How Can Blockchain Technology Help Government Drive Economic Activity?

Curated on Posted on by Stefaan Verhulst

Thomas Hardjono and Pete Teigen providing “A Blueprint Discussion on Identity“: Data breaches, identity theft, and trust erosion are all identity-related issues that citizens and government organizations face with increased frequency and magnitude. The rise of blockchain technology, and related distributed ledger technology, is generating significant interest in how a blockchain infrastructure can enable better identity management across a variety of industries.  Historically, governments have taken the primary role in issuing certain types of identities (e.g. social security numbers, driver licenses, and passports) based on strong authentication proofing of individuals using government-vetted documentation – a process often referred to as on-boarding. This identity proofing and on-boarding process presents a challenge to government because it is still heavily paper-based, making it cumbersome, time consuming and dependent on siloed, decades old, and inefficient systems.

Another aspect of the identity challenge is the risk of compromising an individual’s digital identifiers and government-issued credentials through identity theft. With so many vital services (e.g. banking, health services, transport, residency) dependent on trusted, government-vetted credentials, any compromise of that identity can result in a significant negative impact to the individual and be difficult to repair. Compounding the problem, many instances of identity theft go undetected and only discovered after damage is done.

Increasing the efficiency of the identity vetting process while also enhancing transparency would help mitigate those identity challenges.  Blockchain technology promises to do just that. Through the use of multiple computer systems (nodes) that are interconnected in a peer-to-peer (P2P) network, a shared common view of the information in the network ensures synchronicity of agreed data. A trusted ledger then exists in a distributed manner across the network that inherently is accountable to all network participants, thereby providing transparency and trustworthiness.

Using that trusted distributed ledger, identity-related data vetted by one Government entity and including that data’s location (producing a link in the chain) can be shared with other members of the network as needed — allowing members to instantaneously accept an identity without the need to duplicate the identity vetting process.  The more sophisticated blockchain systems possess this “record-link-fetch” feature that  is inherent in  the blockchain system’s building blocks.  Additional efficiency enhancing features allow downstream processes using that identity assertion as automated input to enable “smart contracts”, discussed below.

Thus, the combination of Government vetting of individual data, together with the embedded transparency and accountability capabilities of blockchain systems, allow relying parties (e.g. businesses, online merchants, individuals, etc.) to obtain higher degrees of assurance regarding the identity of other parties with whom they are conducting transactions…..

Identity and membership management solutions already exist and can be applied to private (permissioned) blockchain systems. Features within these solutions should be evaluated for their suitability for blockchain systems.  Specifically, these four steps can enable government to start in suing blockchain to address identity challenges:

  1. Evaluate existing identity and membership management solutions in order to identify features that apply to permissioned blockchain systems in the short term.
  2. Experiment with integrating these existing solutions with open source blockchain implementations.
  3. Create a roadmap (with a 2-3 year horizon) for identity and membership management for smart contracts within permissioned blockchains.
  4. Develop a long term plan (a 5 year horizon) for addressing identity and membership management for permissionless (public) blockchain systems. Here again, use open source blockchain implementations as the basis to understand the challenges in the identity space for permissionless blockchains….(More)”.

Smart or dumb? The real impact of India’s proposal to build 100 smart cities

Curated on Posted on by Stefaan Verhulst

 in The Conversation: “In 2014, the new Indian government declared its intention to achieve 100 smart cities.

In promoting this objective, it gave the example of a large development in the island city of Mumbai, Bhendi Bazaar. There, 3-5 storey housing would be replaced with towers of between 40 to 60 storeys to increase density. This has come to be known as “vertical with a vengeance”.

We have obtained details of the proposed project from the developer and the municipal authorities. Using an extended urban metabolism model, which measures the impacts of the built environment, we have assessed its overall impact. We determined how the flows of materials and energy will change as a result of the redevelopment.

Our research shows that the proposal is neither smart nor sustainable.

Measuring impacts

The Indian government clearly defined what they meant with “smart”. Over half of the 11 objectives were environmental and main components of the metabolism of a city. These include adequate water and sanitation, assured electricity, efficient transport, reduced air pollution and resource depletion, and sustainability.

We collected data from various primary and secondary sources. This included physical surveys during site visits, local government agencies, non-governmental organisations, the construction industry and research.

We then made three-dimensional models of the existing and proposed developments to establish morphological changes, including building heights, street widths, parking provision, roof areas, open space, landscaping and other aspects of built form.

Demographic changes (population density, total population) were based on census data, the developer’s calculations and an assessment of available space. Such information about the magnitude of the development and the associated population changes allowed us to analyse the additional resources required as well as the environmental impact….

Case studies such as Bhendi Bazaar provide an example of plans for increased density and urban regeneration. However, they do not offer an answer to the challenge of limited infrastructure to support the resource requirements of such developments.

The results of our research indicate significant adverse impacts on the environment. They show that the metabolism increases at a greater rate than the population grows. On this basis, this proposed development for Mumbai, or the other 99 cities, should not be called smart or sustainable.

With policies that aim to prevent urban sprawl, cities will inevitably grow vertically. But with high-rise housing comes dependence on centralised flows of energy, water supplies and waste disposal. Dependency in turn leads to vulnerability and insecurity….(More)”.

The hidden costs of open data

Curated on Posted on by Stefaan Verhulst

Sara Friedman at GCN: “As more local governments open their data for public use, the emphasis is often on “free” — using open source tools to freely share already-created government datasets, often with pro bono help from outside groups. But according to a new report, there are unforeseen costs when it comes pushing government datasets out of public-facing platforms — especially when geospatial data is involved.

The research, led by University of Waterloo professor Peter A. Johnson and McGill University professor Renee Sieber, was based on work as part of Geothink.ca partnership research grant and exploration of the direct and indirect costs of open data.

Costs related to data collection, publishing, data sharing, maintenance and updates are increasingly driving governments to third-party providers to help with hosting, standardization and analytical tools for data inspection, the researchers found. GIS implementation also has associated costs to train staff, develop standards, create valuations for geospatial data, connect data to various user communities and get feedback on challenges.

Due to these direct costs, some governments are more likely to avoid opening datasets that need complex assessment or anonymization techniques for GIS concerns. Johnson and Sieber identified four areas where the benefits of open geospatial data can generate unexpected costs.

First, open data can create “smoke and mirrors” situation where insufficient resources are put toward deploying open data for government use. Users then experience “transaction costs” when it comes to working in specialist data formats that need additional skills, training and software to use.

Second, the level of investment and quality of open data can lead to “material benefits and social privilege” for communities that devote resources to providing more comprehensive platforms.

While there are some open source data platforms, the majority of solutions are proprietary and charged on a pro-rata basis, which can present a challenge for cities with larger, poor populations compared to smaller, wealthier cities. Issues also arise when governments try to combine their data sets, leading to increased costs to reconcile problems.

The third problem revolves around the private sector pushing for the release of data sets that can benefit their business objectives. Companies could push for the release high-value sets, such as a real-time transit data, to help with their product development goals. This can divert attention from low-value sets, such as those detailing municipal services or installations, that could have a bigger impact on residents “from a civil society perspective.”

If communities decide to release the low-value sets first, Johnson and Sieber think the focus can then be shifted to high-value sets that can help recoup the costs of developing the platforms.

Lastly, the report finds inadvertent consequences could result from tying open data resources to private-sector companies. Public-private open data partnerships could lead to infrastructure problems that prevent data from being widely shared, and help private companies in developing their bids for public services….

Johnson and Sieber encourage communities to ask the following questions before investing in open data:

  1. Who are the intended constituents for this open data?
  2. What is the purpose behind the structure for providing this data set?
  3. Does this data enable the intended users to meet their goals?
  4. How are privacy concerns addressed?
  5. Who sets the priorities for release and updates?…(More)”

Read the full report here.

Using Social Media To Predict the Future: A Systematic Literature Review

Curated on Posted on by Stefaan Verhulst

Review by Lawrence Phillips, Chase Dowling, Kyle Shaffer, Nathan Hodas and Svitlana Volkov: “Social media (SM) data provides a vast record of humanity’s everyday thoughts, feelings, and actions at a resolution previously unimaginable. Because user behavior on SM is a reflection of events in the real world, researchers have realized they can use SM in order to forecast, making predictions about the future. The advantage of SM data is its relative ease of acquisition, large quantity, and ability to capture socially relevant information, which may be difficult to gather from other data sources. Promising results exist across a wide variety of domains, but one will find little consensus regarding best practices in either methodology or evaluation. In this systematic review, we examine relevant literature over the past decade, tabulate mixed results across a number of scientific disciplines, and identify common pitfalls and best practices. We find that SM forecasting is limited by data biases, noisy data, lack of generalizable results, a lack of domain-specific theory, and underlying complexity in many prediction tasks. But despite these shortcomings, recurring findings and promising results continue to galvanize researchers and demand continued investigation. Based on the existing literature, we identify research practices which lead to success, citing specific examples in each case and making recommendations for best practices. These recommendations will help researchers take advantage of the exciting possibilities offered by SM platforms….(More)”

#WhereIsMyName ?

Curated on Posted on by Stefaan Verhulst

Mujb Mashal the New York Times: “These are some of the terms Afghan men use to refer to their wives in public instead of their names, the sharing of which they see as a grave dishonor worthy of violence: Mother of Children, My Household, My Weak One or sometimes, in far corners, My Goat or My Chicken.

Women also may be called Milk-sharer or Black-headed. The go-to word for Afghans to call a woman in public, no matter her status, is Aunt.

But a social media campaign to change this custom has been percolating in recent weeks, initiated by young women. The campaign comes with a hashtag in local languages that addresses the core of the issue and translates as #WhereIsMyName.

The activists’ aim is both to challenge women to reclaim their most basic identity, and to break the deep-rooted taboo that prevents men from mentioning their female relatives’ names in public….

Like many social media efforts, this one began small, with several posts out of Herat Province in the west. Since then, more activists have tried to turn it into a topic of conversation by challenging celebrities and government officials to share the names of their wives and mothers.

The discussion has now made it to the regular media, with articles in newspapers and conversations on television and radio talk shows.

Members of the Parliament, senior government officials and artists have come forward in support, publicly declaring the identities of the female members of their families….(More)”

Data Africa

Curated on Posted on by Stefaan Verhulst

“Data Africa is an open data platform designed to provide information on key themes for research and development such as: agriculture, climate, poverty and child health across Sub-Saharan Africa at the sub-national level. The main goal of the online tool is to present the themes to a wide, even non-technical audience through easily accessible visual narratives.

In its first stage, the platform is focused on national and sub-national level data for 13 countries:

  • Burkina Faso
  • Ethiopia
  • Ghana
  • Kenya
  • Malawi
  • Mali
  • Mozambique
  • Nigeria
  • Rwanda
  • Senegal
  • Tanzania
  • Uganda
  • Zambia

Over time, we anticipate expanding the coverage of the platform with additional countries and increasing the amount of data available through the platform….

The data contained in the online tool draws from a variety of sources, including:

The Implementation of Open Data in Indonesia

Curated on Posted on by Stefaan Verhulst

Paper by Dani Gunawan and Amalia Amalia: “Nowadays, public demands easy access to nonconfidential government data, such as public digital information on health, industry, and culture that can be accessed on the Internet. This will lead departments within government to be efficient and more transparent. As the results, rapid development of applications will solve citizens’ problems in many sectors. One Data Initiatives is the prove that the Government of Indonesia supports data transparency. This research investigates the implementation of open data in Indonesia based on Tim BernersLee five-star rating and open stage model by Kalampokis. The result shows that mostly data in Indonesia is freely available in the Internet, but most of them are not machine-readable and do not support non-proprietary format. The drawback of Indonesia’s open data is lack of ability to link the existing data with other data sources. Therefore, Indonesia is still making initial steps with data inventories and beginning to publish key datasets of public interest…(More)”

Rage against the machines: is AI-powered government worth it?

Curated on Posted on by Stefaan Verhulst

Maëlle Gavet at the WEF: “…the Australian government’s new “data-driven profiling” trial for drug testing welfare recipients, to US law enforcement’s use of facial recognition technology and the deployment of proprietary software in sentencing in many US courts … almost by stealth and with remarkably little outcry, technology is transforming the way we are policed, categorized as citizens and, perhaps one day soon, governed. We are only in the earliest stages of so-called algorithmic regulation — intelligent machines deploying big data, machine learning and artificial intelligence (AI) to regulate human behaviour and enforce laws — but it already has profound implications for the relationship between private citizens and the state….

Some may herald this as democracy rebooted. In my view it represents nothing less than a threat to democracy itself — and deep scepticism should prevail. There are five major problems with bringing algorithms into the policy arena:

  1. Self-reinforcing bias…
  2. Vulnerability to attack…
  3. Who’s calling the shots?…
  4. Are governments up to it?…
  5. Algorithms don’t do nuance….

All the problems notwithstanding, there’s little doubt that AI-powered government of some kind will happen. So, how can we avoid it becoming the stuff of bad science fiction? To begin with, we should leverage AI to explore positive alternatives instead of just applying it to support traditional solutions to society’s perceived problems. Rather than simply finding and sending criminals to jail faster in order to protect the public, how about using AI to figure out the effectiveness of other potential solutions? Offering young adult literacy, numeracy and other skills might well represent a far superior and more cost-effective solution to crime than more aggressive law enforcement. Moreover, AI should always be used at a population level, rather than at the individual level, in order to avoid stigmatizing people on the basis of their history, their genes and where they live. The same goes for the more subtle, yet even more pervasive data-driven targeting by prospective employers, health insurers, credit card companies and mortgage providers. While the commercial imperative for AI-powered categorization is clear, when it targets individuals it amounts to profiling with the inevitable consequence that entire sections of society are locked out of opportunity….(More)”.

Government innovations and the hype cycle

Curated on Posted on by Stefaan Verhulst

Danny Buerkli at the Centre for Public Impact: “The Gartner hype cycle tracks how technologies develop from initial conception to productive use. There is much excitement around different methodologies and technologies in the “government innovation” space, but which of these is hyped and which of these is truly productive?

Last year we made some educated guesses and placed ten government innovations along the hype cycle. This year, however, we went for something bigger and better. We created an entirely non-scientific poll and asked respondents to tell us where they thought these same ten government innovations sat on the hype cycle.

The innovations we included were artificial intelligence, blockchain, design thinking, policy labs, behavioural insights, open data, e-government, agile, lean and New Public Management.

Here is what we learned.

  1. For the most part, we’re still in the early days

On average, our respondents don’t think that any of the methods have made it into truly productive use. In fact, for seven out of the ten innovations, the majority of respondents believed that these were indeed still in the “technology trigger” phase.

Assuming that these innovations will steadily make their way along the hype cycle, we should expect a lot more hype (as they enter the “peak of inflated expectations”) and a lot more disappointment (as they descend into the “trough of disillusionment)” going forward. Government innovation advocates should take heed.

  1. Policy Labs are believed to be in “peak of inflated expectations”

This innovation attracted the highest level of disagreement from respondents. While almost two out of five people believe that policy labs are in the “technology trigger” phase, one out of five see them as having already reached the “slope of enlightenment”. On average, however, respondents believe policy labs to be in the “peak of inflated expectations”….

  1. Blockchain is seen as the most nascent government innovation

Our survey respondents rather unanimously believe that blockchain is at the very early stage of the “technology trigger” phase. Given that blockchain is often characterized as a solution in search of a problem, this view may not be surprising. The survey results also indicates that blockchain will have a long way to go before it will be used productively in government, but there are several ways this can be done.

  1. Artificial intelligence inspires a lot of confidence (in some)…
  1. New Public Management is – still – overhyped?… (More).

‘I’ve Got Nothing to Hide’ and Other Misunderstandings of Privacy

Curated on Posted on by Stefaan Verhulst

“In this short essay, written for a symposium in the San Diego Law Review, Professor Daniel Solove examines the nothing to hide argument. When asked about government surveillance and data mining, many people respond by declaring: “I’ve got nothing to hide.” According to the nothing to hide argument, there is no threat to privacy unless the government uncovers unlawful activity, in which case a person has no legitimate justification to claim that it remain private. The nothing to hide argument and its variants are quite prevalent, and thus are worth addressing. In this essay, Solove critiques the nothing to hide argument and exposes its faulty underpinnings….(More)”