IEEE Technology and Society Magazine - March 2020 - 16

COMMENTARY

Alexandra
Luccioni

M

Artificial Intelligence Leaves
the Research Lab
Progress in ML in the last decade
has been extraordinary and has
rekindled the notion that AI systems could eventually reach human
levels of performance, which was
Digital Object Identifier 10.1109/MTS.2020.2967486
Date of current version: 2 March 2020

16

On the Morality of
Artificial Intelligence

©ISTOCKPHOTO.COM

uch of the existing research on the social
and ethical impact of
Artificial Intelligence
has been focused on
defining ethical principles and guidelines surrounding Machine Learning
(ML) and other Artificial Intelligence
(AI) algorithms [21]. While this is extremely useful for helping define the
appropriate social norms of AI, we
believe that it is equally important to
discuss both the potential and risks
of ML and to inspire the community
to use ML for beneficial objectives. In
this article, which is primarily aimed
at ML practitioners, we thus focus
more on the latter, carrying out an
overview of existing high-level ethical frameworks and guidelines, but
above all proposing both conceptual
and practical principles and guidelines for ML research and deployment, insisting on concrete actions
that can be taken by practitioners
to pursue a more ethical and moral
practice of ML aimed at using AI for
social good. In this way, actions can
be evaluated again these principles
and guidelines to serve as a kind of
"social litmus test" by which others
can hold ML practitioners to account.

Yoshua Bengio

abandoned for several decades.
Even if we are still currently far from
this achievement, technological
progress in ML has passed a threshold that enables it to have a huge
economic impact, estimated to be
close to 16 trillion US dollars by
2030 [38]. This contrasts with the
first few decades of ML progress,
when researchers had the luxury of
focusing purely on the fundamental aspects of their work, not worrying too much about its potential
societal impacts - an object recognition algorithm could be tested
on a common dataset like MNIST
IEEE TECHNOLOGY AND SOCIETY MAGAZINE

[24] or ImageNet [10], and an objective performance metric would
be obtained in order to measure
progress, without having to think
about the messiness and complexity of deployment and social impact.
Something crucial has changed in
recent years, as algorithms initially
developed in the lab are increasingly being improved and deployed
in society in real-world applications
such as healthcare, transportation,
and industrial production with reallife consequences, and we are likely
seeing just the tip of the iceberg in
terms of social impact.
∕

MARCH 2020



http://www.ISTOCKPHOTO.COM

IEEE Technology and Society Magazine - March 2020

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