IEEE Technology and Society Magazine - December 2021 - 12

Guest Editors' Introduction
approval of these technologies that is beyond the
internal controls of an organization and the limited
international governance scrutiny [10].
On the international front, Walter Johnson and
Diana M. Bowman of Arizona State University examine
instruments available to set a global governance
agenda for AI. Although alternatives exist in the form
of international law, framework conventions, intergovernmental
organizations,
and
public-private
partnerships,
the authors see standards as the future
of international governance. They argue that these
tools, created by multiparty bodies, are generally
able to allay the legitimacy and participation concerns
present in alternative forms of global efforts to
manage AI.
Soft and hard law approaches can complement
each other to manage AI applications and methods.
Two of this issue's contributory articles evince this
synergy. Emile Loza de Siles of Duquesne University
offers the U.S. National Institute of Standards
and Technology (NIST) Cybersecurity Framework
(https://www.nist.gov/cyberframework) as an example
of a soft law program that lays the foundation for
hard law requirements in several federal agencies.
Considering this, she proposes that NIST undertake a
similar effort to create bias and discrimination standards
for AI.
The article by Neil A. Chilson and Taylor D. Barkley
of the Charles Koch Institute suggest a two-tiered
strategy in the U.S. governance of facial recognition
technologies (FRT). On the one hand, government
utilization of FRT should be subject to hard law that
guarantees the protection of civil liberties. On the
other, the continuously changing nature of commercial
FRT applications should rely on soft law to
dynamically address privacy and bias issues. Last, we
have an article by Jordan Buckwald and Gary Marchant
of Arizona State University titled " Improving Soft
Law Governance of the Internet of Things. "
The ideas contained within this issue should provide
stakeholders with a new perspective to consider.
One that views soft law programs as processes
and practices that are instituted in between the
time it takes for a technological innovation to pass
over the " valley of death " period into the nascent
stages of commercialization and initial wider diffusion
[11], [12]. By the provision of emergent standards,
industry guidelines, or frameworks, it can be
argued that there better be " something " that stakeholders
can cling on to in terms of soft law, rather
12
than a complete absence of structure, direction, and
acceptable application.
This phenomenon was particularly prevalent
in the early phases of diffusion of automatic identification
technologies, including the introduction
of biometrics for applications beyond law enforcement
[13]. About the importance of standards in
practice, Michael [14] wrote: " it is without a doubt
that the BioAPI Consortium activities placed pressure
on the International Standards Organization
(ISO) to develop formalized biometric standards to
assist with the proliferation of biometric applications
worldwide. Without a common language, the implementation
of automated recognition systems would
have been severely inhibited. " Today, we see the
greater complexity of socio-technical challenges surrounding
AI that are triggering legal disputes. Technological
convergence now means that we have
automated facial biometrics possible through firmware
updates (containing machine learning algorithms)
in closed-circuit television that make use
of billions of publicly sourced web-scraped images
able to identify close matches. In some places, total
or partial bans have been accepted [15], while in
others, open innovation continues to drive roll-out
and adoption [16].
This special issue's objective is to provide
stakeholders with ideas to consider when building
effective and trustworthy AI soft law programs. In
this regard, we would like to strongly encourage
organizations to think about the sustainability of
their efforts and avoid devoting resources to the
creation of initiatives that are not intended to be
enforced or implemented. Overall the special issue
contributors have revealed that trust and credibility
are fundamental principles in the design of AI soft
law and that, when selecting program archetypes, it
is imperative to consider the alignment of incentives
between parties. Finally, the exclusive use of soft
law is not the answer to every governance problem.
Instead, a flexible approach that complements or
substitutes for hard law can create effective synergies
for the governance of AI.

 References
[1] G. E. Marchant and B. Allenby, " Soft law: New tools
for governing emerging technologies, " Bull. Atomic
Sci., vol. 73, no. 2, pp. 108-114, Mar. 2017, doi:
10.1080/00963402.2017.1288447.
IEEE Technology and Society Magazine
https://www.nist.gov/cyberframework
IEEE Technology and Society Magazine - December 2021

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