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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.
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 6
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.
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 7
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 8
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
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.
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 11
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 15
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
Does a horrifying future
await people forced to
live at the mercy of
algorithms?
17
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 19
Respect human rights
In a world of machine
learning systems, who
will bear accountability
for harming human
rights?
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 20
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
The upcoming legal challenges
04
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
Potential AI Legal Issues
Data Privacy
Law
Enforcement
Access to Data
Lack of
regulatory
Framework &
Standards
Legal Ethics
Intellectual
Property
Protection
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 23
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 24
To legislate or not to legislate ?
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 25
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)
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 26
Country-specific developments:
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."
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 27
Country-specific developments:
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.
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 28
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 29
Country-specific developments:
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.
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 30
Country-specific developments:
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 31
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 32
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 33
Pilot project on Algorithmic Awareness
Building - EU
proposed by the
European Parliament
facing the challenging by
automated decision-
making such as biases
and discrimination
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 34
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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 35
Develop Appropriate Internal AI Practice
Promoting a
stronger
Compliance
culture
Performing
Due
Diligence
Enable
Anti-Corruption
Compliance
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
 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
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 37
 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
Q & A
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.
© 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
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

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DDr. Alexander Petsche (Baker & McKenzie)

  • 1. March 13, 2019 DDr. Alexander Petsche, MAES Compliance in a Digital World: Misconception or Reality?
  • 2. 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
  • 3. From BIG to smart data – What is it? What can it do and what not? 01
  • 4. © 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.
  • 5. © 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.
  • 6. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 6 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.
  • 7. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 7 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
  • 8. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 8 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
  • 10. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 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.
  • 11. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 11 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.
  • 12. AI powered “Robo-Lawyer” by the example of the Rolls Royce investigation 02
  • 13.
  • 14. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 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
  • 15. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 15 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
  • 16. Rise of the racist robots & the respect of human rights 03
  • 17. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG Does a horrifying future await people forced to live at the mercy of algorithms? 17 © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
  • 18. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 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 © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
  • 19. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 19 Respect human rights In a world of machine learning systems, who will bear accountability for harming human rights? © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
  • 20. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 20 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 © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG
  • 21. The upcoming legal challenges 04
  • 22. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG Potential AI Legal Issues Data Privacy Law Enforcement Access to Data Lack of regulatory Framework & Standards Legal Ethics Intellectual Property Protection
  • 23. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 23 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
  • 24. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 24 To legislate or not to legislate ?
  • 25. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 25 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)
  • 26. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 26 Country-specific developments: 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."
  • 27. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 27 Country-specific developments: 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.
  • 28. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 28 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
  • 29. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 29 Country-specific developments: 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.
  • 30. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 30 Country-specific developments: 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
  • 31. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 31 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
  • 32. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 32 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
  • 33. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 33 Pilot project on Algorithmic Awareness Building - EU proposed by the European Parliament facing the challenging by automated decision- making such as biases and discrimination
  • 34. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 34 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
  • 35. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 35 Develop Appropriate Internal AI Practice Promoting a stronger Compliance culture Performing Due Diligence Enable Anti-Corruption Compliance
  • 36. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG  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
  • 37. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 37  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
  • 38. Q & A
  • 39. 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. © 2019 Diwok Hermann Petsche Rechtsanwälte LLP & Co KG 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

Hinweis der Redaktion

  1. Definition quoted McKinsey
  2. 2020 forecast in a study by IDC
  3. VOLUME: 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. Just sifting through this mound of bits requires unprecedented computational resources and scale. VELOCITY: 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. For marketing organizations that aspire to manage their brands in real time, deriving insights from this data flow is like sorting water molecules as they’re emerging from a fire hose. VARIETY: 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. Traditional computing systems are not built to handle this kind of diversity and complexity; traditional data analysis methods have had to evolve as well. VERACITY: Poor data produces poor results, or, as IT engineers like to say, “Garbage in, garbage out.” 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.
  4. Definition quoted European Commission
  5. FUTURE of AI Act Federal Advisory Committee: Role: to promote a "climate of investment and innovation," "optimize the development of [AI]," support the "unbiased development and application of [AI]," and "protect the privacy rights of individuals.“ to provide advice to the Secretary of Commerce with regard to several specific topics: competitiveness of US, workforce matters, education, ethics training, opening sharing of data and research of AI etc. required to conduct a study on various intersections between AI and society, including analyzing the effects of AI on the economy, workforce, and competiveness of the United States. within 540 days of enactment of the Act, the Advisory Committee is required to provide a report based on the study to the Secretary of Commerce and Congress. Definition of AI: Any artificial systems that perform tasks under varying and unpredictable circumstances, without significant human oversight, or that can learn from their experience and improve their performance. Such systems may be developed in computer software, physical hardware, or other contexts not yet contemplated. They may solve tasks requiring humanlike perception, cognition, planning, learning, communication, or physical action. In general, the more human-like the system within the context of its tasks, the more it can be said to use artificial intelligence. Systems that think like humans, such as cognitive architectures and neural networks. Systems that act like humans, such as systems that can pass the Turing test or other comparable test via natural language processing, knowledge representation, automated reasoning, and learning. A set of techniques, including machine learning, that seeks to approximate some cognitive task. Systems that act rationally, such as intelligent software agents and embodied robots that achieve goals via perception, planning, reasoning, learning, communicating, decision making, and acting. Other terms: “artificial general intelligence”: "a notional future artificial intelligence system that exhibits apparently intelligent behavior at least as advanced as a person across the range of cognitive, emotional, and social behaviors.“ “narrow artificial intelligence”: artificial intelligence systems that address specific applications such as playing strategic games, language translation, self-driving vehicles, and image recognition. AI Jobs Act Definition of “artificial intelligence” as systems that: think like humans (including cognitive architectures and neural networks); act like humans (such as passing the Turing test using natural language processing, knowledge representation, automated reasoning, and learning); think rationally (such as logic solvers, inference, and optimization); act rationally (such as intelligent software agents and embodied robots that achieve goals via perception, planning, reasoning, learning, communicating, decision-making, and acting); or automate or replicate intelligent behavior.
  6. Participating Member States agree to cooperate on: Boosting Europe's technology and industrial capacity in AI and its uptake, including better access to public sector data; these are essential conditions to influence AI development, fueling innovative business models and creating economic growth and new qualified jobs; Addressing socio-economic challenges, such as the transformation of the labour markets and modernising Europe's education and training systems, including upskilling & reskilling EU citizens; 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.