IEEE Technology and Society Magazine - March 2020 - 19

not aligned with these goals and
impacts [40, p. 144], which is unsettling given the myriad of ML project
and initiatives worldwide.
The IEEE report also lays down
principles to guide "the ethical and
values-based design, development,
and implementation of autonomous
and intelligent systems," many of
which are similar to those defined
by the Montreal Declaration: respect
of human rights, data agency, transparency, accountability, etc. They go
further in proposing that "A/IS creators shall adopt increased human
well-being as a primary success criterion for development" instead of
focusing on isolated metrics such
as accuracy, and from a deployment perspective, offering alternative metrics to quantify meaningful
progress, for instance by evaluating social, economic, and environmental factors instead of profit and
other common success metrics. The
report also includes propositions for
policymakers, legislators, and other
stakeholders from the extended
AI community and, as such, represents the most extensive effort of
establishing ethical boundaries and
guidelines for AI research to date.
In a recent survey of the various
global ethics guidelines proposed
around AI, the authors observed
that despite a conceptual overlap
between the many existing guidelines, including the two mentioned
above, there are major differences
regarding how the principles are
interpreted [21]. This underlines
the complexity and nuance of applying theoretical, philosophical
principles in practice, and raises
questions such as: what aspects
of the AI research and deployment
pipeline do ethics principles affect? How would it be possible to
resolve conflicts between, for instance, fairness and sustainability
(i.e., training an algorithm longer
and with more data - thus potenMARCH 2020

tially leading to more greenhouse
gas emissions - to ensure that it
is not discriminatory and covers all
demographic groups equally well)?
And, above all, how is it possible
to translate ethical principles into
a programming language? In any
case, the bridge between theory
and practice has yet to be built and
there are different ways in which
that can happen. This underlines
the necessity of involving actors
from different levels of the AI ecosystem (and neighboring ones) in
order to ensure that experts in policymaking work in tandem with experts
in coding and engineering to create
tools and frameworks that are coherent and usable by all.

Identifying Ethical Concerns of
AI Applications
There are several types of ethical
concerns regarding AI applications
and, in this paper, we focus more
concretely on bias leading to potential discrimination. While it is true
that on the one hand, AI-infused
technology such as computer vision
can enhance public security, for instance by identifying crime in realtime based on CCTV cameras - the
trade-off is that security features
can also be abused to track individuals and to establish a surveillance state where privacy is greatly
threatened by those who control
the technology.
On the military side, similar
technology can be used to design
autonomous drones that use computer vision to identify their target,
representing a grave threat to global
security and democracy due to the
lack of human oversight. In addition
to the security risk, such weapons
would be moral and legal hazard: AI
technology is not yet able to comprehend and represent the social and
psychological context in which such
a targeted attack could take place in
a manner that is coherent with inter∕

IEEE TECHNOLOGY AND SOCIETY MAGAZINE

national laws regarding war or with
human morality. Unfortunately, the
most common argument brought in
favor of developing lethal autonomous weapons is that they are needed as a precautionary measure (i.e.,
since other countries are undeniably working on them, each country
needs to do the same). In reality, the
weapons needed to defend against
killer drones would be very different
from the drones themselves, and do
not need to be lethal autonomous
weapons since they would be designed to destroy weapons rather
than to target people, similar to the
Iron Dome used by Israel.
Another common argument is
that an international treaty would
be useless since some countries
will refuse to sign it. But we have
seen in the past that even when major powers do not sign a treaty (such
as the one on anti-personnel mines,
signed by 133 countries, excluding
the U.S., in 1997), the treaty can still
be used to create a moral stigma,
as well as a decline in demand; in
the case of anti-personnel mines,
the result has been that U.S. companies have stopped building them,
even though their government never
signed the treaty.
Another flawed argument is that
regulating lethal autonomous weapons could threaten innovation in AI,
whereas in fact AI has been developed very successfully in a civilian
setting (mostly in academia and
major technology companies) and
its continued development does not
require data or engineering from AI
military development.
Another potential threat to democracy stemming from AI could
come not simply from the increased
ability to monitor and to target individuals, but also from the more subtle power to influence them, e.g., via
AI-driven advertising, automated online trolls, and other psychological
manipulations via the Internet and

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IEEE Technology and Society Magazine - March 2020

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