IEEE Technology and Society Magazine - March 2020 - 22

different kinds of actors can connect
and collaborate on AISG projects,
e.g., ML graduate students or engineers, problem owners in NGOs or
local governments, philanthropy organizations, or startups that could
deploy the ML solutions. Their interaction is to be facilitated by online
tools and datasets as well as a standardized description of the status,
progress, and expected impact of

We believe we have a shared
responsibility to consider
whether the products we
contribute to will be beneficial
to humanity.
each project. We hope that initiatives
like this will help solidify and amplify
the impact of AISG; in the meantime,
there are also many profoundly positive uses of AI that are emerging and
we would like to highlight and applaud such efforts next.

AI in Healthcare
Achieving universal health coverage is
one of the seventeen UN Sustainable
Development Goals [42], and although
major progress has been made in numerous domains, such as maternal
health as well as HIV/AIDS reduction,
there are still many problems that are
far from being solved. While ML is not
a cure-all, there are many challenges
that it can help with such as personalized medicine, diagnosis of medical
imagery, and improved drug discovery [13]. ML in the health sector is in
fact a thriving research domain, with
its own workshops at major ML conferences and research published in
major medical journals read by practitioners worldwide.

22

and ethical usage of ML in healthcare
[48] and to ensure that Hippocratic
principles are a solid part of the research and development process,
as well as working with stakeholders of the domain (e.g., radiologists,
clinicians, patient organizations,
and hospital administrators) to propose solutions to the hurdles discussed above.

In the last five years alone, groundbreaking work has been done in
improving the diagnosis of diabetic
retinopathy from a single visit [3],
detecting breast cancer in lymph
nodes [14], and large-scale discovery of diseases based on health
records [32]. There are also an increasing number of startups and
companies working in the space,
either by commercializing research
done in academia or by developing products specifically catered to the medical
sector, with the most advanced applications harnessing the power of deep
learning for analyzing and
classifying medical imagery.
Despite the many exciting advances that are being made, there are many
hurdles in ML research in
healthcare, starting with data
privacy and control (who
owns the data? Can patients share
their own data, or should the process be centralized? How to find the
right balance between privacy and
the lives which will be saved by applying ML on the aggregated health
records from many different sources?), to the manner in which medical
data should be processed (Should
it contain information such as race
and postal/zip code, which can impact diagnoses, be included in electronic heath records, or does that
open the door to discrimination and
bias?), and how should such systems
be deployed (human-in-the-loop or
fully automated)?3 There are also often questions of responsibility and
interpretability that arise, given the
high stakes of deploying ML systems
in situations of life and death. In order to make meaningful progress in
this sector, it is therefore important
to continue existing research on fair

AI for Education
The promise of using adaptive intelligent systems and agents for education has been around since the
1960s [39], but access to personalized digital education tools has yet
to become a reality in most countries, especially in the developing
world, where it could have the most
impact to democratize education
and knowledge [30]. In recent years,
given the increasing global shortage
of qualified teachers along with the
increasing number of students, the
issue of access to education has become a global one, a fact highlighted
by its presence among the UN SDGs.
And yet, the use of ML in the education sector has been limited to specific, narrow applications such as
predicting the probability of learner
attrition [7], or improving learner
evaluation [1]. There are many reasons for this, starting from the difficulty of representing learning
content in a domain-agnostic way
to facilitate scalability, to overcoming cultural and linguistic barriers
to deploying tutors worldwide. But
the limited use of ML in education
is also caused by more fundamental
issues such as the lack of large-scale
educational datasets and inherent
technological constraints in developing countries.
Despite these hurdles, there are
many new and longstanding efforts
to create intelligent tutors, be it using symbolic AI approaches such as
ontologies and knowledge modeling
[28], educational data mining [12], or

3

For a more extensive overview of the opportunities and challenges of using ML in healthcare,
see [13].

IEEE TECHNOLOGY AND SOCIETY MAGAZINE

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MARCH 2020
IEEE Technology and Society Magazine - March 2020

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