OUR WORLD - Perspectives on Artificial Intelligence

January 2021
<strong>OUR</strong> WOR LD
<strong>Perspectives</strong> on Artificial Intelligence

A NEW EUROPE PUBLICATION
<strong>OUR</strong> <strong>WORLD</strong>
<strong>Perspectives</strong> on Artificial Intelligence
Foreword by Adina Vălean
European Commissioner for Transport
&
Kyriakos Pierrakakis
Greek Minister of Digital Governance
In partnership with

The Cover
EDITOR & PUBLISHING PARTNER
Jerry Zagoritis
ASSOCIATE EDITOR’S
Nicholas Waller
Ariti-Marina Alamanou
EDITION COORDINATORS
Vassilis Nanis
LAYOUT & DESIGN
Suman Haque
LOGISTICS & OPERATIONS
Irini Panagopoulou
CONTRIBUTORS:
Julia Anderson
Anna-Michelle Asimakopoulou
Adam Bielan
Iban Garcia del Blanco
Sybrand Brekelmans
Angeliki Dedopoulou
Alexandra Geese
Eva Kaili
Hannes Kirchbaumer
Konstantinos Kyranakis
Abraham Liu
Luis Neves
Georgios Petropoulos
Kyriakos Pierrakakis
Dragos Tudorache
Adina Valean
Axel Voss
Cover title: <strong>Perspectives</strong>
Illustration & Concept: Lavrentis Choraitis
Images: iStock by Getty Images & Shutterstock
A publication by:
ISSN number: 2593 - 4163
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Editor’s Letter
A
few months back, while preparing a workshop on gender stereotypes and biases, I came
across research by the World Economic Forum suggesting a significant gender
gap among AI professionals, with only 22% of AI professionals globally being female,
compared to 78% being male. Perhaps I was expecting it, but I still found it not just disturbing;
more than that. I thought it’s dangerous and I’ll explain why right away.
By Jerry Zagoritis
Editor and
Publishing Partner
of Our World,
and the CEO of
Campaign Lab
An algorithm that has been coded by humans, may reflect biases of the developers. If the
data collected and used for the development of AI is gender biased, the results will also be
biased, leading as a consequence to biased decisions in a number of fields, including for
example hiring decisions.
And so I’d like to use this editorial as an opportunity to raise the attention of the business
leaders and policy makers hosted in our pages to this simple fact: to avoid the replication of
stereotypes and biases, and to enhance AI’s efficacy, we must urgently promote more diversity,
including the participation of more women, in the development of AI.
Back to O/W. In the development of this edition we invited authors who share our vision that
our world needs inspiration, passion and boldness for a better tomorrow.
This is why we invited the Transport Commissioner, Adina Valean, and the Greek Minister
for Digital, Kyriakos Pierrakakis, to foreword this special edition. Mr Pierrakakis has inspired
change and has helped his (our) country take unprecedented digital leaps forward, while Ms.
Valean is no stranger to the readers of this magazine - she has repeatedly contributed her
always inspiring thoughts to O/W editions over the years.
The topic of Artificial Intelligence can be divisive by nature; as are most things novel. With that
in mind we are excited to be hosting representatives of the European Parliament’s Special
Committee on Artificial Intelligence in a Digital Age (AIDA) from all political groups, except
one* – including the AIDA Committee Chair and the European Parliament Rapporteur. EU
Policy makers who, next to important business leaders, national policymakers and thinkers,
contribute to what we see as an exciting debate on the present and future of our world.
Happy Holidays and a Happy New Year!
*We made sure to invite Members of European Parliament from all political groups in order to foster
a fair and balanced debate. Unfortunately, we didn’t receive a response from the GUE/NGL (European
United Left) Member of AIDA, although our team called and followed up by email repeatedly.

Foreword
By Kyriakos Pierrakakis
Greek Minister of Digital
Governance
The dream to create an artificial being that could think and act just like a human
was conceived in the myths of ancient Greece and has travelled for more than
2,000 years to the science-fiction literature of modern times. But each passing
day the dream of Artificial Intelligence (AI) gets closer to realisation as technology
advances from all over the world suggest.
Even though we still lack flying cars, according to the visions of 20th century’s futurists,
but we are close to having completely autonomous vehicles that can safely transport
people and goods regardless of the weather conditions. AI technologies are quickly
integrated into medical procedures, manufacturing, banking, trade, art, and numerous
other everyday activities and it is just a matter of time before seeing it expanding further.
When it comes to AI, Europe is at a pole position as it has a strong track record in
research and in development. Currently, one out of four industrial robots is made in
Europe, so we have an advantage towards our global competitors. We, as Europeans,
can further expand our lead doing what we know best – cooperate and address together
the challenges that could emerge in the future.
But with no doubt, AI is a great power and great power entails great responsibilities.
European Commission President Ursula von der Leyen rightly pointed out in her remarks
on Shaping Europe’s Digital Future last February that “Artificial Intelligence must serve
people and therefore, Artificial Intelligence must always comply with people’s rights”.
We, as policy-makers, are called to strike the right balance and ensure that
technological evolution goes hand-in-hand with upholding the rights of people. We
are called to ensure that AI is used to benefit all citizens within the European Union. We
must also ensure that the disruption caused by the evolution of technology, does not
lead to exclusion or enhance inequalities and does not question well-founded values
such as the protection of privacy. We want AI and all other technologies to empower
people, provide inspiration, and serve the needs of our societies. AI poses the challenge
of addressing biases and ensuring that the great achievements of technology benefit
everyone regardless of residence, gender, and personal beliefs. Trust must be built for
people to use AI in their daily activities and this is where policy-makers are called to work
together with engineers and all societal stakeholders and ensure that AI is an inclusive
technology of inclusion that will not leave anyone behind.

Women in Energy, Climate and Sustainability (WECS) is a
public foundation established to promote gender equality
as an enabler of the transition towards a climate neutral
economy in Europe and worldwide.
Our objective is to facilitate gender diversity and women
empowerment in the fields of energy, climate and
sustainability.
info@wecsfoundation.org
wecsfoundation.org

Foreword
By Adina Vălean
European Commissioner for
Transport
W
i ill Artificial Intelligence mean
driverless cars criss-crossing
our towns and cities, with
passengers relaxing in the back seat? It
could mean this, one day in the future. But
what most people don’t realise, is that AI,
while still in its infancy, is already tip-toeing
its way into our transport network – just less
visibly than the arrival of autonomous cars
would be. AI has the potential to transform
our travelling experience, enabling a
smoother and more sustainable journey
for drivers, passengers and freight, and –
crucially – making mobility even safer.
AI is already making a difference. It is
part of a new algorithm used to provide
expected time of arrival for rail freight
trains, and has significantly improved
performance over time through machinelearning.
Accurate arrival time predictions
are important for the seamless integration
of rail freight with road and maritime
intermodal services. The technology was
developed through the EU-supported
ELETA project.
In aviation, applications are being
deployed to automate flight plan
corrections, improve passenger transfers
and predict traffic. In shipping, AI is helping
container terminals and carriers alike to
determine the best sequence for docking
and loading/unloading. It does this by
randomly trying many solutions and
comparing the merits of each solution.
It then starts applying its knowledge to
each new scenario it tests so they get
progressively better.
The transport industry has clearly
seen the potential, and is investing heavily.
Driverless trains, cars able to detect animals
on the road, and vessels able to advise on
the most fuel-efficient way to operate along
a specific shipping route are the focus of
recent trials around Europe. Companies
are investing, testing and manifesting
their interest. For road transport, we are
still far from autonomous cars. But we are
already on a path to cooperative connected
automated mobility (CCAM). Particularly
in talk of cars, the relationship between
AI and autonomy is often misunderstood.
The former does not necessarily mean the
latter. Instead, AI can be used to provide
different levels of support to a car driver:
from advice on the best route to take given
current traffic conditions, to full autonomy.
While AI may appear to be a silver bullet
that solves all of our transport challenges,
it is nothing without data. From traffic
flows to maintenance requirements, data
is the fuel that AI needs to perform. It is in
analysing big data faster than any human
can that AI technologies can enable a
vehicle to understand the world around
it and make the right decision at the right
time. It is AI combined with data that can
cut journey times, congestion, unnecessary
pollution, downtime for maintenance and
ultimately, frustration due to time lost.
The next piece in the puzzle is therefore
sharing this data. Transport authorities,
infrastructure managers, transport
operators, manufacturers, service
providers, platforms, final users and nontransport
stakeholders are already doing
so to improve operations and services,
but this is a sensitive topic for many in the
industry, so we want to develop a common
European mobility data space.
Not all hurdles to AI are technological.
The European Commission’s 2018 AI
strategy raises the need to prepare for
socio-economic change; and to ensure an
appropriate ethical and legal framework.
A Communication on AI’s trustworthiness
followed, designed to increase public
acceptance while providing ethical
guidelines for organisations pursuing AI.
Concerns range from safety and jobs to
privacy and liability, and it is important
that we recognise, consider and investigate
each and every one of these anxieties now.
Technological progress will be worthless if
our citizens are not on board.
Our job in the Commission is to
adapt our regulatory framework so that
it supports innovation, while at the same
time ensuring respect for fundamental
values and rights.
The European Pillar of Social Rights,
which calls for decent working conditions,
social protection and equal opportunities
for all, is the Commission’s first response
to the challenges posed by new forms of
work.
We are also promoting skills
development, as skills dictate employability.
The New Skills Agenda for Europe also
targets smoother transitions and reskilling
for people changing jobs or careers.
To improve understanding of the
current transformation process in the
transport sector, the Commission is also
funding research projects on the effects of
automation on the labour force, working
conditions, and skills requirements. And we
are engaging with stakeholders, including
our social partners. The results of a study
analysing the situation across transport
modes and recommending future steps
are due at the end of this year.
Alongside social concerns are those
from industry: will the current safety
legislative framework work for potential
new safety risks? Will certification processes
need to change to take account of machine
learning? Will some traditional players see
their role change? Will new alliances see
others missing the AI boat?
While I take these concerns seriously,
I do not see them as blocking factors. It
is clear that all stakeholders will need to
collaborate much more than they have
done, and to find their place in new value
chains. Together, we will find the answers
to these questions.
Ultimately, the world is changing.
Today, technology and innovation are
already challenging our habits and dayto-day
lives and I welcome this! But it is
not only technology that is changing. Our
citizens will no longer accept the status
quo. They no longer accept emissions or
congestion. They want change. And so do
I. I want a modern, accessible, safe and
connected transport system that is both
sustainable – through decarbonisation –
and smart – through digitalisation. The will
and momentum for change is there, and
AI will be a driver in making that change
happen.

CONTENTS
88
PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Editor’s Letter...............................................................................5
By Jerry Zagoritis
Editor and Publishing Partner of Our World, and the CEO of Campaign Lab
Foreword......................................................................................7
By Kyriakos Pierrakakis
Greek Minister of Digital Governance
Foreword......................................................................................9
By Adina Vălean
European Commissioner for Transport
AI beyond tomorrow: transforming the state to cope with the
challenges of tomorrow............................................................... 11
By Ioan-Dragoş Tudorache
Member of the European Parliament from Romania (Renew Europe), Chair of the Special
Committee on Artificial Intelligence in a Digital Age
Politicizing the 5G debate is not good for Europe........................... 14
By Abraham Liu
Huawei Chief Representative to the EU Institutions
We need to invest in our own digital sovereignty .......................... 16
By Axel Voss
Member of the European Parliament from Germany (EPP), Member of the Special
Committee on Artificial Intelligence in a Digital Age
Digital Rights in the Age of Super Intelligence................................ 20
By Eva Kaili
Member of the European Parliament from Greece (S&D), Chair of the Panel for the Future of
Science and Technology (STOA), Member of the Committee on Industry, Research and Energy,
Substitute Member of the Special Committee on Artificial Intelligence in a Digital Age
Artificial Intelligence: a geopolitical and economic game changer.... 24
By Adam Bielan
Member of the European Parliament from Poland (ECR), Member of the Special
Committee on Artificial Intelligence in a Digital Age
Is “Open Strategic Autonomy” … “artificial” or “intelligent”?.......... 26
By Anna-Michelle Asimakopoulou
Member of the European Parliament from Greece (EPP), Vice-Chair of the Committee
on International Trade, Member of the Special Committee on Artificial Intelligence in a
Digital Age
An Artificial Intelligence For Human Well-Being............................. 28
By Iban Garcia del Blanco
Member of the European Parliament form Spain (S&D), Vice-Chair of the Committee on
Legal Affairs, Member of the Special Committee on Artificial Intelligence in a Digital Age
AI in the EU: Balancing Benefit & Control....................................... 30
By Angeliki Dedopoulou
Senior Manager for EU Public Affairs with Huawei Technologies
Strong rules for digital opinion-forming marketplaces.................... 32
By Alexandra Geese
Member of the European Parliament from Germany (Greens-European Free Alliance),
Member of the Special Committee on Artificial Intelligence in a Digital Age
Teach AI in schools. Win the tech race in 2030............................... 34
By Konstantinos Kyranakis
Member of the Hellenic Parliament, former President of the Youth of the European
People’s Party
AI and Machine Learning in Digital Campaigning – will bots bring us
closer than ever or will they ruin our system? ............................... 36
By Hannes Kirchbaumer
Founder and CEO of KIRCHBAUMER.COM, His main focus lies on digital marketing
consulting and political campaigning
Artificial intelligence’s great impact on low and
middle-skilled jobs...................................................................... 38
By Sybrand Brekelmans & Georgios Petropoulos
Sybrand Brekelmans: Research assistant at Bruegel.
Georgios Petropoulos: Marie Curie Skłodowska Research Fellow at MIT and Bruegel and
post-doctoral fellow at the MIT Initiative on the Digital Economy
Global cooperation is the only way forward.................................. 40
By Luis Neves
CΕΟ of the Global Enabling Sustainability Initiative (GeSI)
The dynamics of data accumulation.............................................. 42
By Julia Anderson
Research Analyst, Bruegel

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
AI beyond tomorrow: transforming the state
to cope with the challenges of tomorrow
By Ioan-Dragoş Tudorache
Ioan-Dragoş
Tudorache
Member of the
European Parliament
from Romania (Renew
Europe), Chair of the
Special Committee on
Artificial Intelligence in
a Digital Age
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
With the arrival of Ursula von der Leyen
and the “geopolitical Commission”
focused on the twin strategic
priorities of digital and green, the Brussels
bubble has been continuously abuzz with the
trendiest of catchphrases: artificial intelligence,
and its impact on humankind.
Any new technology since the steam
engine has shaped society. In the 18th and
19th century, the industrial revolution ripped
through the established order and transformed
fundamentally how we did things without much
foresight as to consequences. In 2020, we have
a duty to better guide societal evolution instead
of building the digital society out of inertia and
path-dependency. We have a monumental
opportunity to get artificial intelligence right from
the start, and to leverage it to build the society of
tomorrow in a more conscientious fashion. For
this, we need to make the right choices now and
to open up avenues for making the right choices
in the future.
What I feel is missing in the debates in
Brussels and elsewhere, is a vanguard vision
for artificial intelligence -- a vision that projects
us decades into the future and gives us a sense
of direction. Without dismissing the important
conversations around AI that are on the agenda
today, we need to start thinking of the “why” of
tomorrow to complement the “what” and the
“how” of today. We need to start preparing
for successfully dealing with the systemic
transformations AI will bring to our societies, and
nowhere is this more pressing than in the way
the State and its institutions function. That, to
my mind, is a global conversation we should be
having, and the EU can lead the way in planning
for the transformation of the State in line with
the AI revolution, and through this, become a
truly geopolitical superpower as it aspires to be.
My premise is simple: the modern state,
as shaped after the Peace of Westphalia,
risks becoming the most helpless and likely
future victim of artificial intelligence. With a
slow, ossified, often inefficient and ineffective
bureaucracy and administration, the state has
but a single advantage in the face of all other
structures that organize human activity: its size
and its monopolies on providing security and --
to varying extents -- social services, as well as
on writing and enforcing rules for how society
and economy work. But its monopoly on power,
which allowed it to withstand massive shocks
throughout history (from wars to large-scale rise
of disruptive technologies) can make things turn
terribly wrong when it comes to AI, and not only
in the EU but anywhere else.
Artificial intelligence, alongside the great
opportunities it brings for humans, comes
loaded with the means for the demise or the
perversion of the state. Suprastatal structures,
such as the EU, are just one step removed from
the direct threat states face, but the danger they
face is of the same nature.
A challenge from market forces
The first threat to the state’s existence comes
from market forces -- a challenge to its economic
viability. As data roams (almost) freely in the
hands of private companies, and as AI algorithms
are being developed around the world and
are improving efficiency in every sector of the
economy imaginable, challenges to the state’s
already inefficient delivery of core services are
increasing. When AI-powered private health
becomes cheaper and more effective than state
provided healthcare, how will the state justify its
expenditure on health? When private AI-powered
education -- personalized, cutting edge, datadriven,
digital-age-ready -- becomes the norm,
how will the state compete? When, ultimately,
private AI-assisted security makes citizens feel
safe and protected, how will the state justify its
existence? When all services the state provides
will be better provided by an AI-powered private
sector, what will give the state the authority to
make and enforce laws and rules? And when
private companies begin reinventing rules
and providing alternative systems of behavior
“governance” that become more important to
citizens than the rules of the state, how will the
state maintain its monopoly on enforcement,
11

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
arbitration, and the dispensation of justice?
Before the rise of artificial intelligence and the data economy,
citizens entrusted the state with exclusive competences because
the state was the only one large enough to provide certain
services. The state was the only institution large enough to collect
taxes, it was the only institution large enough to administrate
natural monopolies, and it was the only institution large enough to
maintain a standing army to counter other states’ standing armies.
Enter data, the new fuel -- or even future currency -- of the
digital economy. And on this data, specifically because of the
state’s inherent vulnerability to perversion from collecting data
and building AI, the private sector has a cvasi-monopoly. The
tables are turned: private companies are now increasingly more
effective at providing what the state was historically entrusted
to provide. And while there is yet no company that can match
a modern state (even if because the state still holds a citizensentrusted
monopoly on making and enforcing rules), the trend
is here, at our doorstep. As more and more of human activity
becomes data-driven, data-fueled, and data-dependent, how can
the state adapt, so that it can compete and survive?
The threat of state perversion
The second threat AI brings to the very existence of the state
comes from the opposite direction: it offers the right set of
tools for the state to acquire incredible power over its citizens.
The more the state collects, stores, and processes increasingly
sophisticated and detailed data on their citizens with the help of
increasingly sophisticated AI, the more the institutional setup that
ensures its democratic -- or, arguably, any type of -- functioning
becomes prone to unraveling. Massive advances in one functional
area -- such as, for example, the ability to predict every single
citizens’ potential criminal behavior or, oppositely, to identify and
reward every citizen’s law-abiding behavior -- shift the institutional
balance of power and alter the power competition between
institutions. Resource allocation between institutions gradually
shifts to institutions that are more effective in providing for the
“wellbeing” of the citizens and, in time, the state transforms into
a giant population control machine, continuously and more and
more intrusively optimizing the lives of its citizens. This is but one
scenario, which does not even need a totalitarian state to come
to pass. Nor does the state -- or its leaders -- need to consciously
choose to “control” citizens. Once the state becomes capable
to profile every single one of its “customers” -- behaviorally,
psychologically, politically, and in a myriad other ways -- there is no
turning back. The technology allows it, for the first time in human
history. And, incidentally, the state is since times immemorial
the default repository of the personal data of its citizens. So the
temptation is there.
By 2030, the EU must define a new rulebook, fit for the digital
age, by which the state needs to play in dealing with citizens’
data and in deploying AI. Brussels is rightfully worried by facial
recognition technology, predictive policing, social scoring, and
other blatantly dangerous technologies that could be misused
by public authorities, especially when these technologies combine
with potential biases and discrimination. But the road to Big
Brother needs not be a straight line. We need to think carefully
and to anticipate any possible negative outcomes from using
citizens’ personal data and placing powerful AI in the hands of
state authorities, even in the most benign use cases. For this,
the state itself needs to be transformed, so that it is resilient to
perversion even if endowed with dangerous technological tools.
We need to reinvent the checks-and-balances and rewrite the
social contract for the digital age so that the state survives and
continues to serve its purpose.
Fake news and disinformation
The third challenge to the state brought about by artificial
intelligence comes from without -- the direct attack on truth brought
about by AI-powered disinformation and fake-news. Once truth
unravels social order unravels as well, and the state is threatened.
While propaganda, disinformation, mass manipulation, and fake
news are all as ancient as human civilization, AI acts as a catalyst
that amplifies these threats beyond the containment threshold.
This is an existential challenge, first and foremost, to democratic
systems, because democracy is fueled by truth: citizens need to
know the truth in order to vote, and the vote is the fundamental
building block of democratic systems. The democratic state is
bound to unravel once third parties (whether states or non-state
actors, international or domestic) can sway the results of elections
through powerful AI-enabled tools. We are not yet there, but the
progression is also not linear: once such mass manipulation tools
become powerful enough that they are beyond containment, the
failure of the democratic state will be abrupt, not incremental.
Social order is relevant for the stability of all societies, not just
democracies. AI-powered fake news and disinformation can also
bring to their knees non-democratic states, including those very
states who are now building and using such tools to advance
their international agenda. And, in the future, authoritarian states
might be even more vulnerable to this threat than democracies. As
democratic societies develop antibodies to deal with such direct
attacks on their democratic functioning, authoritarian states are
more likely to deploy mass-manipulation tools internally in order
to control their citizens and to project a preferred version of reality.
But when the proliferation of powerful disinformation AI will make
it accessible to smaller actors, including domestic challengers,
maintaining a fabricated version of the truth and continuously
fine-tuning it will become increasingly difficult.
The stability of the state, democratic or not, depends on
truth or, at the very least, on the stability of the local “truth”. The
progress of society, including the digital transformation, must be
fueled by truth, because scientific progress can only happen when
facts remain unchanged. And the rules-based international order
is fueled by truth, because rules and decisions in international fora
rely on facts and the truth. AI-powered tools that can manipulate
populations on a large scale and change the truth to influence
mass-scale outcomes are a threat to all states and a threat to the
future global stability.
12 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Transforming the state to
withstand the challenges of AI
The answer to all three challenges -- the
increasing competition from private
companies in providing fundamental
services, the state’s vulnerability to
perversion from within if misusing
technology, and the challenge to social
order posed by AI-powered mass
manipulation tools -- is in the reinvention
of the state as the safest and most trusted
digital platform for its citizens. But how?
First, the state needs to be the first
to provide citizens a secure, trusted,
and inalienable mechanism to own and
use their personal data, including in
the interaction with the state itself. The
Commission’s “secure European e-identity”,
foreshadowed by Ursula von der Leyen in
her State of the Union address, is a step
in the right direction, but it needs a boost
in magnitude and ambition. Looking at
2030, the EU must provide its states and
therefore its citizens with a solution to not
just the ownership and the use of their
personal data, but to its “monetization”.
If people are given a mechanism through
which they can stand to gain quantifiable
state-backed benefits from their data,
perhaps to the same extent to which the
state guarantees currency as a carrier of
economic value, market forces will prop-up
the state in the uneven competition with
the private sector. The Googles and the
Amazons of the world will have to adapt
to increasing demands for value from the
data they use, and the state will have a
newfound role in protecting its citizens’
rights.
Second, the state needs to transform
itself so as to become a de facto (and
powerful) standard-setter in the way
personal data is used. The state needs
to become itself a data-agnostic digital
platform, in which citizens plug their data
to access services or to derive economic
benefits. The state needs to become
the selfless and trusted repository of
its citizens data, just like a bank or a
safehouse. Rules must be rewritten and
the functioning of the states’ institutions
must be rebalanced so as to guarantee
citizens the inviolability of their data. And,
just like the current state pays workers for
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
their time and skill through direct hires and
public tenders, so should it set the tone for
the digital future by offering citizens direct
economic value in those instances when
it does need to use, temporarily and for
limited and clearly-defined purposes, their
personal data.
Third, the state needs to open itself
up for everybody to benefit -- citizens
and industry alike -- thus setting both
transparency and information standards.
By opening the increasingly massive
amounts of industrial data it generates,
the state can be a standard-setter and
a catalyst for the data-driven industrial
transformation, fueling innovation and
progress. Allowing industry to benefit
from public data and from co-creating
data with public authorities, encouraging
and facilitating the sharing and common
use of industrial data, and relying on the
natural monopoly on certain types of data
to set standards, the state can fuel growth
and also become a trusted platform for its
economic actors, not just for its citizens.
And by opening up its data and acting as
a neutral actor in an ecosystem fueled by
SHUTTERSTOCK
data and information, the state can also
guarantee a consistent and empiricsdriven
version of “the truth” that is harder
to undermine, boosting its own immunity
to disinformation and manipulation
attacks from without.
Given the economies of scale and
interoperability requirements for such
transformative changes to the very
foundations of the state, the European
Union has a crucial role to play here. The
traditional “state” will become increasingly
insignificant in a digital economy where
data flows at incredible speeds all across
the globe. No one state can manage such
transformation on its own, and no one
state would benefit from it, should it go at
it alone. While according to the principle of
subsidiarity member states will ultimately
be responsible for many of the genetic
changes the digital age requires of them
to ensure their survival, the Union is in the
perfect position to provide the ultimate
transformation handbook. By 2030, we
need to have the handbook ready -- and
with it, a new set of rules for the role of the
Union in the digital transformation.
13

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
ISTOCK
Politicizing the 5G debate
is not good for Europe
By Abraham Liu
It is often said “may you live in
interesting times”.
These last few months and
particularly these last few weeks and
days have certainly been interesting
times.
It is now clear that, after shall we say,
a slightly unusual American election,
there will be a new US Administration in
January.
It’s still too early to tell what the
policies of President Elect Biden and
Vice President Elect Harris will be, but
by the team they are assembling around
them, it looks like we may be returning
to a world of multilateral cooperation
and engagement, which from a business
leader’s point of view, is encouraging.
We have only to look at this year’s
experiences to see the fruits of such
multilateralism. With the last decade
of global collaboration in the ICT
sector, the quality of the networks has
enabled important aspects of our lives
to continue; like teleworking, remote
medicine, home education and business
continuity. Specifically, the ability to
make high-quality video conference
calls is a direct result of global efforts
and cooperation to develop a single 4G
14 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
It looks like we may be returning to a world
of multilateral cooperation and engagement,
which from a business leader’s point of view,
is encouraging.
Abraham Liu
Huawei Chief
Representative to the
EU Institutions
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
network standard – before the outgoing US
administration came to power.
During the last administration’s term
though, we have seen attempts by the US
to threaten others through its technological
dominance. This has had the effect of
encouraging Europeans to talk about their
digital sovereignty, and the resilience of
their networks. Did you know for example
that the overwhelming amount of your data
storage, cloud services, semiconductors
and technological infrastructure is from
the United States? Of the top ten cloud
providers in the EU, only 3 can truly be
classed European in origin. Huawei on the
other hand, does not deal in your data, or
have access to it, nor do we monetize it. We
are not even compelled to collect data if
ordered to - I am assured that the relevant
Chinese law does not have extraterritorial
application – unlike, ironically, the US
“Cloud Act”. We can actually help Europe
with its digital sovereignty and strategic
autonomy ambitions.
But I believe this talk of where a tech
company comes from, promoted by
the current US administration, is at best
missing the point, and at worst a form
of “corporate racism”. This doesn’t do
anyone any good – least of all European
consumers. Huawei was born in China, but
its technology is not fully Chinese – just like
any European or US tech company the
technology is multinational.
Apple products have European and
Chinese components. Nokia products
have Chinese and US parts. Huawei kit
has European and US elements. The
notion that you have to choose between
Chinese and US or European is false – you
are already choosing Chinese in everything
you buy. Indeed, while trying to exclude
Huawei from 5G networks on the basis that
we are Chinese, both the US and Europe
are increasing their trade with China
and Chinese companies, and European
companies are expanding their markets
inside China as well.
It is as a direct result of this connected
and interdependent technology supply
chain that many European and American
companies are also being damaged by the
policies of the current US administration.
I am hopeful that this message will
resonate with new lawmakers in the US,
and their counterparts here in Europe,
because there are so many benefits that
the new technological developments in
AI, quantum and cloud computing can
bring - not least to Europe’s environmental
protection goals as spelled out in its Green
Deal, and its recovery plans. A 5G network
without Huawei is a more expensive, less
technologically advanced network.
It’s probably too optimistic to conclude
that a new US administration will overturn
or change the most restrictive policies
towards Huawei in the short term – or
even in the medium term. But while we
are planning for the worst, we are hoping
for the best.
For me, it would be enough that in
the coming weeks and months, we here
in Europe, and in Huawei, live through a
period of greater communication, more
collaboration, and increased cooperation
across countries and continents. That we
live in slightly less interesting times!
15

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
We need to invest in our
own digital sovereignty
Axel Voss
Member of the
European Parliament
from Germany (EPP),
Member of the Special
Committee on Artificial
Intelligence in a Digital
Age
By Axel Voss
digital transformation
continues to have a strong
“The
impact on our daily life and
has led to radical changes in almost
every part of Europe’s economy and
society. Although the EU Digital Single
Market strategy of 2014 was a first
important step to make the European
Union future-proof for the digital era,
I believe that many relevant policy
questions remain unanswered.”
This is the beginning of my own
digital manifesto published in January,
calling on the European Commission
to quickly draw up its plans to further
develop the Digital Single Market.
Almost a year later, my statement still
stands. The strategies that were built
up were very important first steps - but
if we do not follow up on it by heavily
investing in our own digital sovereignty,
we will end up as a digital colony of the
US or China, uncompetitive and without
a voice of our own.
Currently, none of the top 15 digital
companies is European. There is no
significant European operating system,
browser, social media network or search
engine. The investment gap compared
to the USA and China is estimated at
€190 billion per year. Our response to
this challenge has been a wide range
of incoherent, fragmented interim
solutions following lengthy decisionmaking
processes. It seems that we
need to jump on the moving train, yet
we are still standing at the platform
discussing which seat to take. Not only
will this approach prevent us from ever
catching up with a rapidly changing
technological environment, it may also
give our citizens the impression that the
European political class has lost control
– a perception that could ultimately
result in a significant loss of trust in our
democratic system.
Hence, we need to go beyond first
step measures. We need to invest
heavily in our digital infrastructure and
technology “made in the EU”. Although
the Commission is already promising
16 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
investments, what we need is a mobilisation of private and
public investments at the scale of the European Green Deal.
In fact, a transformation of our digital infrastructure will also
strengthen our sustainability and thus both major initiatives
should go hand in hand.
We also need to invest in digital competence and
education. Right now, a graduate in data science who wants
We need to invest heavily in
our digital infrastructure and
technology “made in the EU”.
Although the Commission is
already promising investments,
what we need is a mobilisation
of private and public
investments at the scale of
the European Green Deal. In
fact, a transformation of our
digital infrastructure will also
strengthen our sustainability
and thus both major initiatives
should go hand in hand.
SHUTTERSTOCK
to develop new technologies will move to the Silicon Valley.
The EU has to become an attractive location for innovation
if it wants to remain a large competitive economy. And
thirdly, we need to invest in start-ups and digital companies
to scale up. Only 12 big new European companies were able
to scale up since 1951, compared to 51 in the US. Thus, we
need to provide easy access to venture capital and ease the
administrative burden on SMEs and start-ups.
To achieve this, I would like to see clear prioritisation. We
need to have a clear picture about our areas of strength that
we should reinforce, our critical deficiencies that we should
overcome and the upcoming disruptive technologies in which
strong investments make sense. That way, we can prioritise
in which technologies we want to be leading competitors,
whether its cloud computing, automated cars or digital
finance, and start heavily investing in these sectors as flagship
areas. Our objective should be to become less dependent on
non-European technologies and services, while establishing
sound ethical, technological and security standards.
To put such measures in place, we need to change the way
political processes and governmental systems work by making
legislative procedures more effective. To achieve a truly
harmonised Digital Single Market, we need EU regulations
instead of directives with constant impact assessment. It took
us ten years from the first consultations for GDPR to its actual
implementation. We cannot afford these delays in a race with
competitors like China.
While I commend the proposals made by the European
Commission, the clock is ticking. The scenario of becoming a
digital colony will put our competitiveness at risk for the long
term. If we do not have a say in shaping the next technological
stage, actors who do not share our core values and standards
will dominate the process, with troubling consequences for
our privacy, security and our prosperity. Our goal should be
to strive for a European way of digitalisation in a Digital Single
Market 2.0: human-centred, value-oriented and based on the
social market economy, without becoming protectionist.
New digital innovation is not possible without international
cooperation and free flow of data. However, cooperation
needs to be built on trust and respect of our values.
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
17

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PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Digital Rights in the Age
of Super Intelligence
By Eva Kaili
Eva Kaili
Member of the
European Parliament
from Greece (S&D),
Chair of the Panel for
the Future of Science
and Technology
(STOA), Member of the
Committee on Industry,
Research and Energy,
Substitute Member of
the Special Committee
on Artificial Intelligence
in a Digital Age
In our transforming world, digital technology
has the critical mass to push our frontiers and
release unlimited potential. As the wave of digital
transformation soars high, improving our lives,
industries and economies, we must not overlook
the risks that technologies and innovations pose
on the fairness and cohesion of our societies, and
our rights as European citizens.
Through the construction of vast digital
infrastructures, which track, monitor and collect
personal data at scale, major online platforms
are commodifying our data. An entire ecosystem
of apps and companies controls huge data flows
and ‘treats’ technology users as data suppliers via
the imposition of default choice architectures.
The current form of ‘computational governance’
in which we are ruled by algorithms and predictive
analytics which make use and trade of individual
human experiences to produce predictions
of human behaviour has largely remained
unregulated. We can still be in control of safe AI,
trustworthy and complimentary to humans.
AI is at the core of this wave of digitisation;
the power it carries must be tamed before it is
unleashed in its next form of super intelligence.
Manifested in both software and hardware
with intelligence expressed in lines of code and
powerful automation, it is already being used to
solve complex problems, discover patterns or
predict traffic, and make suggestions to users. AI
is also tested for completely autonomous vehicles,
as deep machine learning could make decisions
faster, safer and better than people. Progress
remains incremental, however the more we
advance our understanding and the further we
develop this technology, the closer we reach the
point where AI leaps from narrow to general,
and then super intelligence. Narrow AI is already
making simple suggestions and decisions at the
service of people; however, the next level of AI
sophistication can completely omit humans from
the process and reach levels that humans will not
be capable to understand or control anymore.
As most of human activity and industry has
moved online during the pandemic, and we
become more dependent on an increasing amount
of digital services, a series of critical questions
arise: How can we build data infrastructures that
recognise the role of data as a public good? Can
we foresee an impartial system of data governance
that could reconcile the astonishing potential of
these technologies with their significant human
downsides? Do we as citizens know that when we
use a search engine to find something online, the
engine is learning by searching us as well? Does the
collection and processing of behavioural data by
these technology platforms threaten to dismantle
democracy and undermine the rule of law?
To tackle these questions we need to come up
with novel, innovative responses, so that we make
our future better than the grim outlook predicted in
Black Mirror, the popular Netflix series. We have to
be aware and cautious in designing the appropriate
framework for AI; intelligent systems are used on
the pandemic front for rapid COVID diagnosis
and prognosis based on AI-enabled CT scanning
analysis. AI systems are used now to upgrade our
mapping systems, the management of resources,
to make our mobility smarter, our agriculture more
precise, with predictive maintenance and smart
sensors that can contribute to a greener future,
saving time, energy, and resources.
As the impact of exponential technologies
grows, a new framework is needed in Europe to
harness its benefits and mitigate its risks. This
framework must be built on ethical principles and
binding standards, which elevate people’s trust in
AI and ensure that in the digital age, people co-exist
with intelligent systems without fearing exclusion,
manipulation, oppression or discrimination.
20 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
SHUTTERSTOCK
Retaining freedom of choice in a humancentric
AI that would prevent brain
computer interfaces challenging the nature
and future of humanity. In contrast to the
trends of the Fourth Industrial Revolution
towards inequalities and dehumanization,
technology and innovation best practices
need now to be bent back towards the
service of humanity, and Europe could
lead as a global rules and standards setter
for the Fifth Industrial Revolution.
The European principle-based
framework for AI systems must translate
and establish by law, respect to our rights
in the digital age. Progress has been made
with landmark initiatives such as the GDPR,
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
however more work is needed in this space
to establish legally binding rules which
provide developers and innovators with
more legal clarity, and award individuals
stronger protections of their rights and
freedoms. At the foundational level,
this framework must guarantee higher
transparency and accountability, define
the liability of AI systems, establishing
standards to trace, audit, explain, appeal
and reverse decisions made by AI during
its entire lifecycle.
The rapid development of automation
in Europe must not reflect mistakes of
the past; AI algorithms and systems must
be trained on diversified sets, and their
objectives have to be clearly defined
and controlled to avoid risks of bias and
discrimination or data poisoning. Decisions
made by AI must be aligned with the
collective ethical fabric that defines Europe
throughout the lifecycle of intelligent
systems and follow clear red lines, such
as the risk-based approach that would,
for example, completely ban research in
autonomous lethal weapons or conscious
AI. The high-risk applications should alert
us of the fragility of our common values,
as well as our rights as citizens, our
responsibilities as policy-makers and our
obligations to future generations.
21

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Strong protections can enable the development of
ethical AI in Europe, and force gatekeepers to respect with
reciprocity the new architecture of data governance. The
data ecosystem is ground-zero for AI innovation in Europe.
Data is the source of learning for AI systems and therefore
very important for AI to become smarter. Privacy by design,
consumer protection, and product safety will be at the top of
our agenda since we will not compromise privacy for safety.
More options available to users, clear consent, and rewards
for the use of our data, with stronger security standards, as in
cases of extreme personalization and micro targeting, would
enable fairer AI in this evolving ecosystem, and inspire trust
for European citizens. The EU has expertise in leading efforts
to establish global norms and standards for data processing,
which it can leverage in collaboration with international
partners to achieve global consensus. In the European
Parliament, we are currently working on an ambitious
agenda to enable better, rules-based data collection and
safer mechanisms to process, store, exchange, and transfer
data. Standardisation is necessary to ensure the high quality
and safety of data, alongside the establishment of data
spaces governed by rules addressing the divergent levels of
inherent risk of different sectors and technologies. Coupling
these efforts with a secure digital ID can be instrumental in
rethinking the current data ownership models.
In the EU, a significant portion of the budget and other
funding instruments is earmarked for the development of
digital skills, and digital education systems fit for purpose,
as we must prepare the grounds for a seamless transition
of our societies and economies into an era powered by
intelligent systems. At both the national and the EU levels,
progress is necessary to calibrate education and professional
training with the needs for digital skills, to enable greater
understanding of AI across generations, and stimulate career
paths into future jobs of AI innovation and research.
Our vision is to foster technology that creates value for
all, and enables our society to progress without impeding
on the rights of Europeans. Striking this sensitive balance is
a complicated endeavour, however the acceleration of AI
development, and the reach of its impact, implore us to get it
right from the outset. The EU is a global champion of privacy
and data protection in the digital age. This achievement
must be leveraged in establishing a safe ecosystem, where
trustworthy AI enables us to achieve our objectives with
regulatory control, and establish a fair and equal society fit
for the future.
The European principle-based
framework for AI systems must
translate and establish by law,
respect to our rights in the digital
age. Progress has been made
with landmark initiatives such as
the GDPR, however more work is
needed in this space to establish
legally binding rules which provide
developers and innovators with more
legal clarity, and award individuals
stronger protections of their rights
and freedoms.
22 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Artificial Intelligence: a geopolitical
and economic game changer
Adam Bielan
Member of the
European Parliament
from Poland (ECR),
Member of the Special
Committee on Artificial
Intelligence in a Digital
Age
By Adam Bielan
Artificial intelligence (AI) has been
developing and broadening
rapidly. It has increasingly
become over the last years one of the
pillars of digital transformation. AI’s
influence now entails numerous sectors
such as health, education, transport,
cybersecurity, or research. AI is not only
about our economies, scaling-up effects
or efficiency gains. As we are currently
witnessing an escalating global race for
digital leadership, it has turned into a
geopolitical subject with increasingly
high stakes.
While lagging behind the tech
giants, notably the American and
Chinese digital empires, the EU now
seeks to take part in the formulation
of an international AI approach and
of its principles. In this context, the
EU is highlighting the importance of
strategic partnerships with key actors
such as China, Japan and the USA, and
stresses the significance of cooperating
with regional organizations like the
OECD and the Council of Europe. The
EU Member States can indeed act as a
balancing power between two visions
for future AI developments. Critical
ethical questions are on the table and
an EU definition of AI directions thus can
become an alternative to data models
driven by state security and control
interests like in China, and strongly
influenced by the GAFAM interests
and other large Internet platforms and
industries like in the US. The safeguard
of privacy and human rights is indeed
one of the core elements of Member
States’ and the EU’s digital policy to the
benefit of citizens.
The European Union (EU), through
new programs, increased funds and
a new agenda on the matter, wishes
to better address AI challenges and
implement a new strategy vis à vis the
24 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
opportunities Artificial Intelligence represents. Indeed, the
recent years have seen the end of inaction and the EU is
now catching up with the ‘big giants’. Positive developments
include the White Paper on Artificial Intelligence published by
the Commission and the set-up of a new Special Committee
on Artificial Intelligence in a Digital Age (AIDA) by the European
Parliament. The latter recently adopted reports on ethical
aspects, civil liability regime and intellectual property rights.
SHUTTERSTOCK
I believe this is the right direction for citizens of the European
Union.
On top of this, the Covid-19 pandemic has underlined the
importance for EU Member States and the European Union
in general to boost its investments in several areas in order
to remain an epicenter of the geopolitical chessboard and a
place for innovation and economic growth.
Concerning economic perspectives, some progress has
undeniably been made. Nevertheless, we still need to focus
on skills, digital literacy, job creation and to address job
losses and people left behind. Digital inequalities are a more
important than ever.
Small and Medium Enterprises (SMEs) are the backbone of
the European economy. They are the most challenged when
new policies come into force and are a spring for innovation
and growth. The EU Member States and institutions have kept
this in mind, with plans to accompany SMEs to adapt their
procedures, their business models and their ways of working
using AI.However, much remains to be done in order to make
this shared vision a reality. Most notably, the necessity to
have a clear and comprehensive framework is evident. A
potentially complex regulatory framework will be much
harder to digest for European SMEs, which lack extensive law
departments compared to global players. Therefore, further
actions are needed to bridge that gap. Digital Innovation Hubs
and Regulatory Sandboxes can represent in that regard a
positive development, and administrative burden reduction
policies should be a priority on our agenda.
Also, I believe it is necessary to provide start-ups and SMEs
access with the widest possible range of data and algorithm
libraries as the lack of such a possibility is slowing down their
development, which may eventually not be fast enough to
challenge their international competitors. Available data,
together with its usability, accessibility and interoperability,
is essential for a « culture of data » to grow in the European
Union. The fragmentation of European data has long
prevented access to available data, with each Member State
tending to work on its own national data plan. This contrasts
with China, whose population and country size have early on
encouraged cloud and data storage practices.
In addition to SMEs, Artificial Intelligence could also better
connect and mutually benefit the public sector. Strengthening
collaboration between the public sector and the small and
medium enterprises can take many forms. I have for instance
proposed a few months ago a pilot project with fellow
MEPs establishing the « EU GovTech platform », designed
to bring the best out of well-functioning national GovTech
frameworks to the benefit of the entire single market. These
frameworks enable the aggregation of EU and national public
institutions’ demand for modern technologies. This means
increasing access of EU’s innovative companies to the public
procurement market.
We, as European lawmakers, will require strenuous efforts
to deliver innovation-friendly and future-proof Single Market
legislation that remains both understandable for citizens
and flexible for businesses. This is also our priority in AIDA
committee. The environment in which we find ourselves
represents an opportunity for lawmakers and national
leaders to prove how serious we are in supporting SMEs.
This is not only an economic matter, defining the directions
we want to lead our economies. These questions are also
intrinsically linked to a global scene in which the EU is trying to
make a stand. That is why we need to step up our efforts, as
AI, its framework and its development in the coming years will
undeniably play a part in defining tomorrow’s global order.
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
25

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Is “Open Strategic Autonomy”
… “artificial” or “intelligent”?
By Anna-Michelle Asimakopoulou
Anna-Michelle
Asimakopoulou
Member of the
European Parliament
from Greece (EPP), Vice-
Chair of the Committee
on International Trade,
Member of the Special
Committee on Artificial
Intelligence in a Digital
Age
Paving the digital path
for Europe should
involve a human centric,
transparent, trustworthy
and inclusive artificial
intelligence strategy. One
that protects citizens and
businesses in Europe and
fosters innovation
and competitiveness.
At the 2020 State of the
Union address, European
Commission President Ursula
von der Leyen has unveiled her
ambitious goal to make this decade
“Europe’s Digital Decade”.
The debate over digital
developments is becoming more
and more lively in Europe, and the
acceleration of the pace of digitization
as a result of the COVID-19 pandemic
is shaping the “new normal”. The
urgent need to analyze vast amounts
of health data to understand and
control the virus has prompted us
to invest more heavily in artificial
intelligence and data management.
The pandemic has also brought
to light Europe’s overdependence in
several critical and strategic sectors.
Thus, the EU institutions were “forced”
to revisit Europe’s industrial strategy
in order to offer greater resilience
and diversification of supply chains in
strategic areas such as health supplies
and medicine. As a result, Europe is
today, more committed to becoming
“strategically autonomous”.
Artificial Intelligence is at the
forefront of the digital power race.
The debate over Αrtificial Ιntelligence
encompasses technological, ethical,
legal, socio-economic as well as
geopolitical aspects. The OECD, the
Council of Europe, the EU, the US,
China and many other countries have
developed their own strategies and
guidelines on Artificial Intelligence.
In addition to the many standing
committees dealing with AI in the
European Parliament, the Special
Committee on Artificial Intelligence in
the Digital Age (AIDA) was created to
take a horizontal approach in setting
a long-term roadmap for AI in Europe.
Europe is working hard to be
at the forefront of the technology
revolution and to ensure the
competitiveness of its technology
businesses. It strives to find the right
balance, which promotes innovation
and competitiveness, builds trust
and protects our rights, values and
principles. Europe aims to provide
a trustworthy digital environment to
our citizens and businesses to shape
the conditions for the development
and use of AI. This is one of the main
objectives of the European Strategy
for Data and the Regulation on Data
Governance recently proposed by the
European Commission, building upon
existing regulations such as personal
data protection (GDPR), consumer
protection and competition rules.
Meanwhile, China is investing
billions in AI, 5G, quantum computing
and chip fabrication, as part of a
strategy, launched in July 2017, to
establish itself as the world’s AI
leader. By the year 2030, it expects
to have a domestic AI industry worth
at least $150 billion. It steals IPR, tries
to dominate international standard
setting bodies and lures countries
into its techno orbit through the
“digital silk road” initiative. China has
1,4 bn people to produce data, and
its’ own robust technology giants
which it subsidizes with state aid
and uses their products in the Party’s
surveillance state.
This potential of new
“dependencies” namely on holders
26 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
ISTOCK
of certain critical technologies who use
them in an “autocratic” manner, as well
as on those controlling large volumes
of data is why, Europe is striving to
create its’ own “open yet sovereign
Single Market for Data” which is crucial
for AI development. Therefore, AI can
be considered as a “key” to achieving
European “digital sovereignty” as part of
the overall strategy of “Open Strategic
Autonomy.” As Commission Vice-
President Maros Sefcovic emphasized
“we can make Europe more resilient by
boosting our open strategic autonomy
and building a fairer, climate-neutral and
digitally sovereign future.”
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
There is much discussion about
what the meaning of “Open Strategic
Autonomy” actually is. Is it legitimate
protection or undue and covert
“protectionism?” “Autonomy”, or
“αυτονομία” in Greek, comes from the
words αὐτός (autós, “self”) and νόμος
(nómos, “law”). It literally means being
free to make your own rules. Actually, It
is your right to be able to make your own
rules. Making these rules in Europe has
produced our commonly shared values:
democracy, the rule of law, and respect
for human rights.
Paving the digital path for Europe
should involve a human centric,
transparent, trustworthy and inclusive
artificial intelligence strategy. One
that protects citizens and businesses
in Europe and fosters innovation
and competitiveness. Becoming
“protectionist” would not only be
“artificial” it would also be untenable.
A digitally sovereign or strategically
autonomous Europe, must be open
to the world. It must create practical
alliances with like-minded partners to
set international standards, and create
rules based on commonly shared
values. This is not only the “intelligent”
choice, it is also the only democratic
one.
27

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
An Artificial Intelligence
For Human Well-Being
By Iban Garcia del Blanco
SHUTTERSTOCK
The massive rollout of artificial
intelligence (AI) entails a
technological leap that will cause
(that is already causing) very substantial
changes in the labor market, in the
relationship with public authorities, in
personal relationships and even in our
own domestic life.
Any technological progress carries
benefits and risks. When Dworkin spoke
of science, he highlighted its ambivalence
both as a promise and as a threat. When
we talk about artificial intelligence, we talk
about benefits and/or risks on a scale not
previously known, taking into account its
intrinsic power. Due to its characteristics
and its potential, this technology will take
us through dizzying changes that would
otherwise take generations. Fortunately,
AI provides us with powerful tools to
better address the major challenges of
our time: the fight against climate change,
the fight against depopulation; and even
to anticipate future pandemics such
as COVID19, which has put up against
the ropes our resilience as societies,
and that we can now better address by
accelerating research on drugs, vaccines,
and developing state-of-the-art tracking
applications.
As the regulatory superpower it claims
to be, Europe must lead the way globally.
I champion a broad pact between the
public and private sectors, each within
the scope of their competences, that
enables the unimpeded promotion of
technological development on the one
hand, and an ecosystem of trust for the
European citizens’ peace of mind on the
other. The ultimate goal of AI can only be
the improvement our societies and our
citizens’ lives.
Early this year, the European
Commission published a White Paper on
artificial intelligence together with a digital
strategy –and by the beginning of 2021,
it intends to release an all-encompassing
regulation on the phenomenon. In this
regard, the European Parliament is one
step ahead: I had myself the honor of
being the rapporteur for the first European
legislative initiative on the “Ethical aspects
of artificial Intelligence, robotics and related
technologies”, approved this past October
with an extraordinary backing by the
plenary of the Parliament, with the intent
to indicate the Commission our ideas and
positions related to the future regulation.
As noted above, Europe wants and can
pioneer the legal establishment of an ethical
threshold that both protects European
citizens from the possible adversities that
this technological evolution entails, and
28 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
provides an added value of trust to European AI in
the world. An ethical threshold consistent with our
European principles and values, reflected in the
Charter of Fundamental Rights of the European
Union, and congruent with our civilizing project.
A legislation inspired by a humanistic approach
and focused on technological development. A
regulation applicable to both, AI developed in
Europe or willing to operate in the Union, and with
the aim of becoming a basic framework shared
throughout the world on minimum requirements
in the development and use of this technology.
The planning of the sector should not be
left off solely in the hands of the market. From
the Parliament, we assert the need for public
participation that safeguards objectives beyond
the possible economic profitability, so that the
measurable aspects in terms of social profitability
are also the subject of research and development:
better public services, social responsibility,
environmental sustainability, gender equality...
I also perceive as essential a public-private
collaboration that allows mobilizing the sufficient
amount of resources needed to catch up with
the investment efforts done in other parts of the
world -such is the strategy that the Commission
itself proposes in its White Paper.
From a regulatory viewpoint, it is clear that
the Union understands the need to establish
a demanding framework for safeguarding
citizens’ rights, as well as the essential principles
contained in the Lisbon Treaty. Nonetheless, we
also believe that certain margin of maneuver is
essential for the operators, and that they are the
ones who assume the need to bind themselves
with an ethical model in the development and
deployment. Legislators are aware of the need not
to establish an oppressive regulatory framework
that stifles the initiative, and also that it will be
impossible to understand such a complex and
changing reality without a flexible framework
that does not become obsolete with the arrival
of the following innovation. Likewise, one of the
main objectives is to avoid the fragmentation of
the European digital internal market, while also
complying with the sacred principle of subsidiarity.
That is why the report approved by the Parliament
designs a top-down governance model: a
European coordinating body able to harmonize
the regulatory development throughout the
Union and to adapt quickly to technological
developments; and entities responsible for
administering and enforcing standards at the
national level. There has been an intense debate
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
Iban Garcia del
Blanco
Member of the
European Parliament
form Spain (S&D), Vice-
Chair of the Committee
on Legal Affairs,
Member of the Special
Committee on Artificial
Intelligence in a Digital
Age
around who should exercise that role without
the report ending up unraveling it. In my opinion,
the existence of a European agency in charge is
necessary, with sufficient rule to avoid the need
to regulate every small change that this constantly
growing technological environment is generating.
The report approved by the European
Parliament puts emphasis on the control of the
so-called “high-risk applications”, for which it
provides controls prior to their use and throughout
their operation, as it establishes demanding
obligations related to transparency, auditability
and reversibility. To define this high risk, the text
proposes a combination of categories referring to
the degree of predictability of putting a community
or an individual at risk, the use and the sector
where it will be used. Through this mixed system
we intend to give legal certainty to the system and,
at the same time, generate what has to be known
as “trustworthy ecosystem”.
Our objective is also that the design, the
development, the control and the supervision
of this regulatory framework be participated by
all citizens, and especially by the most involved
or affected individuals and groups. The text
establishes a mandate for all European and national
supervisory bodies so that, in an indispensable
way and periodically, they have the assistance
and participation of civil society in the design of
the main characteristics of the model, with special
attention to integrate the perspective of the small
and medium companies, the unions, or the
consumers. We intend not only a co-responsibility
on the compliance and enforcement, but also their
inclusion somehow or other in the design of the
governance mechanisms for the entire system.
Finally, I would like to highlight the need to
incorporate the general public into the debate and
knowledge of the implications of AI. This debate
cannot be only reduced to a group of experts,
operators, legislators and even associations and
groups of users. Such a disruptive technology has
to be subject to democratic control; societies as a
whole have to be aware of this reality and be able to
make big decisions about the models of society they
want. For this reason, I prefer to talk about “digital
literacy” rather than “acquisition of digital skills”
-and I believe that it is the duty of public authorities
to make the main concepts understandable to their
citizens and to guarantee a mature public debate
around AI. In this, the very survival of democracy
is at stake, taking into account the disturbing social
effects that an uncontrolled application of AI can
have in some areas.
29

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
When the President of the
European Commission made
her first speech to the European
Parliament in December 2019, she officially
recognized “Artificial Intelligence” as an area
of strategic importance for the European
Union. Nine months later, addressing once
again the European Parliament in her maiden
“State of the Union’ speech”, she had moved
from spelling out “Artificial Intelligence” to
talking in terms of ‘AI’ – so well-known is the
technology within the EU bubble now. This is
not so surprising when AI is being deployed
across most (if not all) sectors of the economy,
from disease diagnosis to minimizing the
environmental impact of farming.
It is true that much work has been done
by the European Commission since President
Ursula Von der Leyen and her team took
office. Already promised in December 2019
was a “legislative proposal” on AI – what was
delivered was an AI White Paper in February.
While this, admittedly, is not a legislative
proposal, it is a document that has kick-started
the debate on human and ethical AI, the use of
Big Data, and how these technologies can be
used to create wealth for society and business.
The Commission’s White Paper
emphasizes the importance of establishing
a uniform approach to AI across the EU’s 27
member states, where different countries
have started to take their own approach to
regulation, and thus potentially, are erecting
barriers to the EU’s single market. It also,
importantly for Huawei, talks about plans to
take a risk-based approach to regulating AI.
At Huawei we studied the White Paper
with interest, and along with (more than
1,250!) other stakeholders, contributed to
the Commission’s public consultation, which
closed on 14 June, giving our input and ideas
as experts working in this field.
Finding the Balance
The main point that we emphasized to the
Commission is the need to find the right
balance between allowing innovation and
ensuring adequate protection for citizens.
In particular, we focused on the need for
high-risk applications to be regulated under
a clear legal framework, and proposed ideas
for what the definition of AI should be. In
this regard, we believe the definition of AI
should come down to its application, with
risk assessments focusing on the intended
use of the application and the type of impact
Angeliki
Dedopoulou
Senior Manager for
EU Public Affairs with
Huawei Technologies
30 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
SHUTTERSTOCK
AI in
the EU:
Balancing
Benefit &
Control
By Angeliki Dedopoulou
resulting from the AI function. If there
are detailed assessment lists and
procedures in place for companies
to make their own self-assessments,
then this will reduce the cost of initial
risk assessment – which must match
sector-specific requirements.
We have recommended that
the Commission looks into bringing
together consumer organizations,
academia, member states, and
businesses to assess whether an AI
system may qualify as high-risk. There
is already an established body set up
to deal with these kinds of things – the
standing Technical Committee High
Risk Systems (TCRAI). We believe this
body could assess and evaluate AI
systems against high-risk criteria both
legally and technically. If this body
took some control, combined with a
voluntary labelling system, on offer
would be a governance model that
• considers the entire supply chain
• sets the right criteria and targets
the intended goal of transparency for
consumers/businesses
• incentivizes the responsible
development and deployment of AI,
and
• Creates an ecosystem of trust.
Outside of the high-risk applications
of AI, we have stated to the Commission
that the existing legal framework based
on fault-based and contractual liability
is sufficient – even for state-of-theart
technologies like AI, where there
could be a fear that new technology
requires new rules. Extra regulation
is however, unnecessary; it would be
over-burdensome and discourage the
adoption of AI.
From what we know of the current
thinking within the Commission, it
appears that it also plans to take a
risk-based approach to regulating AI.
Specifically, the Commission proposes
focusing in the short-term on “highrisk”
AI applications – meaning either
high-risk sectors (like healthcare) or in
high-risk use (for example whether it
produces legal or similarly significant
effects on the rights of an individual).
So, What Happens Next?
The Commission has a lot of work to do
in getting through all the consultation
responses, taking into account the
needs of business, civil society, trade
associations, NGOs and others. The
additional burden of working through
the coronavirus crisis has not helped
matters, with the formal response from
the Commission now not expected until
Q1 2021.
Coronavirus has been a game-changer
for technology use in healthcare of
course, and will no doubt have an
impact on the Commission’s thinking in
this area. Terms such as “telemedicine”
have been talked about for years, but the
crisis has turned virtual consultations
into reality – almost overnight.
Beyond healthcare we see AI
deployment being continuously rolled
out in areas such as farming and in the
EU’s efforts to combat climate change.
We are proud at Huawei to be part of
this continuous digital development in
Europe – a region in which and for which
we have been working for 20 years.
The development of digital skills is at
the heart of this, which not only equips
future generations with the tools to seize
the potential of AI, but will also enable
the current workforce to be active
and agile in an ever-changing world:
there is a need for an inclusive, lifelong
learning-based and innovation-driven
approach to AI education and training,
to help people transition between jobs
seamlessly. The job market has been
heavily impacted by the crisis, and quick
solutions are needed.
As we wait for the Commission’s
formal response to the White Paper,
what more is there to say about AI
in Europe? Better healthcare, safer
and cleaner transport, more efficient
manufacturing, smart farming and
cheaper and more sustainable energy
sources: these are just a few of the
benefits AI can bring to our societies,
and to the EU as a whole. Huawei will
work with EU policymakers and will
strive to ensure the region gets the
balance right: innovation combined with
consumer protection.
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
31

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
AI and misinformation
Strong rules for digital
opinion-forming marketplaces
By Alexandra Geese
Alexandra
Geese
Member of the
European Parliament
from Germany
(Greens-European Free
Alliance), Member of
the Special Committee
on Artificial Intelligence
in a Digital Age
The Covid crisis in
particular has led to
a false information
epidemic, with
increased yearning
for simple answer
and quick fixes.
Simultaneously, the
corrective effect of real
life contact is lacking.
Who is protecting us?
There is no privacy on the
internet and nothing is as free
as it looks. And yet we confide
everything to it, even giving it authority
over democratic discourse. That is
dangerous. Because communication
on online platforms is controlled
by algorithms programmed with
ruthless business interests, seeking
to keep people on sites as long as
possible to show them adverts. That
works best with extreme content, for
our strongest emotions are fear and
anger.
Hate speech, misinformation,
polarisation and prejudices fall on
fertile ground, currently exacerbated
by the Covid crisis. We are all
spending much more time online,
thereby automatically coming across
more false information, including
disinformation deliberately spread
by domestic or foreign parties and a
whole host of other factually incorrect,
decontextualised or obsolete content
which mislead people, reinforce
dubious narratives and devalue true
expert knowledge.
That can have fatal consequences,
such as Donald Trump alleging
in spring 2020 that it might be
“interesting” to inject people with
disinfectant to fight coronavirus. A
US government expert had previously
correctly explained that bleach and
disinfectant could quickly kill the
coronavirus on metal surfaces. The
absolutely nonsensical idea which
Trump then released into the world
cost human lives, seeing significant
increases in calls to the poison centre.
People were gargling with bleach. A
family in Florida even sold bleach
as a “miracle mineral solution” and
founded a church to sell it.
The Covid crisis in particular has led
to a false information epidemic, with
increased yearning for simple answer
and quick fixes. Simultaneously, the
corrective effect of real life contact is
lacking. Who is protecting us?
Not the platform operators,
because misinformation helps their
business interests. Dubious but
highly emotional content boosts their
profits. Fact-checking is a good way of
preventing the worst excesses. But it
is no panacea. Twitter and Facebook
demonstrated how misleading content
can be handled differently during
the US presidential election. Whilst
Twitter placed fact-checking labels
on numerous tweets by President
Trump, Facebook and its algorithms
allowed the “Stop the Steal” group to
amass 200,000 members overnight,
despite it disseminating false election
information and inciting violence, thus
breaching the network’s terms and
conditions. However, democratic
governments must not make arbitrary
decisions about online content, either.
Freedom of expression is precious,
and no government should be able
to intervene in content moderation
on a political whim.
Rather, policy-makers ought to
draft and implement clear rules.
First of all, uniform rules for dealing
with illegal content. But the majority
32 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
of misinformation is not illegal. Should
we prohibit people from sharing private
content, the validity of which they cannot
judge? Of course not.
What we can demand and desperately
need, however, is transparency, first of
all for recommendation systems – the
artificial intelligence employed to govern
content. A Wall Street Journal report
found extremist content in a third of
all Facebook groups. The groups were
recommended to 66% of members by a
Facebook algorithm. We have a societal
right to know about and publicly debate
these decision mechanisms. Meaningful
transparency will be increasingly
important in the future - just think about
deep fakes – but it is already urgent.
AlgorithmWatch, a Berlin-based NGO,
has made concrete proposals regarding
researchers’ and investigative journalists’
access to raw data.
The mechanisms which currently
control our communication and
significant portions of the basic
information in public debate are a
veritable black box. Independent
researchers only have restricted access
to data, with access to public application
programming interfaces (APIs) being
increasingly curtailed in recent years,
leaving Facebook, YouTube and the like
to use every psychological trick to exploit
their knowledge monopoly. As a society,
we must put an end to this. We ought to
be able to co-determine rules for digital
opinion-forming marketplaces, based on
hard facts currently withheld from us by
conglomerates.
I propose “Social Media Councils”
as a model for public debate, similar
to Citizens’ Assemblies in Ireland,
comprising civil society, experts for
freedom of expression, democracy
and technology, and representatives of
groups particularly affected by hatred
and hate speech. They can trigger public
debates based on evidence gained
through transparency obligations,
identify good and bad practice, and
issue recommendations for action to
politicians. Facebook and its internal
ethics committee seek to privatise
precisely this public debate. Facebook’s
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
own internal appraisal of its practices
is commendable, but in the long run a
committee answerable to the CEO will
never decide in society’s favour over its
own boss’s business interests. That is
why we need space for public reflection.
Prominent Facebook critics such as
Carole Cadwalladr, who exposed the
Cambridge Analytica scandal, and Roger
McNamee, early investor in Facebook
and venture capitalist, have joined forces
in the “Real Facebook Oversight Board” to
publicise particularly dubious practices.
Micro-targeting and targeted
advertising, i.e. the dissemination of
advertising to very small target groups
based on previously collected and
collated data, must also urgently be
banned. The resulting gigantic database
with millions of highly detailed user
profiles also enables misleading
messages to be spread to especially
ISTOCK
susceptible users and is thus highly
problematic. Moreover, Google and
Facebook control large sections of the
ad-tech market, thereby constantly
expanding their market shares to
the detriment of European media
outlets. Nowadays, press publishers’
proceeds are also highly dependent
on behaviour-based advertisement.
However, the Dutch public broadcaster
NPO has demonstrated that equally
successful methods for context-based
advertisement can exist without personal
data or spying on and pursuing people
via various websites and into their offline
lives. A viable press financing model
is needed to reinforce substantiated
reporting as a foundation of liberty and
democracy, whilst not inadvertently
weakening that very democracy
through the unwanted dissemination of
misinformation.
33

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
ISTOCK
Teach AI in schools.
Win the tech race in 2030.
By Konstantinos Kyranakis
Ten years ago everyone was talking about the future
referring to 2020 as a really special year. Every nation,
every business, every sports team was making big
plans. Well, COVID ruined most of them, except one. The
COVID crisis accelerated technological advancements almost
everywhere in the world. Zoom calls, Amazon orders, online
project planners, gaming, Netflix and so many quarantine
apps skyrocketed this year.
However the tech king that has really expanded
horizontally this year is Artificial Intelligence (AI). Billions
of people have experimented with face filters on a mobile
app. Trillions of online purchases have been made because
of personalized suggestions. And surely while 2020 has not
been the best year for travelling, all those who went through
international airports for some reason might have noticed
automatic passport verification.
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PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Konstantinos
Kyranakis
Member of the Hellenic
Parliament, former
President of the Youth
of the European
People’s Party
Chances are that you fall at least in
one of those categories and if so you have
been exposed to the power of AI which
undoubtedly became a big part of our lives
in 2020 without making too much noise.
From face recognition to self-driving cars,
AI is growing exponentially and we need
to get ready for what is coming.
Kids entering first grade at elementary
schools this year, will be joining the
workforce in the decade of 2030. If
today we consider AI to be part of the
successful operations of a company or
a government, we can only imagine that
ten years from today it will be absolutely
necessary. It will be omnipresent and
its impact will be taken for granted. All
industries, including transport, logistics,
medicine, construction, defense,
architecture, customer service and even
tax authorities will be utilizing AI. We have
a duty to prepare the next generation for
what is to come.
That is the reason I submitted a
proposal to the Greek Parliament for AI
to be taught in schools. Obviously, no one
is expecting for 1st graders to become
Python developers, but if they learn from
a young age how to think algorithmically,
understand the purposes of AI and realise
what this technology can achieve, then
they will be able to go on and accomplish
goals that are hard to even conceive
today.
Concretely, basic methods and
patterns can be taught in the early
stages of elementary school, followed
by establishing a good base in intuitive
programming languages towards the
end of elementary. Stepping on these
building blocks, Machine Learning can
be progressively taught as a lesson that
combines mathematics and computer
science, two subjects that already exist
in schools. Gamifying this subject with
competitions such as Robotics will
also prove beneficial. Note that these
steps create a hazy path that could be
followed and mainly aim to initiate the
conversation of AI training in schools. I
expect that more bulletproof plans will be
created by expert committees when that
time comes.
A great experiment is going on in
Scandinavia right now. Finland has
foreseen the potential impact of AI and
created a course for efficient and effective
AI training for all, with an ultimate
objective of at least 1% of the population
getting trained. Similarly, Sweden spends
over half a billion € on AI research
programs such as W.A.S.P and heavily
invests in its universities. Already in 2018
French President Macron announced the
nation’s long term AI strategy and while
also mentioning that the country will be
investing north of 1.5billion € to boost
France’s AI capabilities. These are just
some of the examples that illustrate that
Europe is taking this issue seriously.
All that said, no investment will matter
if students across Europe today will not
become part of the AI revolution. For kids
with an inclination towards STEM subjects,
highlighting to them the power of AI from
an early age will only benefit them as they
will have a longer time to cultivate a good
understanding in the field. For all other
students, that will eventually focus on
other professions they will also likely end
up utilizing the power of AI, so learning
about it from a young age will ultimately
give them skills and knowledge so as
to become competitive and rise to the
challenges of the 2030 decade.
Europe can become a leader in AI,
10 years from now - so long that it starts
creating the future leaders of the subject
today, in schools. It follows that investing
in this proposal will lead to further
advancement of universities, state funded
research programs and easier talent
discovery by european companies. If we
want more “unicorns” in Europe, we need
bright young people that will have the
vision and the power to make european
companies, global tech winners.
Some people say access to capital is
the key to win the tech race. I think the
most valuable asset is human capital.
Technology is built by humans. And if
humans are intelligent enough to build
artificially intelligent machines that could
outsmart them, we owe it to our future to
educate our kids and make them smarter
than we are.
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
35

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
AI and Machine Learning in
Digital Campaigning – will
bots bring us closer than ever
or will they ruin our system?
By Hannes Kirchbaumer
Hannes
Kirchbaumer
Founder and CEO of
KIRCHBAUMER.COM,
a digital marketing and
political campaigning
consulting agency
based in Vienna
A
rtificial intelligence (AI) impacts numerous aspects of life, from smart
devices helping out in homes, self-driving cars, to the way we consume
information. With the ability to analyze data and learn from it, AI is
revolutionizing our world. Many industries have a high demand for AI capabilities
including the medical field, banking and of course: marketing. With the help of
very effective data analysis, combined with an ability to adapt to input, AI can
identify marketing trends. Organizations, brands and political parties can now
leverage AI to save time and resources through automated digital marketing
services.
Using statistical techniques, machine learning algorithms can easily identify
patterns in data. By analyzing online behavior of users which includes their
news and retail consumption patterns, relationships and social media patterns,
it is possible to create unique psychographic and behavioral user profiles. This
makes targeted advertising campaigns possible that can be sent to each user
based on their individual psychology. The more interaction data is available, the
better. It is all about sending the right information to the right person at the
right time. This goal has been here for a long time and with the right data to be
processed, it became reality.
FIVE MAIN BENEFITS OF AI IN DIGITAL MARKETING
1) MORE EFFECTIVENESS
AI helps businesses remove the guesswork to create a more effective
marketing strategy with data-driven analysis. Live dashboards and journey
analysis indicate what works best and how users react to certain events.
2) BETTER USER EXPERIENCE
AI makes it possible to provide any audience with the right content. This
applies not only to websites, but also to newsletters and social media.
3) HIGHER PRODUCTIVITY
Automation of repetitive tasks becomes possible, saving time and money.
Simultaneously, new resources are freeing up. Employees, who used to be
36 January 2021 | <strong>OUR</strong> <strong>WORLD</strong>

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
occupied with those tasks, can now focus
on even more important ones.
4) UNDERSTANDING THE AUDIENCE
With AI, data can be analyzed to
predict behaviors and decision of any
target user. The outcome can be used for
more tests that will deliver new insights
and live results.
5) BETTER DECISIONS
AI empowers decision-making and
helps to create better-performing
content. It is now also possible to let AI do
the creation work as long as some basic
information and graphics are provided.
Often, the website content might already
be enough for AI to make some well and
often high-performing ad creatives.
AI IN POLITICAL CAMPAIGNS
When working in the political
campaigning field, the first topic in a
discussion about AI will often be the
possible misuse of it. By knowing the
profiles of the users/voters, AI can of
course also be used to send personalized,
political messages that are insincere and
fake. Content that highlights a different
side of an argument can be sent to
potential voters and every voter gets a
different version that is in line with their
expectations. That helps the candidate
in developing a general opinion that is
in his or her favor.
In the 2016 US presidential elections,
it was found that automated social media
bots were used to increase the Twitter
traffic for pro-Trump hashtags.
They had roughly double the activity
of his rival, Hillary Clinton. On Facebook,
Cambridge Analytica provided audience
data that made super micro targeted
campaigns possible and it was found
that the way they had gathered data was
against the policy.
That did not benefit Facebook’s
general image – naturally, they had to do
something about it. Suddenly, targeting
for political campaigns and other social
issues was reduced significantly by the
big players.
But when checking whether
audiences and targeting settings are
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
acceptable and in line with policy, it is
again AI that is coming in. In the 2020 US
presidential elections, it was also used to
mark posts with a fact-checking warning.
Another misuse of AI and machine
learning is the development of so called
“deepfakes”. It concerns audio or video
content generated by AI that shows
someone saying or doing something
that was never said or done in reality.
These kind of videos appear extremely
authentic and can be used as a way of
influencing people. Ironically, it might
need AI again to detect this kind of
content.
THE FUTURE OF AI IN DIGITAL
MARKETING & CAMPAIGNING
Artificial intelligence is an incredible
technology that can work wonders for any
organization. Using AI digital marketing
strategies will help deliver improved
customer experience and market more
effectively. AI digital marketing also
changes the way we build websites and
interact with customers.
SHUTTERSTOCK
But what must public and political
organizations do to be able to use both
the audience and creation part in a good
way?
Everything comes down to collecting
your own data, own AI and machine
learning solutions and get in more direct
content with your users. It is still possible
to gain first-party data with campaigns
on the big networks. Exactly that data
should be used to create a perfect
database to work with.
The demand of GDPR-compliant
community-building services with an
AI-based, smart marketing automation
functionality are very high at the moment
and I would recommend all kinds of
organizations to find a way to build a
so-called “client data platform”. With
increasing restrictions on third party
data, it is an absolute obligation to have
your first party data in perfect condition.
It will help organizations not only reach
their users more efficiently, but also give
them valuable information they might
not have expected.
37

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Artificial intelligence’s great impact
on low and middle-skilled jobs
By Sybrand Brekelmans & Georgios Petropoulos
Sybrand
Brekelmans
& Georgios
Petropoulos
Sybrand Brekelmans:
Research assistant at
Bruegel
Georgios Petropoulos:
Marie Curie
Skłodowska Research
Fellow at MIT and
Bruegel and postdoctoral
fellow at the
MIT Initiative on the
Digital Economy
T
he academic literature suggests
that, in the past decades,
technological progress has
led to job polarisation in European
Union countries. While computer
technologies and robots have
replaced, to some extent, routine
middle-skilled jobs such as machine
operation, construction work or
administrative work, they have also
led to an increase in complementary,
non-routine high-skilled jobs (eg
managers, professionals) and in
low-skilled jobs (eg agriculture,
cleaning and personal care services).
However, our new research suggests
that the new technologies that have
emerged since 2010 – artificial
intelligence and machine learning –
are set to change drastically the job
landscape over the next few decades.
These technologies are likely to have
a deeper impact across a wider range
of jobs and tasks, including possible
destruction of low-skilled jobs.
Artificial intelligence (AI) systems
are able to perform tasks that involve
decision-making, therefore changing
the impact of automation on the
workforce.
AI-powered technologies can
now retrieve information, coordinate
logistics, handle inventories, prepare
taxes, provide financial services,
translate complex documents, write
business reports, prepare legal briefs
and diagnose diseases. Moreover,
they are set to become much better
at these tasks in the next few years
thanks to machine learning (ML):
computers fed by big data can learn,
practice skills and ultimately improve
their own performances and perform
their assigned tasks more efficiently.
Our new working paper evaluates
the ‘probability of automation’ for
different jobs, using data from 24
European countries.
This probability is initially
computed at the job task level and
then aggregated at the occupational
level (Table 1). Since each job
consists of a variety of tasks, with
different potential for automation,
the probability of automation at the
job level does not necessarily mean
the destruction of jobs, but rather
whether automation can significantly
transform the nature of those jobs.
We use this measure of
automation in an aggregate
framework where jobs are grouped
into three different categories of skill:
low, middle and high-skilled jobs.
Figure 1 shows the results.
These results suggest that
artificial intelligence and machine
learning will have different impacts
compared to computer and robotic
technologies, which caused job
polarisation (drop in routine middleskilled
jobs and increase in lowskilled
jobs). In contrast, AI is highly
likely to significantly alter not only
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PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Table 1: European jobs with the highest and lowest probabilities of automation
Figure 1: Exposure to automation of different skill groups
middle-skilled jobs, but also low-skill employment. Moreover,
while the high skilled are relatively less at risk from AI and
ML-induced transformation, its impact is still non-negligible
for these jobs.
The results also suggest a future transformation of work.
In middle and low-skilled jobs, AI systems will complete the
easily automated tasks while humans continue to perform
those that cannot be automated.
A high probability of automation may also be associated
with the creation of new tasks and jobs though the productivity
gains from adopting AI technologies, but these jobs and tasks
will most likely be high-skilled.
The transformative nature of AI and ML requires proactive
measures to re-design labour markets. The workforce needs
to be prepared for the upcoming changes, while the efficiency
gains from these technologies should be harnessed. Countries
with high degrees of labour flexibility, high quality science
education and less pervasive product market regulations tend
to have higher skill-oriented job structures and are therefore
less exposed to labour transformation due to automation.
Brekelmans S., G. Petropoulos (2020), ‘Artificial intelligence’s great impact on low and middle-skilled jobs’, Bruegel Blog, 29 June, available
at https://www.bruegel.org/2020/06/artificial-intelligences-great-impact-on-low-and-middle-skilled-jobs/
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
39

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
ISTOCK
Global cooperation
is the only way forward
The ICT industry and its critical role
The crackdowns felt
across the industry
Through my long career in the Information
and Communication Technology (ICT)
industry, I have been lucky enough to be at
the front seat, watching as the world enters
this ever-changing era of digitalisation.
Over the years, we have developed
a unique ecosystem that enables
interactions between services, systems,
data, software, and people. In its very
essence, the digital world is the pathway
through which we connect, share, and
transform together as a global community.
We are in the midst of the Fourth Industrial
Revolution, seeing our society transform,
from innovative business models to the
Internet of Things (IoT), e-Learning to the
rise of remote working. We are all part of
an interconnected digital ecosystem and
it must remain so if we are to succeed in
tackling the world’s biggest challenges.
Digital technologies are increasingly
becoming the heart of the way we live
and work. What’s less understood, but no
less important, is that digital technologies
are becoming essential to our future on
the Planet, its ecosystems, and inclusive
economic development. The way that we
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PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Luis Neves
CΕΟ of the Global
Enabling Sustainability
Initiative (GeSI)
<strong>OUR</strong> <strong>WORLD</strong> | December 2020
use digital technologies will determine our
future.
Recent research by GeSI, conducted
with the support of Deloitte, finds that the
application and use of digital technology will
be essential to achieving the bold objectives
set by the UN Sustainable Development
Goals (SDGs). What’s particularly urgent is
to mobilise the use of digital technologies to
transition to a low carbon economy that will
mitigate the worst consequences of climate
change. Digital technologies, GeSI’s research
finds, have the unique capability to enable
nations to decouple carbon emissions from
economic growth – enabling the world to
raise the standard of living while slowing
temperature increases.
Yet just when the world’s governments
need to be collaborating to promote global
technology strategies for sustainability, we
see the increase of techno-nationalism.
Around the world, countries are instituting
destructive, zero-sum policies that are
hampering access to and the application
of technology that would help the world
advance to achieve the SDGs.
• The US and Japan are restricting
Chinese made components that
affect the progress of building 5G
networks
• China has retaliated by ordering its
public institutions and government
agencies to stop using foreign made
computers and software
• In Brussels, the EU has called for
the creation of a U.S.-EU Trans-
Atlantic economic model that block
China’s attempts to influence global
standards in 5G and other nextgeneration
technologies.
• Middle East countries face
technological sanctions
• India has explored ways to
nationalize data.
The list of examples goes on. Arguments
abound regarding the importance of
protecting national security and economic
sovereignty. Yet the arguments overlook
a larger, more urgent question regarding
collective security.
As it stands the situation will inevitably
lead to the fragmentation of the global
supply chain, decrease competition and
increase prices for European consumers and
manufacturers.
We need a global effort to adopt cutting
edge networks, super-fast processors, smart
data analytic capabilities, etc. and apply
them to the most pressing challenges that
the world faces regarding climate change,
biodiversity loss, water scarcity, poverty,
health, and education, among others.
The more techno-nationalism and its
bans, restrictions, and crackdowns we see,
the more the world will notice the effects
of these actions which will be felt not only
across industry but also society.
Everything today depends on digital
technologies. Digital technologies have
been the engine of growth and prosperity.
They keep us connected. They are at the
centre of our global society as the COVID-19
pandemic has shown. They are meant to be
used for good, for peace, and development.
Finally, they need to be accessible to all
and universal. We need to do everything to
ensure this Purpose is maintained.
The path forward
What are we as an industry to do? What
are we as citizens, policy makers, to do? We
must choose a smarter future. With our
unparalleled capabilities and interconnected
systems, what we must do now is continue
down the path we have already set for
ourselves: towards the achievement of the
Sustainable Development Goals (SDGs).
With the European Union ramping up its
digital strategy, no company should be left
behind so we can ensure competition and
a fair level playing field aiming at a stronger,
smarter, more sustainable world. Global
cooperation is the only way forward.
The challenges our society are currently
facing and will continue to face requires
smart, innovative minds working together
to find impactful solutions and fast. The ICT
sector has a unique opportunity to rebuild a
society that has been hampered by disease,
politics, and war. Technology can accelerate
action, but it requires a robust, shared
ambition. Critical to a shared ambition is a
strong leadership to secure it. We must take
the lead on global transformations to create a
more prosperous, inclusive, and sustainable
world with digital at its core. Failure is not an
option. We can only succeed if we move
forward together.
41

PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
The dynamics of data accumulation
By Julia Anderson
Each new sunrise increases the certainty that the sun will rise
tomorrow. The more we know about the past, the better we
can predict the future. The same holds true in important AI and
related machine learning (ML) applications, where great volumes of
data are needed to reach marketable levels of predictive accuracy.
To maintain dynamism in ML markets, competition authorities
must thus ensure that all participants access the data they need to
compete viably. But how much is enough? Is more always better,
or is there a point where additional observations do little to change
predictions? Do we need to observe the sun rise a billion times to
know what will happen tomorrow?
Training data is a scarce resource
As detailed in a previous blog, machine learning (ML), a subfield
of AI, is a prediction technology that generates new information
(‘predictions’) based on existing information (‘data’). Notable ML
applications include automated driving, image recognition, language
processing and search. [1]
Like any product, ML models are only as good as the raw
material. Access to adequate training data is critical for ML application
vendors—which The Economist went as far as to qualify as “the
world’s most valuable resource”.[2]
Where do ML providers and adopters obtain this valuable
resource? Varian (2018) lists eight potential sources, including: as a byproduct
of operations (e.g. generated from machines and sensors),
web scraping, data vendors (e.g. Nielsen), cloud providers (e.g.
Amazon), public sector data, and offering a service (e.g. ReCAPTCHA).
Yet, despite a variety of sources, training data can be an
important bottleneck for businesses trying to develop or implement
ML—and an on-going concern for ML models that require regular
retraining. According to a 2019 European Commission report, access
to adequate training data is a key limiting factor for the development
of ML applications.[3]
One particular issue is that data must be ‘big’—a term that is
often used to refer to datasets for which individual observations
carry little informational content, so that value is derived from having
a large number of observations (Chakraborty, 2017). In many ML
applications, data volume greatly affects model performance.
Image recognition algorithms, for instance, must be trained on
large volumes of data (Carrière-Swallow et al., 2019). The question
here is: How much (relevant) data is enough to make a valuable ML
model?[4]
Economies of scale and the volume of data needed
The relationship between data volume and returns from ML
is a contentious topic. Hal Varian and Pat Bajari, chief economists
at Google and Amazon, respectively, argue that model accuracy
increases with data sample size, but at a decreasing rate.[5]
Intuitively, in teaching me (or an algorithm) what Labradors looks
like, the first ten Labradors are more informative than the following
ten. In technical terms, data exhibits ‘decreasing returns to scale’.
Figure 1. Decreasing returns to scale of Stanford Dogs data for
ML accuracy Source: Varian (2018)
If data exhibits decreasing returns to scale, then we might
conclude that firms only need to secure a large-enough lumpsum
of data to compete viably. Economists Ajay Agrawal,
Joshua Gans, and Avi Goldfarb disagree.[7] Their argument
runs as follows: even if data exhibits decreasing returns (as
in Figure 1), a slight lead in data quantity may induce a slight
lead in quality that attracts users. More users generate more
data, which drives higher quality. This is the so-called ‘data
feedback loop’ illustrated in Figure 2. Over time, a small initial
data advantage can translate into a significant share of the user
base and of the market. In the long run, this self-reinforcing
dynamic can lead to market dominance.
In essence, Agrawal et al. distinguish between technical returns
to data (i.e. accuracy) and economic returns to data (i.e. market
shares), and argue that even if data exhibits decreasing returns in
a technical sense (as in Figure 1), returns can be increasing in an
economic sense.
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PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
Figure 2. The data feedback loop
Source: based on OECD (2016)
One of the mechanisms underlying the data feedback loop
relates to the increased granularity that comes with volume. Google
and Microsoft’s differing views in the area of internet search ML
illustrates this point.[8]
Microsoft, in justifying a 10-year deal for Yahoo search in 2009,[9]
has advanced that higher performance is a direct consequence of
scale.[10] Following this argument, Google search algorithm is better
than Yahoo’s because more people use Google. More people using
Google means that Google can collects more data and provide better
search results, attracting more users, and so on. This is the feedback
loop in action.
Google has expressed scepticism about Microsoft’s scale
argument.[11] Yahoo’s billions of searches should be sufficient to
compete effectively.[12] Yahoo’s inferior performance would reflect
the lower quality of its algorithm,[13] rather than the amount of data
it holds. To quote Google’s Varian: “it’s not quantity or quality of the
ingredients that make a difference, it’s the recipes”.
Microsoft has countered that its billions of searches are
insufficient. Many queries are extremely rare and, for these queries,
Google has more observation points, simply because it holds more
data. Google thus produces better results for rare queries.[14]
Empirical work by Schaefler and al. (2018) vindicate Microsoft’s
claims. Schaefler and al. (2018) examine the impact of additional
user data on the quality of internet search results. They disentangle
the effects of more data from the effect of better algorithms and
find that both matter. In their oven, Varian’s analogy falls flat: both
ingredients and recipe are critical to good cooking.
The Google and Microsoft disagreement also exposes the
strong behavioural and structural assumptions that underlie the
feedback loop argument. In the internet search case, for instance,
the argument requires that (i) consumers can detect small quality
differences, (ii) quality differences matters to users (or at least a few),
and (iii) switching costs are sufficiently low. The same reasoning
should not be expected to hold, however, in all ML markets.
In speech to text applications, for instance, it is not clear that small
quality differences are obvious to the user, or that they would matter
enough to justify the potentially large costs of switching providers
(e.g. learning to use a new application). On the other extreme, some
application areas are very sensitive to quality differences. These are
applications where accuracy is a matter of life and death.[15] Who
would accept a second-best medical diagnostic?[16]
No one wants Dr Bing when Dr Google is down the
corridor. For these applications, returns to prediction
quality may amount to the whole market, i.e. “the
winner takes all”.
A large share of the empirical work around
returns to scale for data focuses on one ML
application: internet search.[17] More research
is needed to determine where the feedback loop
argument holds, because the answer is likely to vary
across different application area.
Economies of scope and the volume of
data needed
Glen Weyl and Eric Posner present a different
argument in the debate. In their 2018 book Radical
Markets, the Microsoft economist and University of
Chicago law professor argue that the returns to data
are increasing in scale, but with a twist.
Decreasing returns to scale, they claim, only hold
in pre-ML ‘standard’ statistical contexts. Standard
statistics addresses relatively simple problems (e.g.
estimate a population average) for which rough
Julia Anderson
Research Analyst,
Bruegel
estimates are sufficient:
An entrepreneur who wants to open a wealth
management firm in a neighbourhood wants to
know whether the average income is $100,000 or
$200,000 but doesn’t need to know that it is $201,000
rather than $200,000.[18]
ML tackles problems that are much more
complex, according to Posner and Weyl. These
new, harder tasks are more valuable than earlier,
easier ones. They also call for more data, and more
complex data. For these problems, the more the
data, the better it can address these harder tasks.
Therefore, and still following Posner and Weyl, the
more the data, the more valuable it is.
To illustrate the authors’ point, consider training
an ML to recognise dogs in pictures. This model
requires a certain quantity of data to produce
accurate results. But, as illustrated in Figure 1,
the value of additional data drops past a certain
threshold. However, as the volume of data continues
to grow (and complexity along with it), the algorithm
continues to learn. At some point, it can perform
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PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
an additional, more complex task, such as labelling objects in the
photographs. Again, the value of additional data for the task of
labelling objects flattens out past a certain threshold. Once again,
as the volume of data continues to grow, the algorithm continues
to learn. At some point, it can perform an additional, even more
difficult task, such as understanding the nature of the actions in
the photographs.
Therefore, the value of data grows to the extent that harder
problems need more data. Weyl and Posner’s argument is, implicitly,
that data exhibits economies of scope. As Figure 3 illustrates, we
end up with a picture that challenges Varian’s decreasing returns to
scale hypothesis. Weyl and Posner note that the value of data may
not increase forever: we could see a future where ML has “learned
everything”. But until then, they claim, returns to the volume of data
are increasing.
Figure 3. Value of data as a function of the number of
observations in a typical ML
Source: Weyl and Posner (2018)
Data as a barrier to entry
Economic theory teaches us that, where there are increasing
returns to scale, monopolies naturally emerge—the largest firm
being the most economically efficient.[19] Likewise, where they
materialise, increasing returns to scale to data could be expected to
work to concentrate economic gains in the hands of data-rich firms.
Monopolisation, however, is not necessarily synonymous with
monopolistic behaviour—i.e. supra-competitive pricing, reduced
quality, and/or hampered innovation. So long as the market leaders
are challenged by the prospect of competitive entry by other firms,
monopolised markets can be competitive.
Increasing returns to scale and scope for data, however, might
work in some cases to entrench the incumbent’s position by creating
very high barriers to entry for prospective entrants. This could be a
problem in markets where there is no substitute for a dataset that
is essential, i.e. where there is only one dataset and it cannot be
replicated, dispensed or purchased.[20] As US FTC Commissioner
Terrell McSweeny has noted, “it may be that an incumbent has
significant advantages over new entrants when a firm has a database
that would be difficult, costly, or time-consuming for a new firm to
match or replicate.”[21]
Incumbency advantage could be particularly pronounced in the
digital space. Leading ML users were once leading data collectors,
but increasingly are leading ML providers. Amazon, for instance,
originally sold books as a way to gather personal data on affluent,
educated shoppers (Ezrachi et al. 2016). It is now a leading provider
of ML services on the cloud.
‘Data scarcity’ can seem oxymoronic. The world overflows with
information—right up to toilet seats, the new data hotbed. The
volume of data created and copied each year is expected to reach
44 x 1021 bytes in 2020 — 40 times more bytes than there are stars in
the observable universe.[22] Yet the vast amounts of data collected
online form unique sets of behavioural data that may be harder and
harder to replicate in light of the self-reinforcing dynamics described
above.
Why not let prospective entrants buy the necessary data in
one of the many dedicated marketplaces (such as datapace.io)? A
concern is that data incumbents have strong reasons not to sell their
data: increasing economic returns to data tend to create perverse
incentives for firms to establish a data advantage and erect barriers
to entry thereafter (Cockburn et al., 2018). Incumbents might prefer
hoarding the data they collect in order to, first, gain an advantage
over their competitors and, later, curtail market entry (Jones et al.,
2018).
Two factors that could tend to compound these
challenges.
First, as previously noted, data-rich firms could benefit not only
from economies of scale, but also from economies of scope. Data
acquired for a particular purpose may be valuable in other contexts,
granting incumbent firms an advantage over new entrants in
adjacent markets (Goldfarb et al., 2018). For instance, data collected
in the context of search queries can be used to inform a shoppingrecommendation
algorithm. Economies of scope could leave little
room for potential entrants looking to grow outside the incumbents’
market segments.
Second, even if a firm successfully enters a new ML application
market, incumbents may be in a position to use their rich data to
detect the competitive threat, and acquire the new entrant firm
before the incumbent’s position is challenged (so-called ‘killer
acquisitions’).
Implications for competition policy[23]
Absent intervention, a possible market outcome could be high
concentration and low contestability in data-reliant markets. This
implies the need for competition policy scrutiny around data access.
Should unique and non-substitutable datasets be considered an
‘essential facility’, on par with local loops for fixed telephony?
Forced sharing can create inefficiencies, e.g. in the form investment
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PERSPECTIVES ON ARTIFICIAL INTELLIGENCE
disincentives, but is well established EU policy in many of the network industries. Under what conditions would the benefits of forced
sharing outweigh the costs?
The emergence of ML as a general-purpose technology raises difficult empirical and normative questions. Does the relationship between
data accumulation and economic returns give data-rich incumbents a significant and self-reinforcing advantage? Are competition authorities
equipped to discern and analyse data-driven monopolistic returns? These questions are high on the new European Commission’s agenda,[24]
and for good reasons. Monopolistic behaviour by ML providers could slow the adoption of technology critical for EU competitiveness,
especially hitting those smaller firms that lack the knowledge and resources to build alternative capacity in-house. If technological revolutions
are distributional earthquakes, competition authorities should work to ensure that everyone lands on their feet.
[1] This post focuses on ML applications.
[2] See https://medium.economist.com/will-big-data-create-a-new-untouchable-business-elite-8dc23bcaa7cb
[3] DG COMP 2019, citing https://medium.com/machine-intelligence-report/data-not-algorithms-is-key-to-machine-learning-success-69c6c4b79f33, https://www.
edge.org/response-detail/26587, and http://www.spacemachine.net/views/2016/3/datasets-over-algorithms.
[4] While this post focuses on issues pertaining to the volume of data, other characteristics of data are just as important for generating value. These include the
other so-called ‘4Vs’ of data: volume, but also velocity (i.e. frequency), variety (e.g. administrative data, social media data, pictures, etc), and veracity (i.e. representative
of the target population, free of bias, etc). For a firm to have a competitive advantage over these other characteristics can also generate important economic benefits.
For the purpose of this blog, I note that securing a sufficient volume of data appears to be necessary but not sufficient to having a competitive AI/ML business.
[5] Varian (2018) and Bajari et al. (2018)
[6] In particular, Bajari et al. (2018) find that the length of histories is robustly helpful in improving the demand forecast quality, but at a diminishing rate; whereas
the number of products in the same category is not (with a few exceptions where it exhibits diminishing returns to scale).
[7] Agrawal, Ajay, Joshua Gans, and Avi Goldfarb. 2018a. Prediction Machines: The Simple Economics of Artificial Intelligence. Cambridge, MA: Harvard Business
Review Press.
[8] As related in Goldfarb et al. (2018)
[9] See the deal’s press release: https://news.microsoft.com/2009/07/29/microsoft-yahoo-change-search-landscape/
[10] See https://www.cnet.com/news/googles-varian-search-scale-is-bogus/
[11] See Hal Varian in a CNET interview: “the scale arguments are pretty bogus in our view” (https://www.cnet.com/news/googles-varian-search-scale-is-bogus/)
[12] “the amount of traffic that Yahoo, say, has now is about what Google had two years ago” and “when we do improvements at Google, everything we do
essentially is tested on a 1 percent or 0.5 percent experiment to see whether it’s really offering an improvement. So, if you’re half the size, well, you run a 2 percent
experiment.” Source: ibid
[13] i.e. in performing tasks such as crawling, index, or ranking.
[14] The European Commission’s DG COMP made similar claims in the context of the Google Shopping case. DG COMP claimed that general search service has
to receive at least a certain minimum volume of queries in order to improve the relevance of its results for uncommon queries because users evaluate the relevance
of a general search service on the basis of both common and uncommon queries. See para. 288 of the EC decision (https://ec.europa.eu/competition/antitrust/cases/
dec_docs/39740/39740_14996_3.pdf)
[15] Cockburn et al. (2019).
[16] An podcast episode from the Economist brings this point to life (https://www.economist.com/podcasts/2019/10/09/the-promise-and-peril-of-ai)
[17] See Schaefler and al. (2018): “In perhaps no other market has the question of the role of data stirred such a vivid discussion among industry participants,
academic experts, and policy advocates than in general internet search.”
[18] Glen Weyl and Eric Posner, 2018. Radical Markets
[19] https://cs.stanford.edu/people/eroberts/cs181/projects/1997-98/microsoft-vs-doj/economics/returns.html
[20] See Calvano et al. (2020) for a survey of the literature around these issues in digital markets.
[21] Commissioner Terrell McSweeny, Opening Remarks for a Panel Discussion, “Why Regulate Online Platforms?: Transparency, Fairness, Competition, or
Innovation?” at the CRA Conference in Brussels, Belgium, at 5 (Dec. 9, 2015), https://www.ftc.gov/system/files/documents/public_statements/903953/mcsweeny_-_
cra_conference_remarks_9-12-15.pdf.
[22] includes data generated online and by IoT and connected devices. Source: Word Economic Forum citing Raconteur (https://www.weforum.org/agenda/2019/04/
how-much-data-is-generated-each-day-cf4bddf29f/)
[23] Note that a range of issues lie at the intersection of privacy and competition, including data ownership, reuse, transparency, sharing. These issues are beyond
the score of this post and will not be explored here.
[24] See the mission statement of European Commission President Ursula von der Leyen, which instructs Margarethe Vestager: “In the first 100 days of our
mandate, you will coordinate the work on a European approach on artificial intelligence, including its human and ethical implications. This should also look at how
we can use and share non-personalised big data to develop new technologies and business models that create wealth for our societies and our businesses.” (https://
ec.europa.eu/commission/sites/beta-political/files/mission-letter-margrethe-vestager_2019_en.pdf)
Anderson, J. (2020), ‘The dynamics of data accumulation’, Bruegel Blog, 11 February.
https://bruegel.org/2020/02/the-dynamics-of-data-accumulation/
<strong>OUR</strong> <strong>WORLD</strong> | January 2021
45

Fearless
fear·​less​|​ˈfir-ləs
NEW EUROPE

WE SHARE THE SAME VISION
WE GROW TOGETHER
BETTER TOGETHER