID-19 which covers five complementary
dimensions, including: 1) essential health services;
2) social protection; 3) microeconomic response
and recovery programs; 4) macroeconomic fiscal
and financial stimuli and policies; and 5) community-led resilience and response systems, while ensuring social equality and inclusion [5]. They stress that
all of these dimensions must be taken into account
when designing and deploying interventions to
ensure that all necessary aspects of human and societal well-being are addressed.

AI, COVID-19, and inequality
In a recent article reviewing AI solutions against
COVID-19 [6], we identified three main scales of
AI applications: the molecular scale, the clinical
scale, and the societal scale. We also highlighted the
importance of assessing the maturity and feasibility
of proposed interventions at each of these scales
before deploying AI solutions. At the molecular
level, much of the current research involves assisting drug discovery and development, and improving molecular diagnosis. Many of these applications
remain at the research stage, given the challenges in
synthesizing and testing compounds, as well as limitations in data and model sharing. Nonetheless, at
least four candidate vaccines that reported the use
of machine learning (ML) in their development have
advanced to the clinical evaluation stage according
to the World Health Organization (WHO) [7]. At a
clinical level, AI has mostly been used to assist and
improve patient-level assessment of COVID-19 via
analysis of medical imagery and patient records.
However, the extent to which the analysis from these
techniques alone can be used for the diagnosis of
COVID-19 is still debated by the medical community [8], and transparency and explainability of the
proposed diagnoses remain overlooked by most AI
approaches. Finally, on a societal scale, two crucial
and complementary lines of research focus on modeling the spread of the pandemic across territories
and regions, as well as its accompanying " infodemic "
of misinformation. Despite reservations regarding
whether models trained on data from one context
are applicable in another one, data-driven models

remain paramount in analyzing the spread of the
virus. Overall, while AI can be an important tool in
fighting the pandemic, context-sensitive deployment
is key. Working with stakeholders who have necessary domain knowledge, in addition to developing
appropriate model and data sharing solutions, is
critical [9], [10].
Will the AI systems applied in the fight against
COVID-19 increase or decrease inequality? While the
AI community is still in the early stages of application
development, we have identified some persistent
sources of bias which run the risk of exacerbating
inequality [9]. We believe, however, that some of
these biases can be mitigated (and potentially overcome) if proper assessment is included during the
application development process. Generally, AI
applications against COVID-19 have been developed
in and for countries in the Global North, resulting in
a lack of information about how the resulting tools
will impact the evolution of the disease and related
policies in the Global South [11]. Even within highly
developed countries, many modeling and data collection efforts overlook or neglect underrepresented
minorities [12]-[14].
AI systems can be inherently prone to bias which
can be introduced at several different points in the
application development pipeline. First, bias can be
present during problem scoping, that is, by influencing the way in which the problem to be addressed
by AI is framed, as well as the extent of the work
itself. Second, bias can be present in the data used to
train an AI system, or the way in which the data was
labeled. Especially in healthcare situations, using
AI approaches can reproduce and amplify existing
biases in medical data sets, oftentimes not accounting for data from minority groups [15], [16]. Third,
bias can be present in the choice of the algorithms
themselves and how its intricate configuration
parameters are tuned. Finally, bias may influence
how results are evaluated and interpreted, which in
turn affects how AI outputs are used. A further challenge arises when models trained and tested in one
setting are then applied to another, and results are
interpreted without acknowledging any necessary
corrections specific to the new context. While this is
not an exhaustive list of all potential sources of bias,
these steps of an AI pipeline are all subject to both
conscious and unconscious bias, which can reinforce existing inequalities as well as promote new
ones if not properly addressed.

IEEE Technology and Society Magazine

IEEE Technology and Society Magazine - March 2021

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