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Societal, Economic, Ethical and Legal Challenges of the Digital Revolution: From Big Data to Deep Learning, Artificial Intelligence, and Manipulative Technologies

Abstract

In the wake of the on-going digital revolution, we will see a dramatic transformation of our economy and most of our societal institutions. While the benefits of this transformation can be massive, there are also tremendous risks to our society. After the automation of many production processes and the creation of self-driving vehicles, the automation of society is next. This is moving us to a tipping point and to a crossroads: we must decide between a society in which the actions are determined in a top-down way and then implemented by coercion or manipulative technologies (such as personalized ads and nudging) or a society, in which decisions are taken in a free and participatory way and mutually coordinated. Modern information and communication systems (ICT) enable both, but the latter has economic and strategic benefits. The fundaments of human dignity, autonomous decision-making, and democracies are shaking, but I believe that they need to be vigorously defended, as they are not only core principles of livable societies, but also the basis of greater efficiency and success.

“Those who surrender freedom for security (I would add “efficiency” or “performance” here as well) will not have, nor do they deserve, either one…”

Benjamin Franklin

Keywords

  • Manipulative Technologies
  • Super-intelligent Machines
  • FuturICT
  • Planetary Nervous System
  • Informational Self-determination

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This chapter by Dirk Helbing reprints the article Societal, Economic, Ethical and Legal Challenges of the Digital Revolution, published first on 21 May 2015 in Jusletter IT (weblaw.ch) (reprinted with permission).

This document includes and reproduces some paragraphs of the following documents: “Big Data—Zauberstab und Rohstoff des 21. Jahrhunderts” published in Die Volkswirtschaft—Das Magazin für Wirtschaftspolitik (5/2014), see http://www.dievolkswirtschaft.ch/files/editions/201405/pdf/04_Helbing_DE.pdf; for an English translation see chapter 7 of D. Helbing (2015) Thinking Ahead—Essays on Big Data, Digital Revolution, and Participatory Market Society (Springer, Berlin).

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Notes

  1. 1.

    McKinsey and Co. Open data: Unlocking innovation and performance with liquid information, http://www.mckinsey.com/insights/business_technology/open_data_unlocking_innovation_and_performance_with_liquid_information

  2. 2.

    http://opensource.com/government/13/7/open-data-charter-g8 http://ec.europa.eu/digital-agenda/en/news/eu-implementation-g8-open-data-charter http://ec.europa.eu/information_society/newsroom/cf/dae/document.cfm?doc_id=3489

  3. 3.

    Chris Anderson, The End of Theory: The Data Deluge Makes the Scientific Method Obsolete. WIRED Magazine 16.07, http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory

  4. 4.

    One of the leading experts in this field is Jürgen Schmidhuber.

  5. 5.

    Jeremy Howard, The wonderful and terrifying implications of computers that can learn, TEDx Brussels, http://www.ted.com/talks/jeremy_howard_the_wonderful_and_terrifying_implications_of_computers_that_can_learn

  6. 6.

    The point in time when this happens is sometimes called “singularity”, according to Ray Kurzweil.

  7. 7.

    Süddeutsche (11.3.2015) Roboter als Chef, http://www.sueddeutsche.de/leben/roboter-am-arbeitsplatz-billig-freundlich-klagt-nicht-1.2373715

  8. 8.

    Such movies often serve to familiarize the public with new technologies and realities, and to give them a positive touch (including “Big Brother”).

  9. 9.

    James Barrat (2013) Our Final Invention— Artificial Intelligence and the End of the Human Era (Thomas Dunne Books). Edge Question 2015: What do you think about machines that think? http://edge.org/annual-question/what-do-you-think-about-machines-that-think

  10. 10.

    http://www.theguardian.com/technology/2014/oct/27/elon-musk-artificial-intelligence-ai-biggest-existential-threat

  11. 11.

    Nick Bostrom (2014) Superintelligence: Paths, Dangers, Strategies (Oxford University Press).

  12. 12.

    http://www.bbc.com/news/technology-30290540

  13. 13.

    http://www.cnet.com/news/bill-gates-is-worried-about-artificial-intelligence-too/

  14. 14.

    http://www.washingtonpost.com/blogs/the-switch/wp/2015/03/24/apple-co-founder-on-artificial-intelligence-the-future-is-scary-and-very-bad-for-people/

  15. 15.

    D. Helbing (2015) Distributed Collective Intelligence: The Network Of Ideas, http://edge.org/response-detail/26194

  16. 16.

    http://www.scmp.com/lifestyle/technology/article/1728422/head-chinas-google-wants-country-take-lead-developing, http://www.wantchinatimes.com/news-subclass-cnt.aspx?id=20150307000015&cid=1101

  17. 17.

    For example, the following approach seems superior to what Google Flu Trends can offer: D. Brockmann and D. Helbing, The hidden geometry of complex, network-driven contagion phenomena. Science 342, 1337–1342 (2013).

  18. 18.

    T. Preis, H.S. Moat, and H.E. Stanley, Quantifying trading behavior in financial markets using Google Trends. Scientific Reports 3: 1684 (2013).

  19. 19.

    R.H. Thaler and C.R. Sunstein (2009) Nudge (Penguin Books).

  20. 20.

    Süddeutsche (11.3.2015) Politik per Psychotrick, http://www.sueddeutsche.de/wirtschaft/verhaltensforschung-am-buerger-politik-per-psychotrick-1.2386755

  21. 21.

    For example, many Big Data companies (even big ones) don’t make large profits and some are even making losses. Making big money often requires to bring a Big Data company to the stock market, or to be bought by another company.

  22. 22.

    M. Gill and A. Spriggs: Assessing the impact of CCTV. Home Office Research, Development and Statistics Directorate (2005), https://www.cctvusergroup.com/downloads/file/Martin%20gill.pdf; see also BBC News (August 24, 2009) 1000 cameras ‘solve one crime’, http://news.bbc.co.uk/2/hi/uk_news/england/london/8219022.stm

  23. 23.

    Journalist’s Resource (November 6, 2014) The effectiveness of predictive policing: Lessons from a randomized controlled trial, http://journalistsresource.org/studies/government/criminal-justice/predictive-policing-randomized-controlled-trial. ZEIT Online (29.3.2015) Predictive Policing—Noch hat niemand bewiesen, dass Data Mining der Polizei hilft, http://www.zeit.de/digital/datenschutz/2015-03/predictive-policing-software-polizei-precobs

  24. 24.

    The Washington Post (January 12, 2014) NSA phone record collection does little to prevent terrorist attacks, group says, http://www.washingtonpost.com/world/national-security/nsa-phone-record-collection-does-little-to-prevent-terrorist-attacks-group-says/2014/01/12/8aa860aa-77dd-11e3-8963-b4b654bcc9b2_story.html?hpid=z4; see also http://securitydata.newamerica.net/nsa/analysis

  25. 25.

    D.M. Lazer et al. The Parable of Google Flu: Traps in Big Data Analytics, Science 343, 1203–1205 (2014).

  26. 26.

    D. Helbing (2015) Thinking Ahead, Chapter 10 (Springer, Berlin). See also https://www.ftc.gov/news-events/events-calendar/2014/09/big-data-tool-inclusion-or-exclusionhttps://www.whitehouse.gov/issues/technology/big-data-reviewhttps://www.whitehouse.gov/sites/default/files/docs/Big_Data_Report_Nonembargo_v2.pdfhttp://www.wsj.com/articles/SB10001424052702304178104579535970497908560

  27. 27.

    This problem is related with the method of “geoscoring”, see http://www.kreditforum.net/kreditwuerdigkeit-und-geoscoring.html/

  28. 28.

    http://de.wikipedia.org/wiki/Volksz%C3%A4hlungsurteil

  29. 29.

    The Silicon Valley is well-known for this kind of culture.

  30. 30.

    A. Mazloumian et al. Global multi-level analysis of the ‘scientific food web’, Scientific Reports 3: 1167 (2013), http://www.nature.com/srep/2013/130130/srep01167/full/srep01167.html? message-global=remove

  31. 31.

    J. Schmieder (2013) Mit einem Bein im Knast—Mein Versuch, ein Jahr lang gesetzestreu zu leben (Bertelsmann).

  32. 32.

    Detlef Fetchenhauer, Six reasons why you should be moretrustful, TEDx Groningen, https://www.youtube.com/watch?v=gZlzCc57qX4

  33. 33.

    A. Diekmann, W. Przepiorka, and H. Rauhut, Lifting the veil of ignorance: An experiment on the contagiousness of norm violations, preprint http://cess.nuff.ox.ac.uk/documents/DP2011/CESS_DP2011_004.pdf

  34. 34.

    Remember that it takes about two decades for a human to be ready for responsible, self-determined behavior. Before, however, he/she may do a lot of stupid things (and this may actually happen later, too).

  35. 35.

    I. Kondor, S. Pafka, and G. Nagy, Noise sensitivity of portfolio selection under various risk measures, Journal of Banking & Finance 31(5), 1545–1573 (2007).

  36. 36.

    It’s quite insightful to have two phones talk to each other, using Apple’s Siri assistant, see e.g. this video: https://www.youtube.com/watch?v=WuX509bXV_w

  37. 37.

    http://www.brainyquote.com/quotes/quotes/m/mishaglenn564076.html, see also http://www.businessinsider.com/fbi-director-china-has-hacked-every-big-us-company-2014-10

  38. 38.

    For example, it recently became public that Facebook had run a huge experiment trying to manipulate people’s mood: http://www.theatlantic.com/technology/archive/2014/09/facebooks-mood-manipulation-experiment-might-be-illegal/380717/ This created a big “shit storm”: http://www.wsj.com/articles/furor-erupts-over-facebook-experiment-on-users-1404085840. However, it was also attempted to influence people’s voting behavior: http://www.nzz.ch/international/kampf-um-den-glaesernen-waehler-1.18501656 OkCupid even tried to manipulate people’s private emotions: http://www.theguardian.com/technology/2014/jul/29/okcupid-experiment-human-beings-dating It is also being said that each of our Web searches now triggers about 200 experiments.

  39. 39.

    J. Lorenz et al. How social influence can undermine the wisdom of crowd effect, Proceedings of the National Academy of Science of the USA 108 (22), 9020–9025 (2011); see also J. Surowiecki (2005) The Wisdom of Crowds (Anchor).

  40. 40.

    See Marc Smith’s analyses of political discourse with NodeXL: http://nodexl.codeplex.com/

  41. 41.

    M. Bloodgood and C. Callison-Burch, Using Mechanical Turk to build machine translation evaluation sets, http://www.cis.upenn.edu/ccb/publications/using-mechanical-turk-to-build-machine-translation-evaluation-sets.pdf

  42. 42.

    In an extreme case, this might even be a criminal act.

  43. 43.

    Interestingly, for IBM Watson (the intelligent cognitive computer) to work well, it must be fed with non-biased rather than with self-consistent information, i.e. pre-selecting inputs to get rid of contradictory information reduces Watson’s performance.

  44. 44.

    It seems, for example, that the attempts of the world’s superpower to extend its powers have rather weakened it: we are now living in a multi-polar world. Coercion works increasingly less. See the draft chapters of my book on the Digital Society at http://ssrn.com for more information.

  45. 45.

    even though one never knows before what kinds of ideas and social mechanisms might become important in the future—innovation always starts with minorities.

  46. 46.

    C.A. Hidalgo et al. The product space conditions the development of nations, Science 317, 482–487 (2007). According to Jürgen Mimkes, economic progress (which goes along with an increase in complexity) also drives a transition from autocratic to democratic governance above a certain gross domestic product per capita. In China, this transition is expected to happen soon.

  47. 47.

    This is the main reason why one should support pluralism.

  48. 48.

    See the draft chapters of D. Helbing’s book on the Digital Society at http://www.ssrn.com, particular the chapter on the Complexity Time Bomb.

  49. 49.

    One might distinguish these into two types: dictatorships based on surveillance (“Big Brother”) and manipulatorships (“Big Manipulator”).

  50. 50.

    As digital weapons, so-called D-weapons, are certainly not less dangerous than atomic, biological and chemical (ABC) weapons, they would require international regulation and control.

  51. 51.

    see http://www.washingtonpost.com/blogs/the-switch/wp/2013/08/24/loveint-when-nsa-officers-use-their-spying-power-on-love-interests/

  52. 52.

    http://openpds.media.mit.edu/

  53. 53.

    http://horizon-magazine.eu/article/open-data-could-turn-europe-s-digital-desert-digital-rainforest-prof-dirk-helbing_en.html, https://ec.europa.eu/digital-agenda/en/growth-jobs/open-innovation

  54. 54.

    http://www.defenseone.com/technology/2015/04/can-military-make-prediction-machine/109561/

  55. 55.

    D. Helbing and S. Balietti, From social data mining to forecasting socio-economic crises, Eur. Phys. J Special Topics 195, 3–68 (2011); see also http://www.zeit.de/digital/datenschutz/2015-03/datenschutzverordnung-zweckbindung-datensparsamkeit; http://www.google.com/patents/US8909546

  56. 56.

    https://www.youtube.com/watch?v=mO-3yVKuDXs, https://www.youtube.com/watch?v=KgVBob5HIm8

  57. 57.

    Note that the scientific field of complexity science has a large fundus of knowledge how to reach globally coordinated results based on local interactions.

  58. 58.

    After all, humans have to register, too.

  59. 59.

    E. Pariser (2012) The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think (Penguin).

  60. 60.

    Some problems are so hard that no government and no company in the world have solved them (e.g. how to counter climate change). Large multi-national companies are often surprisingly weak in delivering fundamental innovations (probably because they are too controlling). That’s why they keep buying small and medium-sized companies to compensate for this problem.

  61. 61.

    Similar problems are known for software products that are used by billions of people: a single software bug can cause large-scale problems—and the worrying vulnerability to cyber attacks is further increasing.

  62. 62.

    We have demonstrated such an approach in the Virtual Journal platform (http://vijo.inn.ac).

  63. 63.

    In fact, to avoid mistakes, the more we are flooded with information the more must we be able to rely on it, as we have increasingly less time to judge its quality.

  64. 64.

    This could end up in a way of organizing our society that one could characterize as “Big Manipulator” (to be distinguished from “Big Brother”).

  65. 65.

    The following recent newspaper articles support this conclusion:http://www.zeit.de/politik/ausland/2015-03/china-wachstum-fuenf-vor-acht,http://bazonline.ch/wirtschaft/konjunktur/China-uebernimmt-die-rote-Laterne/story/20869017, http://www.nzz.ch/international/asien-und-pazifik/singapurer-zeitrechnung-ohne-lee-kuan-yew-1.18510938. In fact, based on a statistical analysis of Jürgen Mimkes and own observations, I expect that China will now undergo a major transformation towards a more democratic state in the coming years. First signs of instability of the current autocratic system are visible already, such as the increased attempts to control information flows.

  66. 66.

    D. Helbing, Responding to complexity in socio-economic systems: How to build a smart and resilient society? Preprint http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2583391

  67. 67.

    D. Helbing, Creating (“Making”) a Planetary Nervous System as Citizen Web, http://futurict.blogspot.jp/2014/09/creating-making-planetary-nervous.html

  68. 68.

    L.M.A. Bettencourt et al. Growth, innovation, scaling, and the pace of life in cities, Proceedings of the National Academy of Sciences of the USA 104, 7301–7306 (2007).

  69. 69.

    See D. Helbing, Globally networked risks and how to respond. Nature 497, 51–59 (2013). Due to the problem of the Complexity Time Bomb (http://papers.ssrn.com/sol3/papers.cfm? abstract_id=2502559), we must either decentralize our world, or it will most likely fragment, i.e. break into pieces, sooner or later.

  70. 70.

    Having a greater haystack does not make it easier to find a needle in it.

  71. 71.

    This is particularly well-known for the problem of ambiguity. For example, a lot of jokes are based on this principle.

  72. 72.

    M. Maes and D. Helbing, Noise can improve social macro-predictions when micro-theories fail, preprint.

  73. 73.

    We know this also from so-called “phantom traffic jams”, which appear with no reason, when the car density exceeds a certain critical value beyond which traffic flow becomes unstable. Such phantom traffic jams could not be predicted at all by knowing all drivers thoughts and feelings in detail. However, they can be understood for example with macro-level models that do not require micro-level knowledge. These models also show how traffic congestion can be avoided: by using driver assistance systems that change the interactions between cars, using real-time information about local traffic conditions. Note that this is a distributed control strategy.

  74. 74.

    Assume one knows the psychology of two persons, but then they accidentally meet and fall in love with each other. This incident will change their entire lives, and in some cases it will change history too (think of Julius Caesar and Cleopatra, for example, but there are many similar cases). A similar problem is known from car electronics: even if all electronic components have been well tested, their interaction often produces unexpected outcomes. In complex systems, such unexpected, “emergent” system properties are quite common.

  75. 75.

    In case of cascade effects, a local problem will cause other problems before the system recovers from the initial disruption. Those problems trigger further ones, etc. Even hundreds of policemen could not avoid phantom traffic jams from happening, and in the past even large numbers of security forces have often failed to prevent crowd disasters (they have sometimes even triggered or deteriorated them while trying to avoid them), see D. Helbing and P. Mukerji, Crowd disasters as systemic failures: Analysis of the Love Parade disaster, EPJ Data Science 1:7 (2012).

  76. 76.

    I am personally convinced that the level of randomness and unpredictability in a society is relatively high, because it creates a lot of personal and societal benefits, such as creativity and innovation. Also think of the success principle of serendipity.

  77. 77.

    D. Helbing et al. FuturICT: Participatory computing to understand and manage our complex world in a more sustainable and resilient way. Eur. Phys. J. Special Topics 214, 11–39 (2012).

  78. 78.

    As we know, intellectual discourse can be a very effective way of producing new insights and knowledge.

  79. 79.

    Due to the data deluge, the existing amounts of data increasingly exceed the processing capacities, which creates a “flashlight effect”: while we might look at anything, we need to decide what data to look at, and other data will be ignored. As a consequence, we often overlook things that matter. While the world was busy fighting terrorism in the aftermath of September 11, it did not see the financial crisis coming. While it was focused on this, it did not see the Arab Spring coming. The crisis in Ukraine came also as a surprise, and the response to Ebola came half a year late. Of course, the possibility or likelihood of all these events was reflected by some existing data, but we failed to pay attention to them.

  80. 80.

    The classical telematics solutions based on a control center approach haven’t improved traffic much. Today’s solutions to improve traffic flows are mainly based on distributed control approaches: self-driving cars, intervehicle communication, car-to-infrastructure communication etc.

  81. 81.

    This approach corresponds exactly how Big Data are used at the elementary particle accelerator CERN; 99.9% of measured data are deleted immediately. One only keeps data that are required to answer a certain question, e.g. to validate or falsify implications of a certain theory.

  82. 82.

    J. van den Hoven et al. FuturICT—The road towards ethical ICT, Eur. Phys. J. Special Topics 214, 153–181 (2012).

  83. 83.

    This probably requires different levels of access depending on qualification, reputation, and merit.

  84. 84.

    J. Rifkin (2013) The Third Industrial Revolution (Palgrave Macmillan Trade); J. Rifkin (2014) The Zero Marginal Cost Society (Palgrave Macmillan Trade).

  85. 85.

    Government 3.0 initiative of the South Korean government, http://www.negst.com.ng/documents/Governing_through_Networks/3-icegov2013_submission_19.pdfhttp://www.koreaittimes.com/story/32400/government-30-future-opening-sharing-communication-and-collaboration

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Helbing, D. (2019). Societal, Economic, Ethical and Legal Challenges of the Digital Revolution: From Big Data to Deep Learning, Artificial Intelligence, and Manipulative Technologies. In: Helbing, D. (eds) Towards Digital Enlightenment. Springer, Cham. https://doi.org/10.1007/978-3-319-90869-4_6

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