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Argument: A Realist Perspective on AI Regulation
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Argument
An expert's point of view on a current event.
A REALIST PERSPECTIVE ON AI REGULATION
EXPERIMENTATION IS THE RIGHT STRATEGY—AS LONG AS REGULATORS CAN LEARN FROM ONE
ANOTHER.
By Viktor Mayer-Schönberger, a professor of internet governance and regulation
at the University of Oxford, and Urs Gasser, a professor of public policy,
governance, and innovative technology at the Technical University Munich.
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Dozens of nations around the world are hurrying legislation to regulate the
design and use of artificial intelligence. Ahead of most, the European Union
just passed a comprehensive AI Act. Its stringent restrictions will come into
force over the next two years. Many other nations are not far behind. Global
corporate players are already lamenting the resulting regulatory fragmentation,
suggesting this will deprive users of valuable services or at the very least
increase costs that ultimately consumers will have to pay for. They aren’t
wrong. Regulatory diversity does lead to higher transaction costs. But their
innocent-sounding warning on behalf of the world’s consumers is not only a bit
self-serving. It’s also analytically flawed.
Dozens of nations around the world are hurrying legislation to regulate the
design and use of artificial intelligence. Ahead of most, the European Union
just passed a comprehensive AI Act. Its stringent restrictions will come into
force over the next two years. Many other nations are not far behind. Global
corporate players are already lamenting the resulting regulatory fragmentation,
suggesting this will deprive users of valuable services or at the very least
increase costs that ultimately consumers will have to pay for. They aren’t
wrong. Regulatory diversity does lead to higher transaction costs. But their
innocent-sounding warning on behalf of the world’s consumers is not only a bit
self-serving. It’s also analytically flawed.
--------------------------------------------------------------------------------
If AI is the transformational technology that will generate huge windfalls and
reconfigure our economies, perhaps even society as we know it, you can see why
regulators at all levels want a piece of the action. But for many nations,
especially ones with rapidly growing economic prowess, the way the internet has
been regulated over the past three decades serves as a cautionary tale.
Initially, the internet’s core governance tasks of managing IP numbers and
domain names—the internet’s “name space”—were in U.S. hands. When the internet
turned into a global phenomenon, this became untenable. But the tasks weren’t
taken over by a subunit of the United Nations or the International
Telecommunication Union. Rather, the role was taken on by the Internet
Corporation for Assigned Names and Numbers (ICANN), a purpose-built
California-based nonprofit organization that then went through repeated
fundamental changes of its internal governance regimes. BRICS members, many in
the global south, and others accused the United States of having created ICANN
to cement its power over the core of the global internet.
At one of a series of international diplomatic gatherings on the issue, the EU
tried to get ICANN to commit to fundamental rights in its governance. But the
United States teamed up with China and Russia to sink the proposal. To explain
U.S. opposition, one needs to look at internet governance through a realist’s
lens of power: The internet holds too much significance for U.S interests.
Keeping name space management at ICANN, unencumbered by any substantive
constraints, ensures the U.S. continuing levers of influence.
With AI today taking over in importance from the internet, nations are acutely
aware that now is the time to secure power and influence through AI oversight.
The result isn’t a simple race to regulate. More complex dynamics are at play.
For instance, Chinese support to anchor AI governance at the U.N. has been
described as an attempt to constrain the current Western dominance in both AI
technology and AI regulation. Conversely, Washington’s reluctance to involve
international organizations may be about keeping governance of the key
technology of our times close to home.
Regulating AI is made more important and more difficult because of the way AI’s
core features work and the commercial landscape that results from it. Training
AI takes a lot of data—and access to sufficient training data is severely
constrained. Training AI can be done through data that’s freely accessible
online, but that risks complex copyright and privacy lawsuits. Or it can take
place at the small number of very large digital platform companies that, through
their dominant digital services, ingest sufficient mountains of training data.
For instance, Alphabet, Google’s parent company, can do its own AI training,
thanks to the billions of data points that Google collects every day. Similarly,
Meta can train its model through data gleaned from Facebook, Instagram, and
WhatsApp. Meanwhile, Microsoft has teamed up with OpenAI through an investment
worth billions of dollars. Alphabet, Meta, Microsoft are U.S. companies. The
companies that can’t train their AI models due to the lack of data access sit
elsewhere: in Europe and in the global south, with perhaps the exception of
China. This makes for a very uneven distribution of AI providers, most of which
are either directly owned or have strong links to large platform companies.
Read More
British Prime Minister Rishi Sunak welcomes Italian Prime Minister Giorgia
Meloni during the AI Safety Summit in England.
WHAT IF REGULATION MAKES THE AI MONOPOLY WORSE?
In an industry already primed for concentration, creative alternatives for
safeguarding the public interest are needed.
Argument
|
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A journalist is silhouetted in front of a neon-lighted poster that reads "AI
from Africa to the world" at the first AI research center established in Africa
by Google in Accra, Ghana.
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The debate about regulating AI urgently needs input from the global south.
Argument
|
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was dropped in Hiroshima, Japan, on Aug. 5, 2004. (Junko Kimura/Getty Images)
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nuclear war.
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This unevenness translates into similarly skewed economics. Many businesses and
organizations around the world need and want to use AI but lack AI models
themselves. Instead, they purchase use of AI models from the few large
providers. The result is that the producers of AI are mostly located on the U.S.
West Coast, while users of AI are dispersed around the world.
This mirrors the situation on the internet, with a small number of U.S. platform
providers and billions of worldwide users. But unlike with social media, where
user-generated content is relatively diverse, such centralized AI provision not
only amasses most profits on the U.S. West Coast but, far more importantly,
provides AI-generated answers to billions of users based on AI models that have
been trained on data lacking global diversity. Or, to put it bluntly: West
Coast-generated answers shape global decision-making.
This means that issues of AI governance are arguably far more important than
those of internet governance. And the resulting power grab is far more
complicated than a competition among nations because organizations and
institutions within them are also jockeying for influence.
Some nations have formed specialized AI governance institutions cutting across
conventional regulatory boundaries to facilitate the rollout and enforcement of
AI laws. But that has not eliminated the role that already-existing institutions
play, from antitrust regulators, such as the Federal Trade Commission in the
United States, concerned with the concentration of training data for AI to data
protection boards worrying about the privacy of personal information used to
train AI, not to mention courts called to adjudicate intellectual property
claims over AI models trained on openly accessible yet mostly copyrighted
materials. New regulatory and enforcement institutions compete with existing
ones; institutions from one subject area with those in others. Add to this the
supra- and intranational layers already mentioned, and you get a sense of the
complex regulatory land grab to regulate AI that’s currently underway.
Importantly, appreciating this dynamic does not necessitate assuming any of
these regulators behave irrationally (although surely some of them may); it
unfolds when the individual regulators act to enhance their power—a perfectly
rational response to the emergence of a new phenomenon in need of regulation.
Examining AI regulation from a realist’s perspective of power helps understand
the multitude of approaches and institutions that are engaged. This is unlikely
to change soon, but is the regulatory diversity we see unfolding a need—or a
nuisance?
Regulatory diversity is undoubtedly costly. Sustaining a multitude of regulatory
institutions is expensive for society, and if these institutions are engaged in
turf wars, this is even more so. Far worse, companies and individuals must
comply with a plentitude of rules whenever their activities cross jurisdictional
boundaries (which happens all the time online). The resulting transaction costs
are inefficient, leading to higher prices and, potentially, lower service
quality. In contrast, a homogenous regulatory regime enables companies to reap
economies of scale and scope. Given the significance of comprehensive training
data and the economic reality that training an AI model is expensive but
querying it is cheap, scale and scope effects are hugely beneficial for AI
services. This suggests we should aim for reducing regulatory diversity to reap
substantial efficiency gains, following the lead of decades of harmonizing
economic regulations across institutional and jurisdictional boundaries.
The benefits of harmonization, however, are founded on a key premise: that we
have identified the right regulatory goal (or goals) as well as the appropriate
regulatory mechanisms to achieve this goal. The push for efficiency through
regulatory homogeneity works if our regulatory path forward is sufficiently
obvious. For regulating AI, the presence of this premise is unclear. When it
comes to regulating AI, we are still in the concept and search phase, despite
much public rhetoric to the contrary. While some argue that harmonizing AI
regulations globally could prevent a race to the bottom in terms of safety and
ethical standards, there is no consensus what exactly the goal of regulating AI
ought to be; many AI regulations strive to achieve multiple goals that are
complexly interdependent. Nor have we identified clearly and unambiguously the
most appropriate mechanisms by which to achieve these goals.
What is needed far more in such a phase of regulatory uncertainty is rule
diversity and the regulatory experimentation that ensues. If we don’t yet know
what we need, we must try many different strategies to find the most suitable
one. Experimentation is a necessary step but is not sufficient. The key to
success is that we learn from our experiments. Otherwise, rule diversity and all
the trials that follow will be in vain.
--------------------------------------------------------------------------------
The rationale for learning from one another in phases of regulatory
experimentation is rather obvious. What is less so is how to achieve it
practically. Several scholars of international political economy have
highlighted the importance of learning in contexts of regulatory
interdependence, especially when regulatory ends and means are not yet well
defined; they point to the importance of open channels of communication and
innovative contexts of such learning. Past cases include the regulation of
ozone-depleting chemicals, new drug approval and monitoring, and genetically
modified crops.
But most institutions of regulatory harmonization, from the intranational to the
supranational level, have been established and designed to introduce regulators
to the single best solution that has already been identified, rather than
enabling a cacophony of regulatory strategies from which to learn.
Institutionally, these regulatory bodies are ill-equipped to facilitate the kind
of experimentation and learning that currently is most needed.
This puts us into a bind: If we want to enable regulatory experimentation and
learning, we need institutions to facilitate that. But the very institutions at
our disposal—such as the post-World War II Bretton Woods setup that culminated
in the establishment of the World Trade Organization and championed global
harmonization of trade regulations—are ill-suited to do that. Whether these
institutions, so focused on a particular set solution, can be reconfigured in
time to facilitate such open-ended learning is an open question. After all, we
are not talking about fine-tuning existing processes but rather replacing an
institution’s very aim and means and reshaping its processes and institutional
working accordingly. Perhaps we need different institutions altogether to aid in
this experimentation and learning—or, at the very least, to ensure institutions
are willing to fundamentally reconfigure existing organizations. The framework
for governing the Volta River Basin in West Africa may be an example of the
former strategy (and so is Wikipedia, if you prefer a more digital and less
government-centric example), while the repeated (and ultimately successful)
restructuring of ICANN after the initial failed start may be a case in point of
the latter.
There is no proven blueprint, though. Experimentation and learning at the
substantive regulatory level entail experimentation and learning at the
institutional level as well. Collecting good ideas, sharing them with others,
and learning from the experiences of others may be costly but far likely to be
more successful in the long term.
Viktor Mayer-Schönberger is a professor of internet governance and regulation at
the University of Oxford and co-author of Guardrails: Guiding Human Decisions in
the Age of AI, with Urs Gasser.
Urs Gasser is a professor of public policy, governance, and innovative
technology at the Technical University Munich and co-author of Guardrails:
Guiding Human Decisions in the Age of AI, with Viktor Mayer-Schönberger.
Read More On AI | Science and Technology
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