IEEE Technology and Society Magazine - December 2021 - 11

should champion to gain acceptance for AI soft law
programs within a community of stakeholders. This
is followed by the presentation of program archetypes
whose design encourages soft law program
implementation via the alignment of incentives.
Last, we underscore the interplay between soft law
and its counterpart, government regulations (also
known as hard law). Rather than being at odds
with each other, the articles within the special issue
find instances where soft and hard law efforts are
complementary.
Principles
Our special issue contributors suggest two highlevel
norms or ideals that are essential for an AI soft
law program's success: trust and credibility. Hilary
Sutcliffe of Society Inside and Samantha Brown,
co-founder of Consequential, argue in their article
that trust in how an organization manages technology
is characterized by the display of seven drivers
that are " deeply rooted in ... individual and collective
psychology " : focus on the public interest, competence,
respect, integrity, inclusion, openness, and
fairness. To build these drivers, the authors offer tangible
suggestions on how an organization can earn
trust and effectively communicate an AI soft law program's
objective.
Craig Shank, formerly of Microsoft, turns his attention
to offering strategic planning considerations for
generating credibility in an AI soft law program. His
article centers on the legitimacy and accountability
of an organization to its stakeholders. Specifically,
three factors should be taken into account. First,
appropriate participants must be included. This
means that internal and external parties that should
be visible and have a voice in a program need to be
seen and heard. Second, the process must withstand
the scrutiny of participants by balancing its transparency,
integrity, and flexibility. Last, a program's credibility
depends on its output. If its ultimate usefulness
is in question because of its perceived lack of technical
quality, or if it disregards the input of relevant
participants, then it is bound to be viewed negatively
and serve little to no purpose.
Program archetypes
Guaranteeing an AI soft law program's enforcement
or compliance requires that designers
think about the alignment of incentives. To
this end, our special issue contributors suggest
December 2021
program structures that address the-sometimes
conflicting-motivations of stakeholders. Simply
put, it is not enough to agree on broad principles.
There must be appropriate drivers and incentives in
place or created for entities developing and using AI
to responsibly implement these ideas. To this end,
Daniel W. Linna Jr. of Northwestern University discusses
how soft law governance in the legal industry
can have downstream consequences in improving
the " effectiveness " of technology solutions. In the
United States, the activities performed by lawyers
are regulated by the American Bar Association
(ABA) and its Model Rules of Professional Conduct.
Through his analysis, Linna offers detailed suggestions
for how the ABA can generate norms for its profession
that compel technology suppliers to improve
their AI-based technologies.Lucille N. Tournas and
Diana M. Bowman of Arizona State University turn to
a proactive medium that can shape AI governance:
insurance. As a risk mitigation tool, it represents an
agreement where a firm promises to cover the losses,
liabilities, or damages experienced by another entity
due to certain activities. To uphold their responsibility,
insurance companies can assert their influence
by demanding the implementation of standards and
norms. The authors describe a relevant precedent
for this tool in emerging technologies in the form of
nanotechnology.
Sara Jordan of the Future of Privacy Forum
devotes her attention to review boards, bodies
that ensure compliance with principles or standards
in the development of AI research or applications.
Her piece identifies three challenges that any
AI-specific review board must address for it to have
impact. First is the need for authority or the capability
to compel action. Second is the determination
of scope or what segment of professionals should
be included in the evaluation of AI subject matter.
Finally, AI can have lasting effects on an institution
and society, hence a board must define the extent
of its risk assessment. On reflection, Jordan raises
some important matters with respect to AI innovation
itself. A follow-on question that remains is, if it
is expected that academic researchers need to go
through Institutional Review Boards (IRBs), then
how much more is needed for a technology that is
being unleashed into the open market and has the
capacity to impact thousands of businesses and millions
of people. A determination needs to be made
on what mechanism should be implemented for the
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IEEE Technology and Society Magazine - December 2021

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