Introduction to International
Public Policy (IIPP)
Haotiain QI
SIS-PKU
Fall 2019
Week 14
Technological Shock
and New Actors
December 17, 2019
Part01
C ONTENTS
P03
Part02
Part03
Part04
Housekeeping
P05
Big Data
P27
Artificial Intelligence
P61
Q&A
Part One
Housekeeping
Part One Housekeeping
1) Logistics
2) Final Exams
Part TWO
Big Data
Part Two Big Data
Part Two Big Data
Technology is NOT an apolitical and amoral force.
- Many technologists and philosophers have argued that complex
technologies are inherently dual-nature, not just dual-use.
- This argument goes back to insights developed from decades ago
in the field of Science and Technology Studies, explaining that
modern technology is essentially co-produced by scientific and
societal actors.
- Modern technology is not about algorithms as abstract artefacts
used for good or bad, but algorithms endowed with a specific
structuring function, a certain power and nature.
- It is this structuring function, the technological design process
itself and the real-world implications that are increasingly under
scrutiny within society.
- It is also this structuring function that is offering better
governance a chance.
Part Two Big Data
In 2019 alone, global IP traffic reached over 1 Zettabytes, which is equivalent to 1 billion Gigabytes. We are
inundated with much more data than we can analyze.
Data mining and analyzing has already taken effect:
within the government to mitigate fraud,
in law enforcement to predict future crimes,
in education to improve academic performance,
and in infrastructure to monitor the city’s usage of precious resources.
Part Two Big Data
Widespread use of digital technologies, the Internet and social media means both citizens and
governments leave digital traces that can be harvested to generate big data. Policy-making takes
place in an increasingly rich data environment, which poses both promises and threats to policymakers.
- New digital technologies enable creativity, connect the world, and provide public goods and
essential services.
- But they also challenge conventional notions of privacy, facilitate crime, and enable
surveillance and oppression.
How and to which end these data-driven technologies are used is determined by political, corporate,
societal, and individual choices.
Part Two Big Data
Promises
- Data offers a chance for policy-making and implementation to be more citizen-focused, taking
account of citizens’ needs, preferences and actual experience of public services, as recorded on
social media platforms.
- As citizens express policy opinions on social networking sites such as twitter, facebook, weibo,
wechat; rate or rank services or agencies on government applications, or enter discussions on the
burgeoning range of social enterprise and NGO sites, they generate a whole range of data that
government agencies might harvest to good use.
- Policy-makers also have access to a huge range of data on citizens’ actual behavior, as recorded
digitally whenever citizens interact with government administration or undertake some act of
civic engagement, such as signing a petition.
Part Two Big Data
- Data mined from social media or administrative operations in this way also provide a range of
new data which can enable government agencies to monitor and improve their own
performance, for example through log usage data of their own electronic presence or
transactions recorded on internal information systems, which are increasingly interlinked.
- And they can use data from social media for self-improvement, by understanding what people
are saying about government, and which policies, services or providers are attracting negative
opinions and complaints, enabling identification of a failing school, hospital or contractor, for
example.
Part Two Big Data
Examples of good use
Global health governance system implemented a flawed prevention system that identified $210.7
million in improper payments to health care providers. Big Data not only helps save money but also
saves lives as it expedites the development of new treatments and containment of viral outbreaks.
- Earlier in 2018, the World Health Organization declared the Zika Virus a global health emergency
and predicts cases to rise to four million in the next year. Currently there are no reliable tests and
vaccines for the virus, but utilizing a data driven infrastructure to identify trends and analyze clinical
test results will shorten the race to the cure.
- Moreover, it can help medical researchers develop new treatments and help doctors make better
decisions. For government, or any other non-market players, they can help reap these benefits by
solving the data quality and availability issues, to encourage improvement of local electronic system,
lowering long-term administrative costs.
Part Two Big Data
Public safety
- Implementing predictive policing is relatively new. In the U.S., it is currently being tested and
deployed in 60 cities. The method involves algorithms and data from type, place and time of previous
committed crimes in order to assign probabilities of future crime events to regions of space and time.
- The city in which the practice was created, Santa Cruz, CA, U.S., saw burglaries drop by 11% and
robberies by 27% in the first year the system was implemented. Same success rates were found in
Reading, PA, U.S. where crime dropped to the lowest it has seen in 35 years.
Part Two Big Data
Education
- Predictive analysis can be used to identify students at risk of dropping out. Adaptive tutoring system
can change according to individual student’s learning styles. Monitoring student retention rates will
make way for enhancing student academic performance and therefore overall satisfaction among
students, teachers, and the administration.
- Data gathered from individual students’ learning styles can also assist teachers as they can adjust
their teaching styles. Working from the inside out, this provides insight on which programs are
failing or succeeding in which to invest more or less focus and funds on.
Part Two Big Data
Macroeconomic forecasting
In most countries, national statistical agencies collected macroeconomic data through surveys.
These surveys are costly and are not available in a real-time fashion. Private sector data sources
however have the potential to supplement and even replace traditional surveys with near-real
time data.
Part Two Big Data
Environmental Governance
- Versatile: can be used to monitor an area as vast and expansive as the Amazon Rainforest, or it
can monitor a small city’s water supply. Enabling environmental protection on every level —
individual, community, country and global.
- Increased speed and ease of obtaining data. In the past, most environmental data came from
individual scientists out in the field. It was a slow, laborious process that didn’t provide useful
information for many months or years. With Big Data that same information is gathered much
quicker which leads to rapid implementation.
Part Two Big Data
Deforestation: On the side of companies responsible for deforestation, Big Data provides alternative
solutions to the immense tree-cutting done every day — lowering the carbon footprint and decreasing
the negative impact on the ecosystem.
Endangered species: deforestation also causes problems for numerous plant and animal species. As they
lose their natural habitat, the probability that these plants and animals will survive drops significantly.
When a forest is razed, a community of plants and animals is also destroyed. By implementing Big Data,
entities can effectively monitor both plant and animal species in danger of extinction. Information is
gathered and plans are created early enough to fight the problem and preserve the species.
Part Two Big Data
Poaching: Traditionally, forest rangers and other land protectors have faced an uphill battle in
prosecuting poachers because of the enormous areas they’re responsible for. They can only cover
small parts of an entire area. With Big Data, however, their reach is significantly expanded. Trouble
areas and animals can be pinpointed and actions taken to prevent poaching.
Residential waste (smart city): Wasting water is something most cities can’t afford. Cities may be
able to monitor how much water you use in a month, and you may be able to monitor when you
watered, but other important information goes undiscovered. Big Data is essential in order to forge
ahead and provide our progenitors with a sustainable future. The more info gathered, the better
protected the environment becomes.
Part Two Big Data
In short.
data has the potential to spur economic growth and improve quality of life in a broad array of fields, by
helping individuals and organizations make better decisions.
- The private sector is currently using vast quantities of data for variety of purposes, including
optimizing energy efficiency in buildings, reducing mechanical failures in equipment, and improving
crop yields on farms.
- The public sector has many opportunities to use data to address major social issues such like
improving health care, fighting crime, building more sustainable communities, and creating more
efficient transportation systems.
What’re clear: the potential benefits
Far from certain: how to achieve those benefits
Part Two Big Data
Across-jurisdiction
Large-scale data analysis is capable of driving global-scale innovation, but data exchange between countries is a
major challenge to many potential benefits of data. One major impediment: data residency law and other laws
restricting information flow. These laws prohibit data from being stored or accessed outside a nation’s borders.
- For instance:
Danish and Norwegian data protection authorities prevent the use of cloud services when servers are not located
domestically;
Canadian provinces of British Columbia and Nova Scotia have instituted laws mandating that personal information
in the custody of a public body can only be stored and accessed within the country;
In some countries, government data sources are not kept secret.
Part Two Big Data
Data-driven innovation can be slowed down with these legal restrictions. There is a need for
international legal standards for government and private access to data across national borders,
initiatives like G8 Open Data Charter, or proposed “Geneva Convention on the Status of Data”
- This requires both political and technological cooperation. Policy standards, as well as
technological standards
- In the area of “Internet of Things”, data formats are being standardized, in most other areas like
mobile health, humanitarian aid, the political processes are falling behind.
Part Two Big Data
Risks
Big data presents new moral and ethical dilemmas to policy makers.
- For example, it is possible to carry out probabilistic policy-making, where policy is made on the
basis of what a small segment of individuals will probably do, rather than what they have done.
- Predictive policing can lead to a “feedback loop of injustice”, as policing resources are targeted at
increasingly small socio-economic groups.
- What responsibility does the state have to devote disproportionately more – or less – resources to
the education of those school pupils who are, probabilistically, almost certain to drop out of
secondary education?
- Consumers trade privacy to allow Facebook to use their data on the basis it will improve their
products, but if government tries to use social media to understand citizens and improve its own
performance, will it be accused of spying on its citizenry in order to quash potential resistance.
Part Two Big Data
Efficiency vs rights, a necessary trade-off?
Again, surveillance, for example
Cameras are everywhere. Children in school, customers in shops, and pedestrians on the street are
increasingly under more – and more advanced – surveillance.
Today, law enforcement agencies and government entities in many countries make use of facial
recognition technology on a routine basis. Private vendors also deploy such systems for
commercial use. And demand is ever-increasing in both the commercial and the security sphere:
the facial recognition technology market is predicted to be worth $2.67 billion in 2022 in the U.S.
alone.
But the pervasiveness of these technologies raises many privacy and ethical concerns. Experts and
civil liberty advocates criticize the use of facial recognition technologies on citizens and
consumers, citing concerns about accuracy, accountability, and the potential for racially disparate
impacts of its use.
Part Two Big Data
what
Part Two Big Data
Technical AND political solutions
For example: research on privacy-preserving data mining. Various techniques can be sued to add noise
to sensitive datasets so that individual information cannot be extracted. Like synthetic data, which
preserves the usefulness of sensitive datasets by emulating their underlying statistical characteristics
while simultaneously masking individual information.
-- here comes the issue of organic governance – many agencies are working on this data issue,
but their research and management efforts are not coordinated.
To achieve a balance between efficiency and rights protection, what’s needed is not just a
technological process, but more of a policy framework – a combination of data sharing agreements,
incentives for individuals to participate, and robust protection and regulations mechanisms.
Part Two Big Data
Not just Big Data of Governance
But also Governance of Big Data
- Voluntary sharing of data is not always assured in private sector. But when there is a strong public
interest, public policy should be used to mandate and incentive private sector to share.
- In government, the mechanism is different. Data collection is not voluntary. Only limited by the
decisions of government agencies and the political process that holds government officials
accountable. Some governmental agencies, like those in intelligence community and law
enforcement, have a bias in favor of collecting more information. This must be tempered by
competing interests, including the civil liberties of individuals and the economic impact of such
decisions.
Part Three
Artificial Intelligence
Part Three Artificial Intelligence
No other technology has recently generated so many hopes and fears, expectation and trepidation,
celebration and condemnation as Artificial Intelligence (AI)
In its current form, called deep learning, AI optimizes predictive reasoning by learning how to
identify and classify patterns within massive amounts of data. With this super-computing efficiency –
being able to simulate myriads of scenarios in seconds – deep learning offers unmatched investigative
opportunities, such as comparing genomes within an entire population, recognizing a certain face out
what
of a crowd, or labelling any location on earth based on millions of pictures.
As a response to the development of AI (and big data), the UN Secretary-General António Guterres
has just established a High-level Panel on Digital Cooperation to foster a “broader global dialogue on
how interdisciplinary and cooperative approaches can help ensure a safe and inclusive digital future
for all.”
Part Three Artificial Intelligence
Looking ahead, future technology seems indistinguishable from magic. But as technology
advances, it creates magic. That will be the case with AI.
- Arthur C. Clack
what
Part Three Artificial Intelligence
“AI will transform the nature of wealth and power.”
Three lenses
- The first lens is that of general purpose technology.
AI fits the category of general purpose technology, which are classes of technologies that
provide a crucial input to many important processes, economic, political, and military, social,
and are likely to generate these complementary innovations in other areas.
what
General purpose technologies are also often used
as a concept to explain economic growth,
things like the railroad, steam power, electricity, motor vehicle, airplane, or computer. And it’s
plausible that artificial intelligence not only is a general purpose technology, but is the
quintessential general purpose technology.
Kevin Kelly :“Everything that we formally electrified, we will now cognitize. There’s almost
nothing we can think of that cannot be made new, different, or interesting by infusing it with
some extra IQ.”
Part Three Artificial Intelligence
- The second lens is to think about AI as an information and communication technology. other
technologies in that reference class would include the printing press, the internet, and the
telegraph.
They make possible new forms of military, new forms of political order, new forms of business
enterprise, and so forth.
- The third lens is that of intelligence. Unlike every other general purpose technology, which
applied to energy, production, or communication or transportation, AI is a new kind of general
what
purpose technology. It changes the nature of our
cognitive processes, it enhances them, it
makes them more autonomous, generates new cognitive capabilities. And it’s this lens that
makes it seem especially transformative.
In part because the key role that humans play in the economy is increasingly as cognitive agents, so
we are now building powerful complements to us, but also substitutes to us, and so that gives rise
to the concerns about labor displacement and so forth. In addition, innovations in intelligence are
hard things to forecast how they will work, and that makes it especially hard to see what it will
bring.
Part Three Artificial Intelligence
what
Part Three Artificial Intelligence
what
Part Three Artificial Intelligence
AI and human
Substitutive effect
vs.
Complementary effect
-
Whether data is ample
Whether info is complete
Level of certainty
Degree of multi-areas
what
Part Three Artificial Intelligence
Labor displacement and inequality.
This is not science fiction to talk about the impact of automation and AI on
inequality. Economists are now treating this as a very serious hypothesis, and the
bulk of belief within the economics community is that AI will at least pose
displacement challenges to labor, if not more serious challenges in terms of
persistent unemployment.
Whether a necessary outcome, debatable.
what
Part Three Artificial Intelligence
Monopoly of tech power
Digital services in general, but AI in particular, have a natural global monopoly structure.
And this is because the provision of an AI service, like a digital service, often has a very
low marginal cost.
In a market like that for Netflix or for Google Search or for Amazon e-commerce, the
what
competition is all in the fixed cost of developing
the really good AI “engine” and then
whoever develops the best one can then outcompete and capture the whole market.
And then the size of the market really only depends on if there’s cultural or consumer
heterogeneity.
Part Three Artificial Intelligence
At the same time, we need to demystify AI:
-
Still a black box: we simply don’t know
Limited resources: big data and computing capabilities, training is difficult
Human brain does NOT need these
Not a machine version of human intelligence: in some way can be seen as statistical logic plus,
based on big data and modern computing power
what
Part Three Artificial Intelligence
Machine Learning tends to work well when:
1. Learning a “simple” function
2. There is lots of data available
Machine Learning tends to work poorly when:
what
1. Learning complex functions from small amounts of data
2. It is asked to perform on new types of data that it learned from
Part Three Artificial Intelligence
What machine learning today
can and cannot do (the barrier is being overcome)
The toy arrived two days late, so I wasn’t able to give it to my niece for her birthday.
Can I return it?
what
“Refund request”
Input text
Refund/ Support/ Shipping
Oh, sorry to hear that.
I hope your niece had a good birthday.
Yes, we can help with….
Source: Andrew Ng, 2019
Part Three Artificial Intelligence
Self-driving car
Cannot do
Can do
what
A
B
hitchhiker
stop
A
10,000
1. Data
2. Need high accuracy
B
bike turn
left signal
10,000
Source: Andrew Ng, 2019
Part Three Artificial Intelligence
what
Part Three Artificial Intelligence
what
Part Three Artificial Intelligence
what
Part Three Artificial Intelligence
what
M. Gong, Y. Li, L. et al. SAR Change
Detection Based on Intensity and Texture
Changes. ISPRS J. Photogramm. Remote
Sens., 2014.
Y. Li, M. Gong, et al. Change-detection Map Learning
using Matching Pursuit. IEEE Trans. Geoscience and
Remote Sensing, 2015.
Part Three Artificial Intelligence
what
Part Three Artificial Intelligence
what
Part Three Artificial Intelligence
Ultimate risks
We are witnessing a swelling public anxiety about the loss of control to an algorithmic force, which seems
to escape our modes of understanding, trust and accountability. Societies are increasingly governed by
human-made technologies as much as by the rule of law. One of the principal concerns at the heart of
governing AI therefore becomes whether we will eventually find ourselves controlled by powerful
technical systems whose design we did not fully understand and whose ramifications we did not
anticipate.
what
In the future, biosensors and algorithms might together capture and analyze an ever more refined record of
our biometrics, vital signs, emotions and behaviors. AI will watch, track and evaluate us: we will go from
the predictive power of one algorithm to the next. We may unwittingly give up to algorithmic networks
unprecedented access to our bodies, genomes and minds and create possibilities for social and bio-control.
Part Three Artificial Intelligence
Today’s concerns
•
•
•
•
•
•
•
Privacy
Accountability
Safety and security
Transparency and explainability
Non-discrimination
Human control
Development responsibility
Bias and Adversarial Attack
what
Part Three Artificial Intelligence
Adversarial attacks on AI
Minor perturbation
Hummingbird
Hammer
what
Minor perturbation
Hare
Desk
Source: Andrew Ng, 2019
Andrew
Ng
Part Three Artificial Intelligence
Example:
Social bias, facial recognition
Joy Buolamwini
https://www.nytimes.com/2018/06/21/opinion/facial-analysis-technology-bias.html
Tech and system design: embedded biases and discriminations
what
- Facial recognition
- Job interviews
- Gender and racial biases
- Public safety
Part Three Artificial Intelligence
“The products of a company called HireVue, which are used by over 600 companies including Nike,
Unilever and even Atlanta Public Schools, allow employers to interview job applicants on camera, using A.I.
to rate videos of each candidate according to verbal and nonverbal cues. The company’s aim is to reduce bias
in hiring…The system’s ratings…reflect the previous preferences of hiring managers. So if more white
males with generally homogeneous mannerisms have been hired in the past, it’s possible that algorithms will
what
be trained to favorably rate predominantly fair-skinned,
male candidates while penalizing women and people
of color who do not exhibit the same verbal and nonverbal cues.”
Part Three Artificial Intelligence
“According to the Center on Privacy and Technology at Georgetown Law, the faces of half of all adults in
the United States — over 117 million people — are currently in face recognition database networks that can
be searched by police departments without warrant. These searches are often reliant on facial recognition
technology that hasn’t been tested for accuracy on different groups of people. This matters because
misidentification can subject innocent people to police scrutiny or erroneous criminal charges.”
“In the case of South Wales, where Big Brother Watch reports that between May 2017 and March 2018 the
what
faces of over 2,400 misidentified innocent people were
stored by the police department without their consent,
the department reported a false-positive facial identification rate of 91 percent. But it’s important to
remember that even if false-positive match rates improve, unfair use of facial recognition technology cannot
be fixed with a software patch. Even accurate facial recognition can be used in disturbing ways. The
Baltimore police department used face recognition technology to identify and arrest people who attended the
2015 protests against police misconduct that followed Freddie Gray’s death in Baltimore.”
Part Three Artificial Intelligence
Leviathan
The fundamental problem of politics, is how do you build this leviathan, that doesn’t abuse its
power. We have been developing institutions for centuries to discipline the leviathan so that it
doesn’t abuse its power, but AI is now providing this dramatically more powerful surveillance tool
and then sort of coercion tool, and that could enable leaders of not only totalitarian regimes to
really reinforce their control over their country.what
It could lead to sort of an authoritarian sliding in countries that are less robustly democratic, and
even countries that are democratic. It will also potentially shift power between different groups.
Part Three Artificial Intelligence
what
Part Three Artificial Intelligence
Regulation and Governance
We’re seeing a lot of work around norm creation and principles of what ethical and safe development of
AI might look like.
An important example is the European GDPR (General Data Protection Regulation) that regulates how
data can be accessed and used and controlled. We’re seeing increasing examples of these kinds of
regulations.
what
Markets do fail and there are profound impacts of new technologies not only on consumer safety, but in
fairness and other ethical concerns. Also more profound impacts, like the possibility that AI will
increase inequality within countries, between people, between countries, between companies.
But regulation can be very problematic. In general, with technology it’s often hard to forecast what the
next generation of technology will look like, and it’s even harder to forecast what the implications will
be for different industries, for society, for political structures. So designing regulation can often fail.
Part Three Artificial Intelligence
We need to think about ways to counterbalance the asymmetry of power between the supposed techleaders and the tech-takers.
We need different communities to work together.
Who are making the efforts?
• Transnational and international bodies
• National initiatives
• Private sector and industries
what
Part Three Artificial Intelligence
Recent examples of Chinese initiatives:
• “Six Principles of AI,” Tencent Institute (April 2017)
• “Beijing AI Principles,” Beijing Academy of Artificial Intelligence (May 2019)
• “Governance Principles for the New Generation of AI,” National Governance Committee for New
what
Generation Artificial Intelligence (June 2019)
• “Artificial Intelligence Industry Code of Conduct,” Artificial Intelligence Industry Alliance (June 2019)
Part Three Artificial Intelligence
Governance falling behind, but also Embedded:
There are people that are conducting AI research, it’s a human endeavor. There are people making
decisions, institutions that are involved that rely upon existing power structures. The processes are
embedded in policy, and there are political and ethical decisions just in the way that we’re choosing to
design and build this technology from the get-go.
One of the things that can help is just improving those communication channels between technologists
what
and policymakers so there isn’t such a wide gulf
between these worlds and these conversations that are
happening and also bringing in social scientists and others to join in on those conversations.
In most cases, though we don’t have concrete policies on the ground yet. But we do have strategies, we
have frameworks for addressing these challenges, mapping what’s happening in that space. The hope is
to encourage transparency and also collaboration between actors, which we think is important.
Part Three Artificial Intelligence
New Actors, polycentric
AI is really being led by a small handful of companies at the moment in terms of the major advances. Not
only the development of technology, but also ethics principles are coming from companies. We will need
some external checks on some of the processes that are happening. There certainly is an important role for
governments and academia and NGOs to get involved and point out those gaps and help kind of hold them
what
accountable.
All in all,
Advances in digital technology are making it possible to collect, store and process ever-expanding
amounts of data. This explosion of data and use of data with help of AI technology holds
tremendous potential to boost innovation, productivity, efficiency and, ultimately, economic growth
and social value. The new technological trend, however, raises many questions:





What do individuals think about the data being gathered about their everyday activities (for
example, through social media and the internet, sensors, radio-frequency identification chips,
geospatial technologies, loyalty cards or transport cards)?
Who should own and control such data?
What is the right trade-off between privacy, property rights and security and allowing society to
benefit from data-driven innovations and better ways of living?
Is the right to be forgotten practicable, useful and meaningful, and does it need to be
complemented with a right to be remembered?
How can society and international community benefit most from big data, artificial intelligence,
etc.?
Part Three Artificial Intelligence
Choose your standards from the list
human autonomy
harm-benefit ratio
fairness
explicability
robustness and safety
explicability
agency and oversight
privacy and data gov
transparency
privacy and data governance
well-being
diversity and non-discrimination
transparency
responsibility
well-being
responsibility
equitability
traceability
reliability
equitability
governability
governability
accessibility
controllability
explainability
human-centricity
what
accountability
explainability
A new paradigm of global governance?
Techs and new actors are making governance process more irreducible, more
polycentric, more connected…
• Emergence: irreducibility
• Non-linearity: networked global community
• Polycentric networks: states and the rest
• Regime complex: material and social authority, complex linkage
……
• Regime complex plus? – intersubjectivity and quantum community
Part Four
Q&A