Der Siegeszug der Künstlichen Intelligenz und disruptiver Technologien scheint unaufhaltsam. Aber was heißt das für unsere Gesellschaft, den Arbeitsmarkt sowie ethische Grundkonstanten? Muss der Gesetzgeber tätig werden? Diesen Fragen ging unser Seminar an der TU Berlin auf den Grund.

1. TU Berlin, Masterstudiengang Wissenschaftsmarketing
Modul Public Affairs
Dr. Hans Bellstedt/Alice Buckley - hbpa GmbH
Berlin, October 2017

2. • Disruptive Technologies: Machine – platform – crowd
• Questions for government:
- Privacy
- Cyber Security
- Liability
- Employment
- Ethics and moral
• From Cyber Security to IP Reform:
How the German government has responded so far
• Topics to be tackled: An agenda for „Jamaica“
• Questions for Public Affairs professionals
Seite 2

3. Internet of things
Artificial intelligence
Robotics
3D/bio printing
Gene editing
Big data
Encryption
Virtual/augmented reality
Cloud computing
Facial recognition
Autonomous vehicles
Seite 3
Platform economy
Bitcoin/Blockchain tech

4. Machine (vs. mind) Platform (vs. product) Crowd (vs. core) *)
• AI (machine
learning/pattern
recognition)
• Automated, data-
driven, bias-resistent
decision making
• Autonomous vehicles
• Internet of things
• Robots, sensors,
drones…
• AR/VR
• Facebook, Google, Whats
App
• Netflix, Spotify…
• Ride-hailing services (Uber,
Lyft)
• AirBnB
• Booking.com (priceline)
• Delivery Hero,
takeaway.com
• Alibaba
• Linux/Open Source
• Wikipedia
• Crowdfunding (e.g
kickstarter)
• Crowdlending (peer-to-
peer)
• BitCoin
• Blockchain (distributed
ledger)
*) taken from: A. Mac Afee, E. Brynjolfsson,
Machine – Platform – Crowd. Harnessing our
digital future, 2017
Seite 4

5. Google‘s AlphaGo AI programme becomes the first to beat Go world champion Lee Sedol, March 2016

6. 8098
7541
7366
4680
4201
3714
3656
3567
3170
2473
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Static image recognition, classification and tagging
Algorithmic trading strategy performance improvement
Efficient, scalable processing of patient data
Predictive maintenance
Object identification, detection, classification, tracking
Text query of images
Automated geophysical feature detection
Content distribution on social media
Object detection and classification - avoidance, navigation
Prevention against cybersecurity threats
US dollars (millions)
Source: Statistica Charts, 2016

7. 104 231
510
1126
2487
5494
0
1000
2000
3000
4000
5000
6000
2015 2016 2017 2018 2019 2020
Numberofcars
Remote
valet
assistant
Highway
autopilot
with lane-
changing
User
operated
Source: BI Intelligence Estimates, 2015

8. • „To regulate, or not to regulate…“
• How do we regulate new technologies without stifling innovation?
• At what level should regulations be made – regional, national,
international? How do we ensure cooperation on this?
• How can government keep up with the rapid pace of technological
development?
• How can we promote a wider understanding of these new technologies?
• How can we safeguard future employment and well-being?
• How can we ensure access to the internet for everyone?
• How can we prevent machines from taking over control?
“AI is a rare case where I think we
need to be proactive in regulation
instead of reactive…
There will certainly will be job
disruption.
Because what’s going to happen is
robots will be able to do everything
better than us… I mean all of us.”
Elon Musk
(Tesla,Hyper-
loop, Space-
X)
Seite 8

9. • Privacy
• Cyber Security
• Liability
• Employment
• Ethics and moral
Let‘s have a closer look…
Seite 9

10. Areas to watch Challenges How to respond?
• Big data
• Data analytics
• Facial/voice recognition
• Autonomous vehicles
• Encryption
• Internet of things
• User‘s increasing dependence on
digital applications
• „Consumer‘s dilemma“: personal
data are the price to pay…
• Data misuse, privacy violation
Plus:
• (Mis-)Use of Whatsapp by
terrorists
• (Mis-)Use of new technologies by
authoritarian regimes (threat of
persecution)
• Create awareness, promote
better understanding of data
protection and privacy
amongst users
• Privacy by design/by default
– work with industry to
achieve this
• Enhance consumer
protection rights, right of
action (Klagerechte)
• Simplify „terms & conditions“
(AGB)
• Foster cross-border solutions
Seite 10

11. Source, New Rules of Customer Engagement Study 2016, based on a poll of over 18,000 customers in nine countries
63
61
56
49 49
44
41 41
28
0
10
20
30
40
50
60
70
UK Germany France USA Australia Netherlands South Africa New Zealand Poland
Percentageofsurveyrespondentswhoagree

12. Areas to watch Challenges How to respond?
• Large networks/grids
(telco, energy, transport)
• Autonomous vehicles
• Internet of things
• Cloud computing
• Bitcoin
• E-health
• Cyber attacks
• Hackers
• Data theft, misuse
• Digital currency security
• Tax evasion/fraud
• Define and protect „critical
infrastructures“ (networks)
• Invest in infrastructure
protection (e.g. firewalls)
• Promote cybersecurity training
• Increase awareness among
employees
• Enhance cross-border
regulation
Seite 12

13. 69
56 56 55
51
67
59
56 55 53
73
68 66 66 64
0
10
20
30
40
50
60
70
80
Changing nature of
threats (internal and
external)
Other priorities taking
precedence over
security
Day-to-day tactical
activities taking up too
much time
Complexity of
technology
environment
Lack of security
employees with the
right skills
Percentageofrespondentswhoagree
Germany UK US
Source: Survey conducted by Forrester Consulting on behalf of Hiscox, November – December 2016

14. Areas to watch Challenges How to respond?
• Autonomous vehicles
• Internet of things
• Robots, drones
• Bitcoin
• Liability in case of an
accident (Cars, robots,
drones)
• Autonomous cars: who
owns the data?
• Liability in case of
production breakdown or
power cut
• Transaction verification
(Bitcoins)
• Clearance between
Automotive, software
suppliers & platform
operators
• Review & adapt insurance
industry business model
• Back DLT (blockchain to
record translations)
Seite 14

15. 1514
1942
1147 1158
1270
1615
303
388
229 232 254
323
0
500
1000
1500
2000
2500
BMW 335 Tesla Model S Lexus RX 450h Honda Accord Toyota Prius Porsche Panamera
USDollars
Human-driven Autonomous
Source: Metro Mile, 2015

16. Areas to watch Challenges How to respond?
• AI and machine
learning
• Robotics
• Robo Advisory
• 3D printing
• Autonomous
vehicles
• Potential negative impact on
employment
• Fundamental change to the
way human labour is valued
• Taxation, social security
contributions and distribution
of wealth
• Implications for state welfare
support – moves towards a
universal/basic income
model? (from AI to BI…?)
• Establish early-on the
disrupting effects of emerging
technologies
• Focus on job-creating,
productivity-enhancing aspects
• Promote mandatory
upskilling/teaching programs
funded by firms
• Review/Update school
curricula
• …Identify non-codable jobs (!)
 „step up, step aside, step in“ (Julia
Kirby, Harvard Univ Press)
Seite 16

17. Source: Dauth, W, S Findeisen, J Suedekum and N Woessner (2017), “German robots – The impact of industrial robots on workers”,
CEPR Discussion Paper 12306.

18. Areas to watch Challenges How to respond?
• Gene editing
• Bio printing
• AI
• Affective computing (i.e. the
ability of machines to have/to
understand emotion)
• Virtual reality
• Augmented reality
• Impact of machines on
humanity and human
behaviour
• AI bias / prejudices (risk of
discrimination)
• Ambitions to „fight death“
(Peter Thiel)/life
prolongation research
• Robots going crazy
• Cross-sector
collaboration –
government, academia,
industry
• Enhanced public debate
• Redefine ethical
standards (?)
• Robots‘ „driving licence“
Seite 18

19. Seite 19
• Straßenverkehrsgesetz-Reform 2017 (Road Traffic Act, amended to adress autonomous driving);
Ethics commission on Autonomous Driving
• Drones directive (Drohnen Verordnung 2017)
• IT Security Act (2015), KRITIS Directives 2016/17
• Weißbuch Arbeiten 4.0 (Employment white paper)
• 9. GWB-Novelle 2016 (Anti-trust law, amended to avoid monopolies in platform economy)
• EU Data protection directive transformed into national law (2017)
• Netzwerk-Durchsetzungsgesetz 2017 (Anti-Hate Speech/Fake News legislation)
• Unterlassungsklagerecht von Verbraucherschutzverbänden gegen Datenschutzverstöße (2016)
• Urheberrecht in der Wissenschaftsgesellschaft – Reform 2017 (Intellectual Property Rights)
• Buchpreisbindung auch für E-books (price fixation for E-books) - 2016
• FinTechRat (FinTech Advisory Board to Ministry of Finance, est. 2017); FinCamp Events (2016)

20. • ‘Jamaica’ coalition must pro-actively address the impacts associated with “disruption” and decide
if – and how – to “tame the machines”.
• There are many issues yet to be addressed, e.g. AI, Blockchain, 3D-printing, VR/AR, face
recognition (dashcams), gene editing…  the “next big things”!
• Between Christian Democrats, Christian Social Union, Free Democrats and Greens, tackling
digital disruption will not be an easy ride…:
- Areas of likely agreement: Digital infrastructure (broadband, 5G), education (“Bildungs-
/Schul-Cloud”), widening the debate on tech
- Areas of potential conflict: Data-based economy vs. further data protection, employment
(basic income?), ethics, Intellectual Property rights (proprietary vs. open/crowd)
• A. Merkel: “Digital revolution also requires global rules”, such as in trade (WTO, G20, EU).
Seite 20

21. • With whom should PA firms be engaging? (Industry
leading the way in many cases, e.g. AI partnership
formed by Google, Facebook, IBM, Microsoft and
Amazon)
• Is it enough to stick to just one country, or do we
need to take a more international approach to
advocacy?
• How do PA firms ensure to have the knowledge to
lobby in such tech-driven areas?
• How will new technologies change the way in which
government itself operates (e.g. Big Data)? And what
about politics, e.g. use of data analytics in election
campaign?
John Manzoni, CEO of
the UK Civil Service
“Data is at the heart of 21st century
government... It makes government
work for everyone, by better
reflecting the world that we live in.”
“If communication consultants want to
remain impactful and relevant in the
21st century, then they should be hiring
experts in the fields of machine
learning, data and computer science.”
Maurice Cousins, Westbourne
Communications (UK)Seite 21

22. Contact:
Dr. Hans Bellstedt, hb@hbpa.eu