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TU Berlin, Masterstudiengang Wissenschaftsmarketing
Modul Public Affairs
Dr. Hans Bellstedt/Alice Buckley - hbpa GmbH
Berl...
• Disruptive Technologies: Machine – platform – crowd
• Questions for government:
- Privacy
- Cyber Security
- Liability
-...
Internet of things
Artificial intelligence
Robotics
3D/bio printing
Gene editing
Big data
Encryption
Virtual/augmented rea...
Machine (vs. mind) Platform (vs. product) Crowd (vs. core) *)
• AI (machine
learning/pattern
recognition)
• Automated, dat...
Google‘s AlphaGo AI programme becomes the first to beat Go world champion Lee Sedol, March 2016
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Static image recognition,...
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2487
5494
0
1000
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2015 2016 2017 2018 2019 2020
Numberofcars
Remote
valet
assista...
• „To regulate, or not to regulate…“
• How do we regulate new technologies without stifling innovation?
• At what level sh...
• Privacy
• Cyber Security
• Liability
• Employment
• Ethics and moral
Let‘s have a closer look…
Seite 9
Areas to watch Challenges How to respond?
• Big data
• Data analytics
• Facial/voice recognition
• Autonomous vehicles
• E...
Source, New Rules of Customer Engagement Study 2016, based on a poll of over 18,000 customers in nine countries
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61
56
4...
Areas to watch Challenges How to respond?
• Large networks/grids
(telco, energy, transport)
• Autonomous vehicles
• Intern...
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56 55 53
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68 66 66 64
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Changing nature of
threats (internal and
external)
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Areas to watch Challenges How to respond?
• Autonomous vehicles
• Internet of things
• Robots, drones
• Bitcoin
• Liabilit...
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BMW 335 Tesla Model S Lexus RX 450h Honda ...
Areas to watch Challenges How to respond?
• AI and machine
learning
• Robotics
• Robo Advisory
• 3D printing
• Autonomous
...
Source: Dauth, W, S Findeisen, J Suedekum and N Woessner (2017), “German robots – The impact of industrial robots on worke...
Areas to watch Challenges How to respond?
• Gene editing
• Bio printing
• AI
• Affective computing (i.e. the
ability of ma...
Seite 19
• Straßenverkehrsgesetz-Reform 2017 (Road Traffic Act, amended to adress autonomous driving);
Ethics commission o...
• ‘Jamaica’ coalition must pro-actively address the impacts associated with “disruption” and decide
if – and how – to “tam...
• With whom should PA firms be engaging? (Industry
leading the way in many cases, e.g. AI partnership
formed by Google, Fa...
Contact:
Dr. Hans Bellstedt, hb@hbpa.eu
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"Taming the machine" - Wie regulieren wir disruptive Technologien?

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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.

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"Taming the machine" - Wie regulieren wir disruptive Technologien?

  1. 1. TU Berlin, Masterstudiengang Wissenschaftsmarketing Modul Public Affairs Dr. Hans Bellstedt/Alice Buckley - hbpa GmbH Berlin, October 2017
  2. 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. 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. 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. 5. Google‘s AlphaGo AI programme becomes the first to beat Go world champion Lee Sedol, March 2016
  6. 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. 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. 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. 9. • Privacy • Cyber Security • Liability • Employment • Ethics and moral Let‘s have a closer look… Seite 9
  10. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 22. Contact: Dr. Hans Bellstedt, hb@hbpa.eu

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