IEEE Technology and Society Magazine - June 2021 - 88

Feature
Some fail to measure impacts at all, instead focusing
on anticipating impacts assumed to be important
and applying best practices to avoid associated
harms, such as bias. Inversely, some tools that are
not labeled " impact assessments " might be classified
as such under our definition, such as the
European Commission's Ethics Guidelines for Trustworthy
AI [54]. Notably, some frameworks have
been proposed by the public sector (i.e., governments)
and others by nongovernmental organizations
and companies.
We are hopeful that more scholars and organizations
focused on responsible uses of AI will adopt
an impact assessment approach or alternative frameworks
that meet the criteria identified above. A key
aspect of enabling effective impact assessment or
other frameworks is creating supportive structures,
such as by adopting new practices in institutions of
higher education as well as organizational changes
in firms. We turn to these issues briefly.
Supportive practices in higher education
and industry
Educational systems have undertaken meaningful
efforts aimed at increasing ethical sensitivity and
decision-making, but have not yet made the changes
needed to support responsible AI practice. Of around
200 AI/ML/data science courses reviewed by Saltz
et al. (2019), little more than 1 in 10 mentioned ethics
in their syllabus or course description. Those that
did focused overwhelmingly on bias, fairness, and
privacy [55]. Although courses focused specifically
on AI ethics cover a wider set of issues including
consequences of algorithms, technically tractable
issues like bias and privacy are still dominant [56].
We suggest that AI ethics education focus not solely
on a few prominent or technically tractable issues
nor on general awareness-building alone, but also
on impact assessment as an overarching framework
to understand AI's impacts on human well-being.
Further, educational institutions should seek to serve
diverse groups of students through coursework,
extracurricular clubs, and even contests to encourage
cross-disciplinary learning and practice.
Companies developing or deploying AI should
move toward the integration of technical and nontechnical
teams rather than functional separation of
roles, for reasons discussed previously. These integrated
teams could include technical developers
as well as other individuals tasked with considering
88
impacts of an AI system who may have social science,
humanities, business, law, or ethics expertise,
or who can represent user and stakeholder interests
effectively. Such a change requires establishing new
organizational and knowledge management practices
that are integrated with traditional engineering
and software lifecycles. Already, organizations
have proposed including a residential nontechnical
thinker tasked with responsible AI-an " ethics engineer "
or " responsible AI champion " [57]. Organizations
could also engage in interdisciplinary and
interdepartmental cross-training, using red team
exercises [37] or hypothetical case studies that draw
on the impact assessment approach.
In summary, we have argued that impact assessments
are a promising strategy to address the gaps
between
principles and effective
practices
responsible AI. However, applying an impact assessment
might feel like an abstract exercise to those
who have not done it. To demonstrate how closing
the principles-to-practices gaps with an impact
assessment might occur, we move now to a demonstrative
example case study.
Case study: Impact assessments
to support responsible AI for forest
ecosystem restoration
This section provides an example case study of
AI's use in forest ecosystem restoration efforts to
demonstrate the ideas and approach proposed in
this article. Further, it helps to highlight future work
needed for practical adoption of impact assessments
or similar responsible AI frameworks.
Case study background
Forest ecosystem restoration is essential for many
reasons. Forests have the most species diversity on
the planet, with some 80% of land-based species.
Forests also reduce the risk of natural disasters such
as floods, droughts, and landslides and help protect
watersheds [58]. Further, forests are critical for mitigating
land-based carbon emissions by increasing
carbon sequestration, critical for climate change
prevention goals [59]. Project Drawdown, for example,
has calculated that the restoration and protection
of tropical forests could lead to 61.23 gigatons of
carbon reduction by 2050 [60].
Achieving these goals requires the restoration of forest
ecosystems through the cultivation of trees, known
as afforestation [61]. Applied afforestation projects
IEEE Technology and Society Magazine
for
IEEE Technology and Society Magazine - June 2021

Table of Contents for the Digital Edition of IEEE Technology and Society Magazine - June 2021
