DDr. Alexander Petsche (Baker & McKenzie)

March 13, 2019 DDr. Alexander Petsche, MAES
Compliance in a Digital World:
Misconception or Reality?

Overview
01
From big to smart data – What is it? What
can it do and what not?
3
02
AI powered “Robo-Lawyer” by the example of
the Rolls Royce investigation
12
03
Rise of the racist robots & the respect of
human rights
16
04 The upcoming legal challenges 21

From BIG to smart data – What is it?
What can it do and what not?
01

© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
What do we mean by “BIG data”?
4
“Big Data” refers to datasets whose size is beyond the ability of typical database software
tool to capture, store manage and analyze. This definition is internationally subjective and
incorporates a moving definition of how big a dataset needs to be in order to be considered
big data – i.e. we don’t define big data in terms of being larger than a certain number of
terabytes. We assume that, as technology advances over time, the size of datasets that
qualify as big data will also increase. The definition can also vary by sector, depending on
what kinds of software tools are commonly available and what sizes of datasets are common
in a particular industry. With those caveats, big data in many sectors today will range from a
few dozen terabytes to multiple petabytes.

© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 5
From big to smart
1 ZE
2012
462 EB
2010
52 EB
1 EB
1 PB
1 TB
GB
100
1 GB
1 MB
1 exabyte…
Five times the amount of
information contained in
all the books ever printed
1 petabyte…
The capacity of all data
centers worldwide in 2002
52 exabyte…
The volume of data stored
in the cloud in 2010
462 exabyte…
The volume of data stored
in the cloud in 2012
1 zettabyte…
1.5 times the amount of all
grains of sand on all
beaches on earth
In 2020, the volume of all
data stored will amount
approx. to 40 zettabytes
Development of data volume
The volume of digital data available
worldwide is growing rapidly,
including in industrial process. Most
of the data is unstructured, which
makes it difficult to use.

What makes big data a big deal?
volume
velocity
veracity
variety
So-called
“4Vs”
How much of the data stream is
spoofers, spammers, pranksters,
and hackers? How much is coming
from unreliable equipment or
sources? When it comes to Big
Data that’s driving your most
important business decisions,
hygiene matters.
All kinds of systems, devices, and
sources contribute to the Big Data
explosion, and the most valuable
insights emerge from mashing up
data sets to spot unusual
correlations.
The sheer quantity of data produced
today by internet usage, social
networks, mobile devices, sensors,
embedded systems, and enterprise IT
is exponentially greater than anything
seen previously in human history.
We’re not only producing more data,
we’re producing it faster: nearly 2.5
exabytes each day. Every minute,
there are more than 205 million email
messages sent. A single 30-minute
plane ﬂight generates 10 terabytes of
data.

Smart Data
 2 primary kinds of Smart Data
 Smart Data can be described as Big Data that has been cleansed, filtered, and prepared
for context
 According to the 4 Vs – especially a reduction in “volume” takes place with Smart Data
information picked up by a sensor
Big Data that has been processed and is
waiting to be turned into actionable information

Artificial Intelligence and Smart Data
 The increasing focus on smart data instead of big data is
strongly related with the upcoming algorithm economy
 Most data out there is unstructured; only with artificial
intelligence and analytics unstructured data can be turned into
smart data and actionable data
 Artificial Intelligence has provided flexibility and can address
unique goals. For example, financial services firms can use AI-
driven Smart Data for customer analysis, fraud detection,
market analysis, and compliance

What do we mean by AI?
10
Artificial intelligence (AI) refers to systems that display intelligent behavior by analyzing their
environment and taking actions – with some degree of autonomy – to achieve specific goals.
AI-based systems can be purely software-based acting in the virtual world (e.g. voice
assistance, search engines) or AI can be embedded in hardware devices (e.g. drones,
autonomous cars)
We are using AI on a daily basis, e.g. to translate languages, block email spam.
Many AI technologies require data to improve their performance. Once they perform well,
they can help improve and automate decision making in the same domain, e.g. AI system will
be trained and then used to spot cyberattacks on the basis of data from the concerned
network or system.

4 types
from reacting robots to self-aware beings
 Type I: Reactive machines
The most basic type are purely reactive, and have the ability neither to
form memories nor to use past experience to inform current decisions,
e.g. chess-playing supercomputer.
 Type II: Limited memory
This class contains machines, which are able to look into the
past. For example self-driving cars are doing some of this
already.
 Type III: Theory of mind
The machine itself is capable of interpreting the world around it
and form information that can be reproduced when needed
based on the observations it made.
 Type IV: Self awareness
This most advanced type of AI involves machines that have
consciousness and don’t only recognize it in humans.

AI powered “Robo-Lawyer” by the
example of the Rolls Royce investigation
02

Key facts
 The Serious Fraud Office (SFO) tackles top-level fraud, bribery and corruption cases
 SFO piloted a software robot to process more than half a million documents a day
 The system supposed to work 2.000 times faster than its human legal counterparts
 The SFO used AI to review over 30 million documents at the Rolls-Royce procedure
 It is getting harder for human beings to deal with the vast amounts of data that exist in
organizations' data centers
 After the SFO found conspiracy to corrupt, or failure to prevent bribery, Rolls-Royce was
made to pay £ 497,25 million
 Therefore the “Robo-lawyer” helps to step up the fight against economic crime

Conclusions
“Using innovative technology like AI is no longer optional
– it is essential”
The amount of data trend is continuing upwards as
company data is decently growing
AI is sizing the prize

Rise of the racist robots & the respect
of human rights
03

Does a horrifying future
await people forced to
live at the mercy of
algorithms?
17

Garbage in - Garbage out
When we feed machines data that
reflects our prejudice, they mimic them
AI don’t become biased on its own, it
needs to learn that from us
People expect AI to be unbiased
If one does not act carefully, it risks
automating the exact same biases
these programs are supposed to
eliminate
18

Respect human rights
In a world of machine
learning systems, who
will bear accountability
for harming human
rights?

New Toronto Declaration
 Calls on:
 algorithms to respect human rights
 governments and tech companies to
ensure that algorithms
respect basic principle of quality and
non discrimination
 Using the framework of international
human rights law
 The rights to quality and non-
discrimination
 Preventing discrimination

The upcoming legal challenges
04

Potential AI Legal Issues
Data Privacy
Law
Enforcement
Access to Data
Lack of
regulatory
Framework &
Standards
Legal Ethics
Intellectual
Property
Protection

Lack of Regulatory Framework & Standards
 Hardly any meaningful AI-related laws or standards
 AI could be defined as the Wild West according to regulatory framework
 Legal challenges between technology companies and the national authority pertaining to
law enforcement access to data

To legislate or not to legislate ?

Country-specific developments:
United States
 The aim of the bill is to better
understand how AI might
maximally benefit the
economic prosperity and
social stability of the US
 Establishes a Federal
Advisory Committee
 The bill promotes a "21st
century artificial intelligence
workforce"
 Focuses on training and
retraining American workers
in light of the possible effects
of AI on the workforce and
human worker demand
FUTURE of AI Act (Bill) AI Jobs Act (Bill)

United Kingdom
Proposed an AI Code with the following principles:
1. AI should be developed for the common good and benefit of
humanity;
2. AI should operate on the principles of intelligibility and fairness;
3. AI should not be used to diminish the data rights or privacy of
individuals, families and communities;
4. All citizens should have the right to be educated to enable them to
flourish mentally, emotionally and economically alongside AI; and
5. The autonomous power to hurt, destroy or deceive human beings
should never be vested in AI.
"Blanket AI-specific regulation, at this stage, would be
inappropriate. We believe that existing sector-specific
regulators are best placed to consider the impact on
their sectors of any subsequent regulation which may be
needed."

Asia Pacific
Jurisdiction Development
China The State Council in China issued a guideline on developing
an open and coordinated AI innovation system.
Singapore The Singapore Government aims to move towards an industry
specific approach rather than an overarching legislation for AI.

AI throughout the European Union
 Public and private research and development
investments in AI in the EU last year were
estimated to total € 4-5 billion
 The EU as a whole aims to increase this
investments to at least € 20 billion by the end of
2020
 Preparing the society as a whole for the tasks
which emerge as a result of AI

Europe
The Declaration of Cooperation
on AI was signed on 10 April
2018 by 25 European countries.
Participating Member States agree to cooperate
on (amongst others):
"Ensuring an adequate legal and ethical
framework, building on EU fundamental rights
and values, including privacy and protection of
personal data, as well as principles such as
transparency and accountability.

Europe
In its Communication on AI for Europe,
the Commission highlighted the
following:
Draft AI ethics guidelines
On safety and liability, the Commission will
publish a report on the broader implication with
respect to liability and safety frameworks for AI,
IoT and robotics by mid-2019
Algorithmic Awareness Building
support consumer organizations and data
protection supervising authorities

Develop AI ethics guidelines - EU
 With due regard to the Charter of
Fundamental Rights of the European Union
 Mainly addressing the issues such as the
future of work, fairness, safety, security,
social inclusion and algorithmic
transparency
 public authorities have to ensure that AI
technology acts in line with the fundamental
rights

Safety and liability - EU
 Suitability of some established rules
on safety and civil law questions on
liability
 Product Liability Directive
 Machinery Directive
 General Product Safety Directive
 Aiming an efficient redress
mechanism for victims in case of
damages, for the purpose of building
user trust and social acceptance

Pilot project on Algorithmic Awareness
Building - EU
proposed by the
European Parliament
facing the challenging by
automated decision-
making such as biases
and discrimination

Empowering individuals and consumers to
make the most of AI
B2C transactions need to be fair
transparent and compliant with consumer
legislation
Building an understanding of AI-powered
applications
Informing people that they are interacting
with an automated system

Develop Appropriate Internal AI Practice
Promoting a
stronger
Compliance
culture
Performing
Due
Diligence
Enable
Anti-Corruption
Compliance

 Firstly, a compliance system doesn’t generate
revenue
 Secondly, compliance will always have to justify their
existence such as financial consequences (multi-
million FCPA fines, stock prices dropping, individual
directors fines), repercussions of bribery, reputation
of being corrupt
 Thirdly, with AI compliance functions could be leaner
 more attractive
 Reducing high-volume – low-benefit transactions
 Would allow Compliance professionals to delve
into more complex issues
How AI can be used to create a more
attractive compliance program

 Ability to search for patterns of behavior and outliers
that could identify potential misconduct
 AI could narrow the scope of investigations, raise
awareness in patterns of criminal funds or mitigate
losses form fraudulent activity
 Contemplate the costs versus benefits of the
implementation of AI
Anti-Corruption Compliance

Diwok Hermann Petsche Rechtsanwälte LLP & Co KG is a member firm of Baker & McKenzie International, a Swiss
Verein with member law firms around the world. In accordance with the common terminology used in professional
service organizations, reference to a "partner" means a person who is a partner, or equivalent, in such a law firm.
Similarly, reference to an "office" means an office of any such law firm. This may qualify as “Attorney Advertising”
requiring notice in some jurisdictions. Prior results do not guarantee a similar outcome.
www.bakermckenzie.com
39
DDr. Alexander Petsche, MAES (Brügge)
Partner
Baker McKenzie
Diwok Hermann Petsche
Rechtsanwälte LLP & Co KG
Schottenring 25 Tel.: + 43 1 24 250 571
1010 Vienna alexander.petsche@bakermckenzie.com

DDr. Alexander Petsche (Baker & McKenzie)

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