WITHOUT FREEDOM, WE ARE NO LONGER CITIZENS

Guest Post By Vincenzo Pavone [1] (IPP-CSIC) and Elvira Santiago (IPP-CSIC)

Big Brother 2.0?

No doubt, after the Snowden revelations and the recent confrontation between Germany and the US, several citizens will be asking themselves whether their private communications are under surveillance, and to what extent. This very event has triggered intensive debate in the media and in the political arenas of several European countries not only about the extent and purpose of the surveillance programs, but also about one of the technologies that are being used to arrange such surveillance: that is Big Data. Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision-making.[2]

It must be acknowledged that there are different ways of using Big Data, and that the application of this set of technologies does not necessarily need to be oriented towards the surveillance of individual citizens. For instance, data can be anonymised, which allows research to be conducted and data to be extracted and analyzed without preserving any link between the data and the citizens to whom these data are related.

Is technology neutral?

However, this is not equal to saying that Big Data is a neutral technology. Any technology, regardless of how many uses it may have, is never neutral. Technologies, or rather sociotechnical practices, can never be understood as stand alone pieces of human art crafts because they only work and make sense in a network of socially constructed meanings, practices, organizational protocols and tailor-made jargons. They come with their own ethics, their own values. These values reflect the dominant political priorities and ethical values of the societal stakeholders producing and using such technologies. They may indeed change over time, but they will do so along with the changes occurring in the society adopting, sustaining and implementing such sociotechnical practices.   

This is, in a few words, the basic assumption proposed by what is known as a co-production approach in science and technology studies [3]: science and social order are co-produced and they live in a mutually constitutive relationship. Producing new scientific knowledge, as well as the new technological tools stemming from such knowledge, produces new forms of social order, and the opposite is also true: in order to produce new forms of social order, new knowledge and technical tools are constantly fabricated.

Of cars and Big Data

An example, perhaps, may illustrate this better. If asked about the cost of a specific car, we would normally answer by pointing at the price of that car. But that is hardly the actual cost… or better that is the cost only if seen from a specific point of view, which externalizes all the real costs of a car and narrows the question down to the transaction between the car dealer and the potential customer. However, cars, as a technology, only make sense as part of a sophisticated network of sociotechnical practices that needs to be constantly maintained to ensure that cars can fully operate across a given space. Cars need roads, police, laws, speed cameras, hospital, doctors, insurance companies, mechanics, gasoline pumps, etc. Without these sociotechnical infrastructures and practices, a car is simply a meaningless, useless box with five seats and four wheels. All these infrastructures have a cost and we accept that most of these costs are to be paid collectively by the citizens, often via the public system of tax collection. And why do we do so? Because we believe that cars are a socially legitimate way to move around.

If that is true for cars, it is even more so for Big Data. The latter, as a sociotechnical practice of security, only can be understood in a society that understands security as a function of surveillance. This is why, in so far as security is concerned, Big Data could never be anonymised as it would not make sense to have millions of data proceeding from harmless citizens’ communication without having name and surname (and much more) on it. The question, thus, is not whether we are spied or not, but rather: how did we come to pursue a concept of security, where many seem to believe that the latter can only be increased through massive surveillance programs operated through Big Data technologies?

A paradigm shift concept of security?

While in the 1990s, human security was associated with human development, human rights and multilateralism, in the aftermath of the Twin Towers attack it has evolved into a new, encompassing term that questions the separation between internal and external security: religious fundamentalism, ethnic conflicts and guerrilla-type wars are sources of threats that can well come from inside the state borders [4]. As a result, internal and external security agendas have eventually merged together [5].  Drug-trafficking, undocumented migration, and economic crimes cease to be an issue of justice or social integration and, overloaded with urgency and exceptionality, get subject to a new security approach emphasizing threat anticipation.

In a regime of threat anticipation, risk assessment and risk management become the cornerstone of a comprehensive approach that is geared to constant detection and prevention of the threats and risks. In this new approach, security is expanded well beyond the criminal domain in order to cope with any sort of suspicious behavior, information or action that could potentially constitute a threat. The resulting securitization of people’s movements and actions cannot be confined to migrants: under the new concept of security, controlling and integrating all sorts of information about ordinary citizens is nothing but inexorable.

The constitutive role of security technologies

In this approach to security, surveillance-oriented security technologies, and the analysis of Big Data is one of them, play a constitutive role: they are part of a new social order. As it has become impossible to conceive security without technology, we are permanently exposed to a technological fix approach to the problem of security: the focus constantly shifts from the search for a (complex) variety of causes and factors that has produced the on-going transformation of security threats to (simple) series of technological remedies that could be conceived, developed and implemented to keep these challenges under control.

Inevitably, the successful deployment of new security technologies under this new holistic concept of security comes at a cost: a restriction of civil liberties and individual privacy. Security and liberty get framed as two interchangeable goods that could be traded against each other: any increase in security requires an equivalent contraction of civil liberties. As the increase of security levels is intrinsically associated with an ever-increasing implementation of surveillance technologies, it does not consider the possibility of increasing security levels through either non-surveillance-oriented technologies or through non-technological actions and interventions.

Without freedom, we are no longer citizens

This is how we got to the point where millions of citizens around the world are spied indiscriminately. However, once we have lost our privacy, we can no longer act, meet, communicate, share or express ourselves freely. Under surveillance, regardless of whether we have something to hide or not, we cannot enjoy our basic civil and political rights. It is in this context that we have to understand Big Data. They are key to the development and implementation to a specific vision of what needs to be promoted as social order. Needless to say, this specific view of a desirable social order is at the same time promoting and fostering the development and implementation of Big Data.

This is why developing such powerful technologies and then hope that a few parliamentary bills will prevent their full implementation is wishful thinking. Rather, we need to learn to conceive security in different terms, as a shared responsibility and not only a function of repressive and preventive surveillance. Social and economic factors such as social and cultural integration, welfare supports, rule of law, fair redistribution of resources and citizens’ participation are at least as important. We often hear that without security, citizens cannot be free. Sure, this is true. However, without freedom, no matter how safe, we are no longer citizens.

[1] Corresponding author: Vincenzo Pavone, Institute of Public Goods and Policies (IPP), Consejo Superior Investigaciones Científicas, Madrid, SPAIN. Vincenzo.pavone@csic.es

[2] Gartner Research. 2013. http://www.gartner.com/it-glossary/big-data/

[3] Jasanoff, S. 2004. States of knowledge: the co-production of science and social order: Psychology Press

[4] Lutterbeck, D. (2005) "Blurring the dividing line: The convergence of internal and external security in Western Europe," European Security 14(2): 231-53.

[5] Bigo, D. (2000) "Internal and external securitisations in Europe," International Relations Theory and European Integration: Power, Security and Community: 154.

SCIENTISTS MUST SPEARHEAD ETHICAL USE OF BIG DATA

Guest post from ALBERT-LÁSZLÓ BARABÁSI

The recent revelation that the National Security Agency collects the personal data of United States citizens, allies and enemies alike has broken the traditional model governing the bond between science and society.

Most breakthrough technologies have dual uses. Think of atomic energy and the nuclear bomb or genetic engineering and biological weapons. This tension never gives way. Our only hope to overcoming it is to stop all research.

But that is unrealistic. Instead, the model we scientists follow is simple: We need to be transparent about the potential use and misuse of our trade. We publish our results, making them accessible to everyone. And when we do see the potential for abuse, we speak up, urging society to reach a consensus on how to keep the good but outlaw the bad.

As the NSA secretly developed its unparalleled surveillance program, relying on a mixture of tools rooted in computer and social sciences, this model failed. Scientists whose work fueled these advances failed to forcefully articulate the collateral dangers their tools pose. And a political leadership, intoxicated by the power of these tools, failed to keep their use within the strict limits of the Constitution.

It’s easy to see why this happened. After all, the benefits of Big Data and the science behind it are hard to overlook. Beyond the many digital applications that make our life increasingly easy today, data science holds promise for emergency response and for stopping the next virus from turning into a deadly pandemic. It also holds the key to our personal health, since our activity patterns and disease history are more predictive of our future disease than our genes.

For researchers involved in basic science, like myself, Big Data is the Holy Grail: It promises to unearth the mathematical laws that govern society at large. Motivated by this challenge, my lab has spent much of the past decade studying the activity patterns of millions of mobile phone consumers, relying on call patterns provided by mobile phone companies. This data was identical to what NSA muscled away from providers, except that ours was anonymized, processed to help research without harming the participants. In a series of research papers published in the journals Science and Nature, my team confirmed the promise of Big Data by quantifying the predictability of our daily patterns, the threat digital viruses pose to mobile phones and even the reaction people have when a bomb goes off beside them.

We also learned that when it comes to our behavior, we can’t use only two scales — one for good and the other for bad. Rather, our activity patterns are remarkably diverse: For any act labeled “unusual” or “anomalous,” such as calling people at odd hours or visiting sensitive locations outside our predictable daily routine, we will find millions of individuals who do just that as part of their normal routine. Hence identifying terrorist intent is more difficult than finding a needle in a haystack — it’s more like spotting a particular blade of hay.

Let’s face it: Powered by the right type of Big Data, data mining is a weapon. It can be just as harmful, with long-term toxicity, as an atomic bomb. It poisons trust, straining everything from human relations to political alliances and free trade. It may target combatants, but it cannot succeed without sifting through billions of data points scraped from innocent civilians. And when it is a weapon, it should be treated like a weapon.

To repair the damage already done, we researchers, with a keen understanding of the promise and the limits of our trade, must work for a world that uses science in an ethical manner. We can look at the three pillars of nuclear nonproliferation as a model for going forward.

The good news is that the first pillar, the act of nonproliferation itself, is less pertinent in this context: Many of the technologies behind NSA’s spying are already in the public domain, a legacy of the openness of the scientific enterprise. Yet the other two pillars, disarmament and peaceful use, are just as important here as they were for nuclear disarmament. We must inspect and limit the use of this new science for military purposes and, to restore trust, we must promote the peaceful use of these technologies.

We can achieve this only in alliance with the society at large, together amending universal human rights with the right to data ownership and the right of safe passage.

Data ownership states that the data pertaining to my activity, like my browsing pattern, shopping habits or reading history, belongs to me, and only I control its use. Safe passage is the expectation that the information I choose to transfer will reach its intended beneficiaries without being tapped by countless electronic ears along the way. The NSA, by indiscriminately tapping all communication pipelines, has degraded both principles.

Science can counteract spying overreach by developing tools and technologies that, by design, lock in these principles. A good example of such a design is the Internet itself, built to be an open system to which anyone could connect without vetting by a central authority. It took decades for governments around the world to learn to censor its openness.

This summer, while visiting my hometown in Transylvania, I had the opportunity to talk with a neighbor who spent years as a political prisoner. Once freed, for decades to come, he knew that everything he uttered was listened to and recorded. He received transcripts of his own communications after the fall of communism. They spanned seven volumes. It was toxic and dehumanizing, a way of life that America has repeatedly denounced and fought against. 

So why are we beginning to spread communism 2.0 around the world, a quarter-century after the Iron Curtain’s collapse? This is effectively what NSA surveillance has become. If we scientists stay silent, we all risk becoming digitally enslaved. 

Posted with permission.

Albert-László Barabási is a physicist and network scientist at Northeastern University and Harvard Medical School, and the author of “Bursts: The Hidden Patterns Behind Everything We Do.”

A New Kind of Economy is Born

Social Decision-Makers Beat the "Homo Economicus"
by Dirk Helbing (ETH Zurich)

The Internet and Social Media change our way of decision-making. We are no longer the independent decision makers we used to be. Instead, we have become networked minds, social decision-makers, more than ever before. This has several fundamental implications. First of all, our economic theories must change, and second, our economic institutions must be adapted to support the social decision-maker, the "homo socialis", rather be tailored to the perfect egoist, known as "homo economicus".

The financial, economic and public debt crisis has seriously damaged our trust in mainstream economic theory. Can it really offer an adequate description of economic reality? Laboratory experiments keep questioning one of the main pillars of economic theory, the "homo economicus". They show that the perfectly self-regarding decision-maker is not the rule, but rather the exception [1,2]. And they show that markets, as they are organized today, are undermining ethical behavior [3].

Latest scientific results have shown that a "homo socialis" with other-regarding preferences will eventually result from the merciless forces of evolution, even if people optimize their utility, if offspring tend to stay close to their parents [4]. 1 

Another, independent study was recently summarized by the statement "evolution will punish you, if you're selfish and mean" [5]. Is this really true? And what implications would this have for our economic theory and institutions?

In fact, the success of the human species as compared to others results mainly from its social nature. There is much evidence that evolution has created different incentive systems, not just one: besides the desire to possess (in order to survive in times of crises), this includes sexual satisfaction (to ensure reproduction), curiosity and creativity (to explore opportunities and risks), emotional satisfaction (based on empathy), and social recognition (reputation, power). Already Adam Smith noted: "How ever selfish man may be supposed, there are evidently some principles in his nature, which interest him in the fortune of others, and render their happiness necessary to him, though he derives nothing from it." 2

Dirk Helbing, professor of sociology at ETH Zurich and complexity scientist concludes: "The social nature of man has dramatic implications, both for economic theory and for the way we need to organize our economy." As we are more and more connected with others, the "homo economicus", i.e. the independent decision-maker and perfect egoist, is no longer an adequate representation or good approximation of human decision-makers. "Reality has changed. We are applying an outdated theory, and that's what makes economic crises more severe," says Helbing.

Outdated theory, outdated institutions

In fact, recent experimental results suggest that the majority of decision-makers are of the type of a "homo socials" with equity- or equality-oriented fairness preferences [1,6]. The "homo socialis" is characterized by two features: interdependent decision-making that takes into account the impact on others and conditional cooperativeness. However, the "homo socialis" takes self-determined, free decisions. He is not ripping off others, afterwards giving back some of the benefits to others through taxes or philanthropy. The "homo socialis" decides rather differently, more considerately, recognizing that friendly and fair behavior can generate better outcomes for everybody.

"But social behavior is vulnerable to exploitation by the 'homo economicus'," continues Helbing. In a selfish environment, the 'homo socialis' cannot thrive. In other words, if the settings are not right, the 'homo socialis' behaves the same as the 'homo economicus'. "That's probably why we haven't noticed its existence for a long time," believes Helbing. "Our theories and institutions were tailored to the 'homo economicus', not to the 'homo socialis'." 

In fact, many of today's institutions, such as homogeneous markets with anonymous exchange, undermine cooperation in social dilemma situations, i.e. situations in which cooperation would be favorable for everyone, but non-cooperative behavior promises additional benefits [7, Fig. 2].

New institutions for a global information society

  

In the past we have built public roads, parks and museums, schools, libraries, universities, and homogeneous markets on a global scale. What would be suitable institutions for the 21st century? "Reputation systems can transfer the success principles of social communities to our globalized society, the global village", suggests Helbing. Most people and companies care about reputation. Therefore, reputation systems could support socially oriented decision-making and cooperation, with better outcomes for everyone [8]. In fact, reputation systems spread on the Web 2.0 like wildfire. People rate products, sellers, news, everything, be it at amazon, ebay, or trip adviser. We have become a "like it" generation, because we listen to what our friends like.

Importantly, recommender systems should not narrow down socio-diversity, as this is the basis of happiness, innovation and societal resilience. "We don't want to live in a filter bubble, where we don't get an objective picture of the world anymore," says Helbing with reference to Eli Pariser [9]. Therefore, reputation systems should be pluralistic, open, and user-centric. "Pluralistic reputation systems are oriented at the values and quality criteria of individuals," explains Helbing, "rather than recommending what a company's reputation filter thinks is best. Self-determination of the user is central. We must be able to use different filters, choose the filters ourselves, and modify them." The diverse filters would mine the ratings and comments that people leave on the Web, but also consider how much one trusts in certain information sources.

"Reputation creates benefits for buyers and sellers," says Helbing. A recent study shows that good reputation allows sellers to take a higher price, while customers can expect a better service [10]. Reputation systems may also promote better quality as well as socially and environmentally friendly production, suggests Helbing. "This could be a new approach to reach more sustainable production, based on self-regulation rather than enforcement by laws." One day, reputation systems may also be used to create a new kind of money, speculates Helbing. The value of "qualified money" would depend on it's reputation and thereby create incentives to invest in ways that increase a money unit's reputation. It might create a more adaptive financial system and help to mitigate the recurrent crises we are facing since hundreds of years. But the details still have to be worked out.

Benefits of a self-regulating economy

Reputation systems could overcome some of the unwanted side effects of anonymous exchange thanks to pseudonymous or personal interactions. Thereby, they could potentially counter "tragedies of the commons" such as global warming, environmental exploitation and degradation, overfishing, .. - constituting some of our major unsolved global problems. We can witness such kinds of "social dilemma problems" everywhere. So far, governments try to fix them with top-down regulations and punitive institutions. However, these are very expensive, and often quite ineffective. "Basically all industrialized countries suffer from exploding debts," says Helbing. "I believe we cannot pay for this much longer, we are at the limit. We need a new approach." As Albert Einstein pointed out: "We cannot solve our problems with the same kind of thinking that created them."

Institutions supporting the "homo socialis" such as suitably designed reputation systems would enable a self-regulation of socio-economic systems. "But self-regulation does not mean that everyone can choose the rules he likes," explains Helbing. "It only works with an other-regarding element. The self-regulation rules must be able to achieve a balance between the interests of everyone, who is affected by the externalities of a decision."

Helbing explains the benefits: "Other-regarding decisions can overcome the classical conflict between economic and social motives. Self-regulation could also overcome the struggle between the bottom-up organization of markets and the top-down regulation by politics. This would remove a lot of friction from our current system, making it much more efficient - in the same way as the transition from centrally planned economies to self-organized markets has often created huge efficiency gains."

This can be illustrated with an example from urban traffic management. "Traffic control is a problem where not everybody's desires can be satisfied immediately and at the same time, like in economic systems. It is a so-called NP-hard optimization problem - the computational effort explodes with system size, as for many economic optimization problems, e.g. in production and logistics." The study compares three kinds of control: A centralized top-down regulation by a traffic center, the classical control approach, and two decentralized control approaches. The first one assumes that each intersection independently minimizes the waiting times of approaching vehicles, as a "homo economicus" would do. The second one decides in an other-regarding way: it interrupts the minimization of waiting times, when this is needed to avoid spill-over effects at neighboring intersections. Helbing summarizes: "The 'homo economicus' approach works well up to a moderate utilization of intersections, but queue lengths get out of control long before the intersection capacity is reached. The bottom-up self-regulation based on the principle of the 'homo socialis' approach beats both, the centralized top-down regulation and the bottom-up self-organization based on principles of the 'homo economicus'. Other-regarding behavior improves the coordination among neighboring intersections. It makes Adam Smith principle of the 'invisible hand' work even at high utilizations."

Economics 2.0: Emergence of a participatory market society

But will such a self-regulating system ever be implemented? Helbing is convinced: "It's already on its way. The Web 2.0, in particular reputation systems and social media are driving the transition towards a new economy, the economy 2.0. We see already a new trend towards decentralized, local production and personalized products, enabled by 3D printers, app stores, and other technologies."

Such developments will eventually create a participatory market society. "Prosumers", i.e. co-producing consumers, the new "makers" movement, and the sharing economy are some examples illustrating this. "Just think of the success of Wikipedia, Open Streetmap or Github. Open Streetmap now provides the most up-to-date maps of the world, thanks to more than 1 million volunteers." Helbing stresses: "This is just the beginning of a new era. A new intellectual framework is emerging, and a creative and participatory era is ahead. The paradigm shift towards participatory bottom-up self-regulation may be bigger than the paradigm shift from a geocentric to a heliocentric worldview. If we build the right institutions for the information society of the 21st century, we will finally be able to mitigate some very old problems of humanity. 'Tragedies of the commons' are just one of them. After so many centuries, they are still plaguing us, but this needn't be."

---

[1] Experts should note that there has been research on so-called "altruistic behavior" in social dilemma situations such as the prisoner's dilemma since more than 3 decades. However, if scientists would have understood the "homo socialis" with other-regarding preferences already before, the key concept of the "homo economicus" should have disappeared from the economic literature since a long time, but it didn't for a reason. In fact, the increasing empirical and experimental evidence for fairness preferences and unexpectedly high levels of cooperation in one-shot prisoner's dilemma, dictator and ultimatum games have been waiting for a convincing theoretical explanation until very recently. It is important here to distinguish between other-regarding preferences and cooperative ("altruistic") behavior. Other-regarding preferences means that people intentionally do not maximize their payoffs, but try to consider and improve the benefits of others. Most game theoretical work is strictly compatible with the concept of "homo economicus", identifying mechanisms that make it advantageous in one way or another to cooperate. For example, if the "shadow of the future" in repeated prisoner's dilemma interactions is long enough, it creates a higher payoff when people cooperate, and that's why they do it. In other words, some mechanisms such as repeated interactions, punishment, transfer payments, and others change the payoff structure of a prisoner's dilemma game such that there is no dilemma anymore. Martin Nowak has mathematically shown that many such mechanisms can be understood with Hamilton's rule, according to which people cooperate when the benefits of cooperation exceed the costs. Other work shows that cooperation in prisoner's dilemma games may survive if people imitate more successful behavior of neighbors, but if one believes in rational choice, why should people imitate, if they can reach a higher payoff by another behavior? In fact, all such cooperation in spatial prisoner's dilemma games disappears, if imitation is replaced by a "best response" rule, which assumes a strict maximization of utility, based on the previous decision of the interaction partners. In Ref. [4], Grund et al. have combined such a "best response" rule with standard evolutionary rules of mutation and selection, when people reproduce. The unexpected outcome was a "homo socialis", if offspring stay close to their parents, which they often do. But the transition is not smooth. It requires the population to go through a phase where unconditionally "friendly" behavior is dysfunctional, which happens only by "mistake" (due to mutations). Random spatio-temporal coincidence of people with friendly traits is equally important for other-regarding preferences to emerge. However, conditionally cooperative behavior resulting from other-regarding preferences may also occur between strangers, i.e. they do not require genetic relatedness, as the following movie shows: http://vimeo.com/65376719. In any case, spatio-temporal correlations (here: the co-evolution of individual preferences and behavior) can promote cooperation more than expected for a payoff-maximizing "homo economicus". These new discoveries mean that key concepts of both, the theory of evolution and of economics, must be reconsidered.

[2] Smith, A., The Theory of Moral Sentiments (A. Millar, London, 1759).

Further Reading:

[0] D. Helbing, Economics 2.0: The Natural step towards a self-regulating, participatory market society, Evolutionary and Institutional Economics Review (2013), see

https://www.jstage.jst.go.jp/article/eier/10/1/10_3/_article and watch the video at

https://www.youtube.com/watch?v=ZHYxMHm4t6U or http://www.youtube.com/watch?v=Ef2Ag_rwouo

[1] Henrich, J., R.Boyd, S. Bowles, C. Camerer, E. Fehr, H. Gintis, and R. McElreath, "In search of homo economicus: behavioral experiments in 15 small-scale societies," Am. Econom. Rev. 91, 73-78 (2001).

[2] Murphy, R. O., K. A. Ackermann, and M. J. J. Handgraaf, "Measuring social value orientation," Judgment and Decision Making 6(8), 771-781 (2011).

[3] Falk, A. and N. Szech, "Morals and Markets," Science 340, 707-711 (2013).

[4] Grund, T., C. Waloszek, and D. Helbing, "How Natural Selection Can Create Both Self-and Other-Regarding Preferences, and Networked Minds," Scientific Reports 3:1480 (2013), see

http://www.nature.com/srep/2013/130319/srep01480/full/srep01480.html

[5] Adami, C. and A. Hintze, "Evolutionary instability of zero-determinant strategies demonstrates that winning is not everything," Nature Communications 4:2193 (2013); Evolution will punish you, if you're selfish and mean, see

http://esciencenews.com/articles/2013/08/01/evolution.will.punish.you.if.youre.selfish.and.mean

[6] Berger, R., H. Rauhut, S. Prade, and D. Helbing, "Bargaining over waiting time in ultimatum game experiments," Social Science Research 41, 372-379 (2012).

[7] Helbing, D. "Globally networked risks and how to respond," Nature 497, 51-59 (2013).

[8] Milinski, M., D. Semmann, and H. J. Krambeck, "Reputation helps solve the tragedy of the commons," Nature 415, 424-426 (2002).

[9] Pariser, E., Filter Bubble (Carl Hanser, 2012).

[10] Przepiorka, W., "Buyers pay for and sellers invest in a good reputation: More evidence from eBay,'' The Journal of Socio-Economics 42, 31-42 (2013).

Dirk Helbing is Professor of Sociology, in particular of Modeling and Simulation, and member of the Computer Science Department at ETH Zurich. He earned a PhD in physics and was Managing Director of the Institute of Transport & Economics at Dresden University of Technology in Germany. He is internationally known for his work on pedestrian crowds, vehicle traffic, and agent-based models of social systems. Furthermore, he coordinates the FuturICT Initiative (http://www.futurict.eu), which focuses on the understanding of techno-socio-economic systems, using Big Data. His work is documented by hundreds of scientific articles, keynote lectures and media reports worldwide. Helbing is elected member of the World Economic Forum’s Global Agenda Council on Complex Systems and of the German Academy of Sciences “Leopoldina”. He is also Chairman of the Physics of Socio-Economic Systems Division of the German Physical Society and co-founder of ETH Zurich’s Risk Center.

From Technology-Driven Society to Socially Oriented Technology-The Future of Information Society - Alternatives to Surveillance

by Dirk Helbing (ETH Zurich)

Our society is changing. Almost nothing these days works without a computer chip; computing power doubles every 18 months, and in ten years it will probably exceed the capabilities of a human brain. Computers perform approximately 70 percent of all financial transactions today and IBM's Watson now seems to give better customer advise than some human telephone hotlines.

The forthcoming economic and social transformation might be more fundamental than the one resulting from the invention of the steam engine. Meanwhile, the storage capacity of data grows even faster than the computational capacity. Within a few years, we will generate more data than in the entire history of humankind. The "Internet of Things" will soon network trillions of sensors together - fridges, coffee machines, electric toothbrushes and even our clothes. Vast amounts of data will be collected. Already, Big Data is being heralded as the oil of the 21st Century.

But this situation will also make us vulnerable. Exploding cyber-crime, economic crises and social protests show that our hyper-connected world is destabilizing. However, is a Surveillance Society the right answer? When all our Internet queries are stored, when our purchases and social contacts are evaluated, when our emails and files are scanned for search terms, and when countless innocent citizens are classified as potential future terrorists, we must ask: Where will this lead to? And where will it end?

Will surveillance lead to self-censorship and discrimination against intellectuals and minorities, even though innovation and creative thinkers are bitterly needed for our economy and society to do well in our changing world? Will free human expression eventually be curtailed by data mining machines analyzing our digital trails?

What are the consequences, say if even the Swiss banks and the U.S. government can no longer protect their secrets, or if our health and other sensitive data is sold on? Or if politically and commercially sensitive strategies can be monitored in real time? What if insider knowledge can be used to undermine fair competition and justice?

The recent allegations that information agencies of various states snoop secretly into the activities of millions of ordinary people has alarmed citizens and companies alike. The moral outrage in response to the surveillance activity has made it clear that it is not a technology-driven society that we need, but instead, a socially-oriented technology, as outlined below. We must recognize that technology without consideration of ethical issues, or without transparency and public discussions can lead us astray. Therefore a new approach to personal data and its uses is required so that we can safely benefit from the many new economic and social opportunities that it can provide.

First, we need a public ethical debate on the concepts of privacy and ownership of data, even more urgently than in bioethics. Important questions that we have to ask are: How do we create opportunities arising in the information age for all, but yet still manage the downside risks and challenges - from cyber-crime to the erosion of trust and democratic rights? Do we really need so much security that we must be afraid of data mining algorithms flagging the activities of millions of ordinary people as suspicious? And what kinds of new institutions would we need in the 21 century?

In the past we have built public roads, parks and museums, schools, libraries and universities. Now, more than ever, we need strategies that protect us against the misuse of data, and that are intended to create transparency and trust. These strategies must place citizen benefits and rights of self-determination at the very core. In addition, we must develop new institutions to provide oversight and control of the new challenges brought by the data revolution. Here are some concrete institutional proposals:

Self-determined use of personal data: Already some time ago, the World Economic Forum (WEF) called for a "New Deal on Data" . It stated that the sustainable use of the economic opportunities of personal data requires a fair balance between economic, governmental and individual interests. A solution would be to return control over personal data to the respective individuals, i.e. give people ownership of their data: the right to possess, access, use and dispose. In addition, individuals should be able to participate in their economic profits. This would require new data protocols and the support of legislation.

Trusted information exchange: As the vulnerability of existing systems and the proliferation of cyber-crime indicates, a new network architecture is urgently needed. The handling of sensitive data requires secure encryption, anonymisation and protected pseudonyms, decentralized storage, open software codes and transparency on the use of data, correction possibilities, mechanisms of forgetting, and a protective "digital immune system."

Credibility mechanisms: Social mechanisms such as reputation, as seen in the evaluation of information and information sources on the internet, can play a central role in reducing abuse. But remember that the wisdom of crowds only works if individual decisions are not manipulated. Therefore, to be effective, individuals must be given control over the recommendation mechanisms, data filtering and search routines they use, such that they can take decisions based on their own values and quality criteria.

Participatory platforms: All over the world people desire increased participation, from consumption to production processes. Now, modern technology allows for the direct social, economic, and political participation of engaged individuals. A basic democracy approach as in Switzerland, where people can decide themselves about many laws, not just political representatives, would be feasible on much larger scales. We also witness an economic trend towards local production, ranging from solar panels to 3D Printers. It can be become a good complement of mass production.

Open Data: The innovation ecosystem needs open data and open standards to flourish. Open data enable the rapid creation of new products, which stimulates further products and services. Information is the best catalyst for innovation. Of course, data providers should be adequately compensated, and not all data would have to be open.

Innovation Accelerator: To keep pace with our changing world, we need to reinvent the innovation process itself. A participatory innovation process would allow ideas to be implemented faster and external expertise to be integrated more readily. Information is an extraordinary resource: it does not diminish when shared, and it can be infinitely reproduced. Why shouldn't we use this opportunity?

Social Capital: Information systems can support diverse types of social capital such as trust, reputation, and cooperation. Based on social network interactions, they are the foundation of a flourishing economy and society. So, let's create new value!

Social Technologies: Finally, we must learn to build information systems that are compatible with our individual, social and cultural values. We need to design systems that respect the privacy of citizens and prevent fear and discrimination, while promoting tolerance, trust, and fairness. What solutions can we offer users to ensure that information systems are not misused for unjustified monitoring and manipulation? For a well-functioning society, socio-diversity (pluralism) must be protected as much as biodiversity. Both determine the potential for innovation.

These are just some examples of the promising ways in which we could use the Internet of the future. Among all these, a surveillance society is probably the worst of all uses of information technology. A safe and sustainable information society has to be built on reputation, transparency and trust, not mass surveillance.

If we can no longer trust our phones, computers or the Internet, we will either switch off our equipment or start to behave like agents of a secret service: revealing as little information as possible, encrypting data, creating multiple identities, laying false traces.

Such behaviour would create little benefits for ordinary citizens, besides protection, but might help criminals to hide. It would be a pity if we failed to use the opportunities afforded by the information age, just because we did not think hard or far enough about the technological and legal frameworks and institutions needed.

The information age is now at a crossroad. It may eventually lead us to a totalitarian surveillance state, or we can use it to enable a creative, participatory society. It is our decision, and we should not leave it to others.

It is also time to build the institutions for the globalized information society to come, in a world-wide collaboration, instead of starting a global war of information systems.

Related Readings – by Dirk Helbing

Google as God? Opportunities and Risks of the Information Age

http://futurict.blogspot.ie/2013/03/google-as-god-opportunities-and-risks.html

Qualified Trust, not Surveillance, is the Basis for a Stable Society

http://futurict.blogspot.ie/2013/06/qualifiedtrust-not-surveillance-is_6661.html

Why Mass Surveillance Does Not Work

http://futurict.blogspot.ie/2013/06/why-mass-surveillance-does-not-work.html

How to Ensure that the European Data Protection Legislation Will Protect the Citizens

http://futurict.blogspot.ie/2013/06/how-to-ensure-that-european-data.html

Other Related Readings

Statement by Vice President Neelie Kroes "on the consequences of living in an age of total information" 04/07/2013

http://europa.eu/rapid/press-release_MEMO-13-654_en.htm?locale=en

Consumer Data Privacy In A Networked World: A Framework For Protecting  Privacy And Promoting Innovation  In The Global Digital Economy

http://www.whitehouse.gov/sites/default/files/privacy-final.pdf

Big Data Is Opening Doors, but Maybe Too Many

http://www.nytimes.com/2013/03/24/technology/big-data-and-a-renewed-debate-over-privacy.html?pagewanted=all

Personal Data: The Emergence of a New Asset Class

http://www3.weforum.org/docs/WEF_ITTC_PersonalDataNewAsset_Report_2011.pdf

The Global Information Technology Report 2008–2009 Mobility in a Networked World

http://www.weforum.org/pdf/gitr/2009/gitr09fullreport.pdf

Why Mass Surveillance Does Not Work

by Dirk Helbing (ETH Zurich, dhelbing@ethz.ch)

These days, it is often claimed that we need massive surveillance to ensure a high level of security. While the idea sounds plausible, I will explain, why this approach cannot work well, even when secret services have the very best intentions, and their sensitive knowledge would not be misused. This is a matter of statistics - no method is perfect.

  

For the sake of illustration, let us assume there are 2000 terrorists in a country with 200 Mio. inhabitants. Moreover, let us assume that the secret service manages to identify terrorists with an amazing 99% accuracy. Then, there are 1% false negatives (type II error), which means that 20 terrorists are not detected, while 1980 will be caught. The actual numbers are much smaller. It has been declared that 50 terror acts were prevented in about 12 years, while a few terrorist attacks could not be stopped (although the terrorists were often listed as suspects).

  

It is also important to ask, how many false positives ("false alarms") do we have? If the type I error is just 1 out of 10,000, there will be 20,000 wrong suspects, if it is 1 permille, there will be 200,000 wrong suspects, and if it is 1 percent, it will be 2 million false suspects. Recent figures I have heard of on TV spoke of 8 Million suspects in the US in 1996, which would mean about a 4 percent error rate. If these figures are correct, this would mean that for every terrorist, 4000 times as many innocent citizens would be wrongly categorized as (potential) terrorists.

  

Hence, large-scale surveillance is not an effective means of fighting terrorism. It rather tends to restrict the freedom rights of millions of innocent citizens. It is not reasonable to apply surveillance to the whole population, for the same reasons, why it is not sensible to make a certain medical test with everybody. There would be millions of false positives, i.e. millions of people who would be wrongly treated, with negative side effects on their health. For this reason, patients are tested for diseases only if they show worrying symptoms.

  

In the very same way, it creates more harm than benefit, if everybody is being screened for being a potential future terrorist. This will cause unjustified discrimination and harmful self-censorship at times, where unconventional, new ideas are needed more than ever. It will impair the ability of our society to innovate and adapt, thereby promoting instability. Thus, it is time to pursue a different approach, namely to identify the social, economic and political factors that promote crime and terrorism, and to change these factors. Just 2 decades back, we saw comparatively little security problems in most modern societies. Overall, people tolerated each other and coexisted peacefully, without massive surveillance and policing. We were living in a free and happy world, where people of different cultural backgrounds respected each other and did not have to live in fear. Can we have this time back, please?

Reference:

Type I and type II errors, see https://en.wikipedia.org/wiki/Type_I_and_type_II_errors