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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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
on
den
tsw
ho
agre
e
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
1500
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, [email protected]