by Ajay Agrawal
market, a weakening dollar might repatriate manufacturing jobs. At the high
end of the market, skilled US workers will for the fi rst time be exposed to
competition from a low- wage country. In isolation, this would reduce one
dimension of domestic US inequality.
If the Chinese market becomes open to US technology giants (and vice
versa), both the Melitz (2003) model and the Oberfi eld (2018) model of trade
predict that the giants will grow even larger. In the context in which these
companies have already absorbed one- fi fth of US value added, and may
have contributed to US top- end inequality, the impact of international trade
in further growing these impacts may increase top- end inequality.
26. https:// obamawhitehouse.archives .gov/ sites/ default/ fi les/ omb/ IPEC/ admin_strategy _on_mitigating_the_theft_of_u.s._trade_secrets .pdf.
Artifi cial Intelligence and International Trade 489
19.6 Conclusion
How will artifi cial intelligence aff ect the pattern of trade? How does it
make us think diff erently about trade policy? In this article we have tried to
highlight some key points.
First, the nature of the technology suggests that economies of scale and
scope will be important. Furthermore, as a knowledge- intensive industry,
knowledge externalities are likely to be important. Prior literature on other
industries suggests that such externalities are often local, but more evidence
is needed. Second, the trade models that are likely to be most useful in
understanding the impact of AI are those that account for these points,
specifi cally, scale, knowledge creation, and the geography of knowledge dif-
fusion. These models suggest that whether AI- focused trade policies (or
AI- focused investments in clusters) are optimal will depend very much on
the presence of scale and the absence of rapid international knowledge diff u-
sion. Third, we discussed whether and how regulation might be used to favor
domestic industry. We highlighted that privacy policy that targets consumer
protection is unlike many other regulations in that it is likely to hamstring
domestic fi rms, even relative to foreign ones. So, rather than focusing trade
discussions on how privacy policy might be used as a disguised restriction
on trade, such discussions should emphasize regulatory harmonization so
as to avoid a race to the bottom. In contrast, several other policies may be
used to favor domestic fi rms including data localization rules, limited access
to government data, industry regulations such as those around the use of
drones, and forced access to source code.
Generally, this is an exciting new area for trade research and policy. There
is still much to learn before we have a comprehensive understanding of these
questions.
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20
Punishing Robots
Issues in the Economics of
Tort Liability and Innovation
in Artifi cial Intelligence
Alberto Galasso and Hong Luo
20.1 Introduction
A tort is an action that causes harm or loss, resulting in legal liability
for the person who commits the act. The role of the tort system is to deter
people from injuring others and to compensate those who are injured. Two
important classes of tort law are product liability law that protects cus-
tomers from defective or dangerous products, and medical malpractice law
that governs professional negligence by physicians. Tort suits often make
the headlines because of their large damages awards. For example, General
Motors recently paid about $2.5 billion in penalties and settlements in a case
involving faulty ignition switches linked to 124 deaths.1
Rapid advancements in the fi eld of artifi cial intelligence and robotics have
led to lively debates over the application of tort law to these technologies. For
example, the diff usion of autonomous vehicles is expected to shift the focus
of motor vehicle accident litigation from driver liability to product (i.e.,
manufacturer) liability. Similar shifts are expected in health care because of
advances in robot- assisted surgery and robot assistance for the elderly and
disabled. These changes in the technological and economic landscape are
also seen as an opportunity to redesign regulatory and liability rules. For
Alberto Galasso is associate professor of strategic management at the University of Toronto and a research associate of the National Bureau of Economic Research. Hong Luo is the James Dinan and Elizabeth Miller Associate Professor of Business Administration at Harvard Business School.
For acknowledgments, sources of research support, and disclosure of the authors’ material fi nancial relationships, if any, please see http:// www .nber .org/ chapters/ c14035.ack.
1. https:// ca.reuters .com/ article/ businessNews/ idCAKBN19E25A- OCABS and Del Rossi and Viscusi (2010) document one hundred cases with punitive damages awards of at least $100
million as of the end of 2008.
493
494 Alberto Galasso and Hong Luo
example, in February 2017 the European Parliament adopted—by a large
majority—a resolution containing recommendations for EU- wide legisla-
tion to regulate “sophisticated robots, bots, andr
oids and other manifesta-
tions of artifi cial intelligence” and to establish legislative instruments related
to the liability for their actions (European Parliament 2017). An eff ective
design and implementation of these policy changes require an understand-
ing of how liability risk aff ects fi rms’ strategies and shapes future techno-
logical progress.
In an infl uential book, Porter (1990) concludes that “product liability is
so extreme and uncertain as to retard innovation,” and he recommends a
systematic overhaul of the US product liability system. A number of legal
scholars share this view and warn about a potential “chilling eff ect” on
innovation; that is, high damages awards may reduce fi rms’ willingness to
develop new and riskier technologies, even if they are potentially superior to
customary products (e.g., Huber 1989; Parchomovsky and Stein 2008). This
idea that excessive liability may retard innovation also shaped high- profi le
legal cases such as the 2008 Riegel v. Medtronic Supreme Court decision and
is a key argument for tort reforms currently discussed in the US Congress.
Despite the fundamental relevance of this issue, empirical work on the
relationship between liability and innovation is scarce. Huber and Litan
(1991) brought together a broad set of experts on fi ve sectors of the economy
where the liability system would have had the largest impacts. Based mostly
on surveys and historical case studies, the authors were far from reaching a
consensus. What were commonly agreed upon, however, were the dearth of
data and systematic evidence, and a call for future research.
This chapter reviews the handful of empirical studies on the links between
liability and innovation using a large sample of data. It aims to provide some
insights into the potential impacts that liability laws and likely changes in
the system may have on the rate and direction of innovation in robots and
artifi cial intelligence, and to identify areas and questions for future research.2
20.2 Liability and Innovation: An Illustrative Theoretical Model
This section presents a simple, stylized model that explores the eff ects of
liability risk on innovation incentives. Technologies are characterized by
multidimensional heterogeneity. Specifi cally, a technology, i, is characterized by two parameters: b ∈ [0,1] and r ∈ [0,1]; b is the expected profi t from i