TU Berlin, Masterstudiengang WissenschaftsmarketingModul Public AffairsDr. Hans Bellstedt/Alice Buckley - hbpa GmbHBerlin, October 2017

• 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

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Internet of things

Artificial intelligence

Robotics

3D/bio printingGene editing

Big data

Encryption

Virtual/augmented reality

Cloud computing

Facial recognition

Autonomous vehicles

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Platform economy

Bitcoin/Blockchain tech

Machine (vs. mind) Platform (vs. product) Crowd (vs. core) *)

• AI (machinelearning/patternrecognition)

• Automated, data-driven, bias-resistentdecision making

• Autonomous vehicles• Internet of things• Robots, sensors,

drones…• AR/VR

• Facebook, Google, WhatsApp

• 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 ourdigital future, 2017

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

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

104 231510

1126

2487

5494

0

1000

2000

3000

4000

5000

6000

2015 2016 2017 2018 2019 2020

Nu

mb

er

of

cars

Remote valetassistant

Highway autopilotwith lane-changing

User operated

Source: BI Intelligence Estimates, 2015

• „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 technologicaldevelopment?

• 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)

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• Privacy• Cyber Security• Liability• Employment• Ethics and moral

Let‘s have a closer look…

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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 ofpersecution)

• Create awareness, promote better understanding of dataprotection and privacyamongst users

• Privacy by design/by default– work with industry toachieve this

• Enhance consumerprotection rights, right ofaction (Klagerechte)

• Simplify „terms & conditions“ (AGB)

• Foster cross-border solutions

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Source, New Rules of Customer Engagement Study 2016, based on a poll of over 18,000 customers in nine countries

6361

56

49 49

4441 41

28

0

10

20

30

40

50

60

70

UK Germany France USA Australia Netherlands South Africa New Zealand Poland

Perc

enta

geo

fsu

rvey

resp

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den

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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 „criticalinfrastructures“ (networks)

• Invest in infrastructureprotection (e.g. firewalls)

• Promote cybersecurity training• Increase awareness among

employees• Enhance cross-border

regulation

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69

56 56 5551

67

5956 55 53

7368 66 66 64

0

10

20

30

40

50

60

70

80

Changing nature ofthreats (internal and

external)

Other priorities takingprecedence over

security

Day-to-day tacticalactivities taking up too

much time

Complexity oftechnology

environment

Lack of securityemployees with the

right skills

Perc

enta

geo

fre

spo

nd

ents

wh

oag

ree

Germany UK US

Source: Survey conducted by Forrester Consulting on behalf of Hiscox, November – December 2016

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: whoowns the data?

• Liability in case ofproduction breakdown orpower cut

• Transaction verification(Bitcoins)

• Clearance betweenAutomotive, softwaresuppliers & platformoperators

• Review & adapt insuranceindustry business model

• Back DLT (blockchain torecord translations)

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1514

1942

1147 11581270

1615

303388

229 232 254323

0

500

1000

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2000

2500

BMW 335 Tesla Model S Lexus RX 450h Honda Accord Toyota Prius Porsche Panamera

US

Do

llars

Human-driven Autonomous

Source: Metro Mile, 2015

Areas to watch Challenges How to respond?

• AI and machinelearning

• Robotics• Robo Advisory• 3D printing• Autonomous

vehicles

• Potential negative impact on employment

• Fundamental change to theway human labour is valued

• Taxation, social securitycontributions and distributionof wealth

• Implications for state welfaresupport – moves towards a universal/basic incomemodel? (from AI to BI…?)

• Establish early-on thedisrupting effects of emergingtechnologies

• Focus on job-creating, productivity-enhancing aspects

• Promote mandatoryupskilling/teaching programsfunded by firms

• Review/Update schoolcurricula

• …Identify non-codable jobs (!)

„step up, step aside, step in“ (Julia

Kirby, Harvard Univ Press)Seite 16

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

Areas to watch Challenges How to respond?

• Gene editing• Bio printing• AI• Affective computing (i.e. the

ability of machines to have/tounderstand emotion)

• Virtual reality• Augmented reality

• Impact of machines on humanity and human behaviour

• AI bias / prejudices (risk ofdiscrimination)

• Ambitions to „fight death“ (Peter Thiel)/lifeprolongation research

• Robots going crazy

• Cross-sectorcollaboration –government, academia, industry

• Enhanced public debate• Redefine ethical

standards (?)• Robots‘ „driving licence“

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• 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)

• ‘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).

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• 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, WestbourneCommunications (UK)Seite 21

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