IEEE Technology and Society Magazine - March 2020 - 23

more recently, ML-driven approaches [8]. However, there are very high
stakes in the field, since technological interventions have the potential
to make a considerable, long-term
impact on human livelihoods, for
example lifting people out of poverty
by endowing them with linguistic and
numerical literacy. But these positive
impacts can be hindered by bias and
technological constraints. We therefore agree with recent proposals to
improve and support human learning at scale and believe that ML has
a key role to play in this endeavor.
This can be done, for instance, by
partnering with existing education
initiatives and organizations in order
to learn what their specific needs
are and how ML can be used to
meet them, or else by collaborating
with Massive Open Online Course
(MOOC) creators in order to gather
data and make it available to the
ML community, and finally by sharing learning materials and activities
used in local education initiatives
(e.g. university courses in Machine
Learning) so that they profit learners
in places where access to high-quality technical education is limited.

as solution to climate change, there
are nonetheless numerous technical challenges for which ML can be
helpful, and which can be combined
to make a significant impact on the
overall issue. These challenges and
the ongoing ML approaches to tackle
them were presented in a recent survey paper [34]. We will not go into all
of these at length, but we will focus
on a few examples that are particularly salient and that we hope will
give an idea of both the relevance of
deploying ML in environmental applications and the opportunities that
this can generate.

Energy and Transportation
Together, electricity and transportation systems are estimated to produce close to half of anthropogenic
greenhouse gas (GHG) emissions
[20] and both sectors have their own
unique challenges for decarbonization. For instance, one of the major
obstacles to building and using renewable energy sources such as solar and wind is the variability of their
output, which is inherently problematic since the power generated by an
energy grid must equal the power
used by its consumers at any given
moment. Currently, this means that
despite the existence of solar panels and wind turbines, these must
be complemented by controllable
but highly polluting energy sources
such as coal and natural gas plants.
ML methods that are appropriate for
time-series predictions, such as Recurrent Neural Networks are particularly suited for these types of tasks
[45] and can dramatically lower the
barrier to entry for renewable energy
globally. Furthermore, even in cases
where controllable energy sources
are used, demand on the energy grid
will still fluctuate based on usage; in
this case, ML techniques such as Reinforcement Learning and Dynamic
Scheduling can be used to balance
the grid in real time [43].

AI for the Environment
Climate change is, without a doubt,
one of the biggest challenges humanity has faced, and we are at an
important point in history when we
are both aware of the issue and still
have the possibility to change its
course. Climate change has been
described as a "wicked" problem,
due to features such as the difficulty
in defining the problem itself and in
developing and deploying solutions
to it, the lack of central authority
that can solve it, the incentives for
individual countries or companies
to not do their share, and the cognitive biases that discount the future
impacts of our actions [17], [25]. Furthermore, while we do not know of
any single technological silver bullet
MARCH 2020

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IEEE TECHNOLOGY AND SOCIETY MAGAZINE

In transportation, reducing activity is a key part of reducing GHG
emissions; however, given the highly
regional nature of transportation
methods (i.e., high-speed trains are
only an option in Europe, whereas
many major U.S. cities have limited
public transportation), custom solutions are needed to make a significant impact. ML can be of particular
help in estimating and predicting
vehicle flow to minimize it, for example by helping to optimize the
design of new roads and hubs [37]
and monitoring traffic [23], as well
as estimating carbon emissions in
real-time [29]. ML can also be used
for designing more energy-efficient
batteries [19], which will become an
increasingly important concern as
more people switch to electric vehicles. In cases of both energy and
transportation, ML can be used to
make systems more efficient and to
improve predictions of complex phenomena based on large amounts of
data; nonetheless, it remains only
one part of the solution, and as
tempting as it is to halt research
projects once a theoretically plausible solution has been found (and
a research paper published), what is
key here is working with domain experts to bring projects towards deployment, where concrete impacts
can be made. Transversal connections between disciplines are therefore key, and must be established
and fostered for projects to flourish.

Individuals and Societies
While changes in our climate can
be abstract, quantified in degrees
of warming or tons of CO2, climate
change will also have very concrete
impacts on society, for instance by
decreasing crop yields, increasing
the frequency of extreme weather
events such as hurricanes and
storms, and impacting biodiversity. There are a myriad of ways in
which ML can help face these issues,

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