Prediction by Geoff Mulgan, Eva Grobbink and Vincent Straub: “The USSR’s launch of the Sputnik 1 satellite in 1958 was a major psychological blow to the United States. The US had believed it was technologically far ahead of its rival, but was confronted with proof that the USSR was pulling ahead in some fields. After a bout of soul-searching the country responded with extraordinary vigour, massively increasing investment in space technologies and promising to put a man on the Moon by the end of the 1960s.
In 2019, China’s success in smart cities could prompt a similar “Sputnik Moment” for the rest of the world. It may not be as dramatic as that of 1958. But unlike beeping satellites and Moon landings, it could be coming to a town near you….
The concept of a “smart city” has been around for several decades, often associated with hype, grandiose failures, and an overemphasis on hardware rather than people (Nesta has previously written on how we can rethink smart cities and ensure digital innovation realises the potential of technology and people). But various technologies are now coming of age which bring the vision of a smart city closer to fruition. China is in the forefront, investing heavily in sensors and infrastructures, and its ET City Brain project shows just how far the country’s thinking has progressed.
First launched in September 2016, ET City Brain is a collaboration between Chinese technology giant Alibaba and several cities. It was first trialled in Hangzhou, the hometown of Alibaba’s executive chairman, Jack Ma, but has since expanded to other Chinese cities. Earlier this year, Kuala Lumpurbecame the first city outside of China to import the ET City Brain model.
The ET City Brain system gathers large amounts of data (including logs, videos, and data stream) from sensors. These are then processed by algorithms in supercomputers and fed back into control centres around the city for administrators to act on—in some cases, automation means the system works without any human intervention at all.
So far, the project has been used to monitor congestion in Hangzhou, improve the response of emergency services in Guangzhou, and detect traffic accidents in Suzhou. In Hangzhou, Alibaba was given control of 104 traffic light junctions in the city’s Xiaoshan district and tasked with managing traffic flows. By combining mass video surveillance with live data from public transportation systems, ET City Brain was able to autonomously change traffic lights so that emergency vehicles could travel to accident scenes without interruption. As a result, arrival times for ambulances improved by 49 percent….(More)”.
Peter Andras et al in IEEE Technology and Society Magazine: “Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind’s Alpha Go Zero) are an impressive example of an artificial intelligence system calculating results that even a human expert for the game can hardly retrace. But this is, quite literally, a toy example. In reality, intelligent algorithms are encroaching more and more into our everyday lives, be it through algorithms that recommend products for us to buy, or whole systems such as driverless vehicles. We are delegating ever more aspects of our daily routines to machines, and this trend looks set to continue in the future. Indeed, continued economic growth is set to depend on it. The nature of human-computer interaction in the world that the digital transformation is creating will require (mutual) trust between humans and intelligent, or seemingly intelligent, machines. But what does it mean to trust an intelligent machine? How can trust be established between human societies and intelligent machines?…(More)”.
Blog Post by Beth Noveck: “…The Inter-American Development Bank (IADB) has published an important, practical and prescriptive report with recommendations for every sector of society from government to individuals on innovative and effective approaches to combatting corruption. While focused on Latin America, the report’s proposals, especially those on the application of new technology in the fight against corruption, are relevant around the world….
The recommendations about the use of new technologies, including big data, blockchain and collective intelligence, are drawn from an effort undertaken last year by the Governance Lab at New York University’s Tandon School of Engineering to crowdsource such solutions and advice on how to implement them from a hundred global experts. (See the Smarter Crowdsourcing against Corruption report here.)…
Big data, when published as open data, namely in a form that can be re-used without legal or technical restriction and in a machine-readable format that computers can analyze, is another tool in the fight against corruption. With machine readable, big and open data, those outside of government can pinpoint and measure irregularities in government contracting, as Instituto Observ is doing in Brazil.
Opening up judicial data, such as information about case processing times, judges’ and prosecutors’ salaries, information about selection processes, such as CV’s, professional and academic backgrounds, and written and oral exam scores provides activists and reformers with the tools to fight judicial corruption. The Civil Association for Equality and Justice (ACIJ) (a non-profit advocacy group) in Argentina uses such open justice data in its Concursos Transparentes (Transparent Contests) to fight judicial corruption. Jusbrasil is a private open justice company also using open data to reform the courts in Brazil….(More)”
Julia Hobsbawm at Strategy + Business: “Picture the scene. The eyes of the world are on the Tham Luang cave system in Thailand, near the border with Myanmar. Trapped on a rock ledge deep inside is the Wild Boars soccer team of 12 boys and their coach, who had ventured into the caves about two weeks earlier. It is monsoon season. Water is rising and oxygen levels are falling. Not all of the boys can even swim. Time is running out.
Elon Musk proposes building a “kid-sized submarine” to assist the rescue effort. Musk’s solution is politely declined by Thai authorities as “not practical.” In fact, by the time Musk’s sub arrives, most of the boys are already out, alive. One of the most audacious, moving, complex, and successful rescue operations in history relied not on a single technology or hero but on the collaboration of many people, working together in a spontaneous network.
This web of connections came together organically and quickly, unassisted by algorithms, in a unique collaboration led by humans. It was a stunning example of what physicist Albert-László Barabási calls “scale-free networks”: networks that reproduce exponentially by their very nature. The exact same network effects that can be lethal in spreading a virus can be productive — beautiful, even — in creating a web of diverse human skills quickly. Networks, as Barabási puts it, “are everywhere. You just have to look for them.”…
Networks that come together like this and use technology, community, and communications in a timely manner are an example of what the U.N. calls its “leave no one behind” strategy for achieving sustainable development goals. I consider it an example of social health in action: They are the kinds of collaborations that help us live full and productive lives. And in business, there is an exciting opportunity to harness social health and the power of networks to help solve problems.
This kind of social health network, perhaps unsurprisingly, is very visible in innovations in the healthcare sector. A digital health community called The Mighty, for example, is a forum to find information about rare illnesses and connect people facing similar challenges, so that they might learn from the experiences of others. It now has 90 million engagements on its website per month and a new member joins every 20 seconds….(More)”.
Report by Michael Crawford Urban: “The complex challenges that governments at all levels are facing today cut across long-standing and well-defined government boundaries and organizational structures. Solving these problems therefore requires a horizontal approach. This report looks at how such an approach can be successfully implemented.There are a number of key obstacles to effective horizontal collaboration in government, ranging from misaligned professional incentive structures to incompatible computer systems. But a number of governments – Estonia, the UK, and New Zealand – have all recently introduced innovative initiatives that are succeeding in creatively tackling these complex horizontal challenges. In each case, this is delivering critical benefits – reduced government costs and regulatory burdens, getting more out of existing personnel while recruiting more high quality professionals, or providing new and impactful data-driven insights that are helping improve the quality of human services.
How are they achieving this? We answer this question by using an analytical framework organized along three fundamental dimensions: governance(structuring accountability and responsibility), people (managing culture and personnel), and data (collecting, transmitting and using information). In each of our three cases, we show how specific steps taken along one of these dimensions can help overcome important obstacles that commonly arise and, in so doing, enable successful horizontal collaboration….(More)”.
Yves-Alexandre de Montjoye et al in Nature: “The breadcrumbs we leave behind when using our mobile phones—who somebody calls, for how long, and from where—contain unprecedented insights about us and our societies. Researchers have compared the recent availability of large-scale behavioral datasets, such as the ones generated by mobile phones, to the invention of the microscope, giving rise to the new field of computational social science.
With mobile phone penetration rates reaching 90% and under-resourced national statistical agencies, the data generated by our phones—traditional Call Detail Records (CDR) but also high-frequency x-Detail Record (xDR)—have the potential to become a primary data source to tackle crucial humanitarian questions in low- and middle-income countries. For instance, they have already been used to monitor population displacement after disasters, to provide real-time traffic information, and to improve our understanding of the dynamics of infectious diseases. These data are also used by governmental and industry practitioners in high-income countries.
While there is little doubt on the potential of mobile phone data for good, these data contain intimate details of our lives: rich information about our whereabouts, social life, preferences, and potentially even finances. A BCG study showed, e.g., that 60% of Americans consider location data and phone number history—both available in mobile phone data—as “private”.
Historically and legally, the balance between the societal value of statistical data (in aggregate) and the protection of privacy of individuals has been achieved through data anonymization. While hundreds of different anonymization algorithms exist, most of them are variations and improvements of the seminal k-anonymity algorithm introduced in 1998. Recent studies have, however, shown that pseudonymization and standard de-identification are not sufficient to prevent users from being re-identified in mobile phone data. Four data points—approximate places and times where an individual was present—have been shown to be enough to uniquely re-identify them 95% of the time in a mobile phone dataset of 1.5 million people. Furthermore, re-identification estimations using unicity—a metric to evaluate the risk of re-identification in large-scale datasets—and attempts at k-anonymizing mobile phone data ruled out de-identification as sufficient to truly anonymize the data. This was echoed in the recent report of the [US] President’s Council of Advisors on Science and Technology on Big Data Privacy which consider de-identification to be useful as an “added safeguard, but [emphasized that] it is not robust against near-term future re-identification methods”.
The limits of the historical de-identification framework to adequately balance risks and benefits in the use of mobile phone data are a major hindrance to their use by researchers, development practitioners, humanitarian workers, and companies. This became particularly clear at the height of the Ebola crisis, when qualified researchers (including some of us) were prevented from accessing relevant mobile phone data on time despite efforts by mobile phone operators, the GSMA, and UN agencies, with privacy being cited as one of the main concerns.
These privacy concerns are, in our opinion, due to the failures of the traditional de-identification model and the lack of a modern and agreed upon framework for the privacy-conscientious use of mobile phone data by third-parties especially in the context of the EU General Data Protection Regulation (GDPR). Such frameworks have been developed for the anonymous use of other sensitive data such as census, household survey, and tax data. The positive societal impact of making these data accessible and the technical means available to protect people’s identity have been considered and a trade-off, albeit far from perfect, has been agreed on and implemented. This has allowed the data to be used in aggregate for the benefit of society. Such thinking and an agreed upon set of models has been missing so far for mobile phone data. This has left data protection authorities, mobile phone operators, and data users with little guidance on technically sound yet reasonable models for the privacy-conscientious use of mobile phone data. This has often resulted in suboptimal tradeoffs if any.
In this paper, we propose four models for the privacy-conscientious use of mobile phone data (Fig. 1). All of these models 1) focus on a use of mobile phone data in which only statistical, aggregate information is ultimately needed by a third-party and, while this needs to be confirmed on a per-country basis, 2) are designed to fall under the legal umbrella of “anonymous use of the data”. Examples of cases in which only statistical aggregated information is ultimately needed by the third-party are discussed below. They would include, e.g., disaster management, mobility analysis, or the training of AI algorithms in which only aggregate information on people’s mobility is ultimately needed by agencies, and exclude cases in which individual-level identifiable information is needed such as targeted advertising or loans based on behavioral data.
Figure 1: Matrix of the four models for the privacy-conscientious use of mobile phone data.
First, it is important to insist that none of these models is a silver bullet…(More)”.
M. Poblet at First Monday: “This paper examines new civic engagement practices unfolding during the 2017 referendum on independence in Catalonia. These practices constitute one of the first signs of some emerging trends in the use of the Internet for civic and political action: the adoption of horizontal, distributed, and privacy-enhancing technologies that rely on P2P networks and advanced cryptographic tools. In this regard, the case of the 2017 Catalan referendum, framed within conflicting political dynamics, can be considered a first-of-its kind in participatory democracy. The case also offers an opportunity to reflect on an interesting paradox that twenty-first century activism will face: the more it will rely on private-friendly, secured, and encrypted networks, the more open, inclusive, ethical, and transparent it will need to be….(More)”.
Paper by Martha Finnemore: “This essay steps back from the more detailed regulatory discussions in other contributions to this roundtable on “Competing Visions for Cyberspace” and highlights three broad issues that raise ethical concerns about our activity online. First, the commodification of people—their identities, their data, their privacy—that lies at the heart of business models of many of the largest information and communication technologies companies risks instrumentalizing human beings. Second, concentrations of wealth and market power online may be contributing to economic inequalities and other forms of domination. Third, long-standing tensions between the security of states and the human security of people in those states have not been at all resolved online and deserve attention….(More)”.
Report by K.Zuegel, E. Cantera, and A. Bellantoni: “Ombudsman institutions (OIs) act as the guardians of citizens’ rights and as a mediator between citizens and the public administration. While the very existence of such institutions is rooted in the notion of open government, the role they can play in promoting openness throughout the public administration has not been adequately recognized or exploited. Based on a survey of 94 OIs, this report examines the role they play in open government policies and practices. It also provides recommendations on how, given their privileged contact with both people and governments, OIs can better promote transparency, integrity, accountability, and stakeholder participation; how their role in national open government strategies and initiatives can be strengthened; and how they can be at the heart of a truly open state….(More)”.
Paper by Daniel Kondor, Behrooz Hashemian, Yves-Alexandre de Montjoye and Carlo Ratti: “The problem of unicity and reidentifiability of records in large-scale databases has been studied in different contexts and approaches, with focus on preserving privacy or matching records from different data sources. With an increasing number of service providers nowadays routinely collecting location traces of their users on unprecedented scales, there is a pronounced interest in the possibility of matching records and datasets based on spatial trajectories. Extending previous work on reidentifiability of spatial data and trajectory matching, we present the first large-scale analysis of user matchability in real mobility datasets on realistic scales, i.e. among two datasets that consist of several million people’s mobility traces, coming from a mobile network operator and transportation smart card usage. We extract the relevant statistical properties which influence the matching process and analyze their impact on the matchability of users. We show that for individuals with typical activity in the transportation system (those making 3-4 trips per day on average), a matching algorithm based on the co-occurrence of their activities is expected to achieve a 16.8% success only after a one-week long observation of their mobility traces, and over 55% after four weeks. We show that the main determinant of matchability is the expected number of co-occurring records in the two datasets. Finally, we discuss different scenarios in terms of data collection frequency and give estimates of matchability over time. We show that with higher frequency data collection becoming more common, we can expect much higher success rates in even shorter intervals….(More)”.
