IEEE Technology and Society Magazine - June 2021 - 89

typically involve three stages - planning, execution,
and monitoring of ecosystem restoration. Several AI
technologies have been used in afforestation efforts
and their use is increasing. During planning, AI systems
have been used to predict forest carbon sequestration
potential through the use of satellite and drone image
data [62], [63]. AI can also facilitate execution of
afforestation through computer vision algorithms used
in identifying appropriate planting sites, monitoring
plant health, and analyzing trends [64]. Lastly, in the
monitoring stage of restoration projects, AI can be used
to identify where deforestation may have been conducted
illegally [65], [66], as well as assess risks due to
fire, disease, insects, or other causes [67].
Current challenges
According to international governance efforts
like the UN SDGs, the UN Forum on Forests, Agenda
21, and the Future We Want (the outcome document
of the Rio+20 Conference) there is a need for holistic,
multi-stakeholder engagement to address forest
ecosystem restoration adequately [68], [69]. This is
due to the existence of multiple groups with critical
interests in forest ecosystems. For example, local
communities and businesses may engage in harvesting
timber and farming as part of the local economy,
while natives living off the land may depend on
hunting animals and harvesting plants, and policymakers
must also worry about carbon sequestration
and climate change efforts.
Though the goals of these groups are not always
in conflict, they can bring different perspectives
and have competing priorities. Therefore, AI-driven
systems used for afforestation that do not take into
account these " multiple ecological, economic,
social and cultural roles " important to various
stakeholders [58] may lead to blind spots and unintended
harms. For example, an AI system that uses
imaging data to determine carbon sequestration
potential could optimize climate change goals in
a narrow sense, but fail to account for social-ecological
aspects of the land important to indigenous
groups, or ignore endangered species important to
conservationists.
As a result, carbon sequestration targets optimized
in the short term could fall short in the long
term as afforestation progress fails to translate into
a sustainably managed multi-stakeholder effort. Failing
to develop and implement AI systems for ecosystem
restoration in a participatory fashion is thus
June 2021
an example of how the laudable goal of improving
environmental well-being can fail to translate into
responsible and effective practices.
Applying impact assessment
As Rolnick et al. [70] state, that " Each stakeholder has
different interests, and each often has access to a different
portion of the data that would be useful for impactful
[machine learning] applications. Interfacing between
these different stakeholders is a practical challenge for
meaningful work in this area. " Therefore, stakeholders,
such as landowners, policymakers, local communities,
and others need to have a voice in the application of AI
to forest ecosystem restoration. Impact assessment can
help address this gap (see Figure 4).
As discussed in the " Disciplinary divide " section,
the impact assessment process involves a
broad internal analysis by the organizations developing
AI systems for forest ecosystem restoration.
For an AI developer engaged in forest ecosystem
restoration, this would involve trying to understand
the variety of possible stakeholders and intended
or unintended impacts of their products. A company
that develops AI to identify target areas for
afforestation given carbon sequestration potential
might begin to identify previously unrecognized
impacts on species diversity, the local economy,
and the well-being of native groups.
To have a more accurate
understanding
of
broader impacts-as well as to build consensus
among stakeholders-the company would then
begin the user and stakeholder engagement process.
Critically, this would involve soliciting the input of
Figure 4. Applying impact assessment to
AI in forest ecosystem restoration.
89
IEEE Technology and Society Magazine - June 2021

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