How games can help craft better policy

Curated on Posted on by Stefaan Verhulst

Shrabonti Bagchi at LiveMint: “I have never seen economists having fun!” Anantha K. Duraiappah, director of Unesco-MGIEP (Mahatma Gandhi Institute of Education for Peace and Sustainable Development), was heard exclaiming during a recent conference. The academics in question were a group of environmental economists at an Indian Society for Ecological Economics conference in Thrissur, Kerala, and they were playing a game called Cantor’s World, in which each player assumes the role of the supreme leader of a country and gets to decide the fate of his or her nation.

Well, it’s not quite as simple as that (this is not Settlers Of Catan!). Players have to take decisions on long-term goals like education and industrialization based on data such as GDP, produced capital, human capital, and natural resources while adhering to the UN’s sustainable development goals. The game is probably the most accessible and enjoyable way of seeing how long-term policy decisions change and impact the future of countries.

That’s what Fields Of View does. The Bengaluru-based non-profit creates games, simulations and learning tools for the better understanding of policy and its impact. Essentially, their work is to make sure economists like the ones at the Thrissur conference actually have some fun while thrashing out crucial issues of public policy.

A screen grab from ‘Cantor’s World’.

A screen grab from ‘Cantor’s World’.

Can policymaking be made more relevant to the lives of people affected by it? Can policymaking be more responsive to a dynamic social-economic-environmental context? Can we reduce the time taken for a policy to go from the drawing board to implementation? These were some of the questions the founders of Fields Of View, Sruthi Krishnan and Bharath M. Palavalli, set out to answer. “There are no binaries in policymaking. There are an infinite set of possibilities,” says Palavalli, who was named an Ashoka fellow in May for his work at the intersection of technology, social sciences and design.

Earlier this year, Fields Of View organized a session of one of its earliest games, City Game, for a group of 300 female college students in Mangaluru. City Game is a multiplayer offline game designed to explore urban infrastructure and help groups and individual understand the dynamics of urban governance…(More)”.

On the Bumpy Road Towards Open Government: The Not-Invented-Here Syndrome as a Major Pothole

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Paper by Lisa Schmidthuber, David Antons and Dennis Hilgers: “This paper investigates the role of public employees in absorbing external knowledge. Triggered by open government initiatives and open calls for participation, external actors are invited to integrate ideas, solutions, or experience into public organizations. Such exploitation of valuable external knowledge across organizational interfaces might, however, be hindered by negative attitudes of public employees towards external input. The rejection of outside knowledge by internal actors is known as the Not-Invented-Here syndrome. This paper sheds light on NIH attitudes in public organizations. After reviewing the state of the art of research on NIH, it emphasizes rethinking NIH in the public sector. The in-depth discussion of previous work thus serves to derive an extensive agenda for future research….(More)”

The Case for Accountability: How it Enables Effective Data Protection and Trust in the Digital Society

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Centre for Information Policy Leadership: “Accountability now has broad international support and has been adopted in many laws, including in the EU General Data Protection Regulation (GDPR), regulatory policies and organisational practices. It is essential that there is consensus and clarity on the precise meaning and application of organisational accountability among all stakeholders, including organisations implementing accountability and data protection authorities (DPAs) overseeing accountability.

Without such consensus, organisations will not know what DPAs expect of them and DPAs will not know how to assess organisations’ accountability-based privacy programs with any degree of consistency and predictability. Thus, drawing from the global experience with accountability to date and from the Centre for Information Policy Leadership’s (CIPL) own extensive prior work on accountability, this paper seeks to explain the following issues:

  • The concept of organisational accountability and how it is reflected in the GDPR;
  • The essential elements of accountability and how the requirements of the GDPR (and of other normative frameworks) map to these elements;
  • Global acceptance and adoption of accountability;
  • How organisations can implement accountability (including by and between controllers and processors) through comprehensive internal privacy programs that implement external rules or the organisation’s own data protection policies and goals, or through verified or certified accountability mechanisms, such as Binding Corporate Rules (BCR), APEC Cross-Border Privacy Rules (CBPR), APEC Privacy Recognition for Processors (PRP), other seals and certifications, including future GDPR certifications and codes of conduct; and
  • The benefits that accountability can deliver to each stakeholder group.

In addition, the paper argues that accountability exists along a spectrum, ranging from basic accountability requirements required by law (such as under the GDPR) to stronger and more granular accountability measures that may not be required by law but that organisations may nevertheless want to implement because they convey substantial benefits….(More)”.

Collective Awareness

Curated on Posted on by Stefaan Verhulst

A Conversation with J. Doyne Farmer at the Edge: “Economic failures cause us serious problems. We need to build simulations of the economy at a much more fine-grained level that take advantage of all the data that computer technologies and the Internet provide us with. We need new technologies of economic prediction that take advantage of the tools we have in the 21st century.

Places like the US Federal Reserve Bank make predictions using a system that has been developed over the last eighty years or so. This line of effort goes back to the middle of the 20th century, when people realized that we needed to keep track of the economy. They began to gather data and set up a procedure for having firms fill out surveys, for having the census take data, for collecting a lot of data on economic activity and processing that data. This system is called “national accounting,” and it produces numbers like GDP, unemployment, and so on. The numbers arrive at a very slow timescale. Some of the numbers come out once a quarter, some of the numbers come out once a year. The numbers are typically lagged because it takes a lot of time to process the data, and the numbers are often revised as much as a year or two later. That system has been built to work in tandem with the models that have been built, which also process very aggregated, high-level summaries of what the economy is doing. The data is old fashioned and the models are old fashioned.

It’s a 20th-century technology that’s been refined in the 21st century. It’s very useful, and it represents a high level of achievement, but it is now outdated. The Internet and computers have changed things. With the Internet, we can gather rich, detailed data about what the economy is doing at the level of individuals. We don’t have to rely on surveys; we can just grab the data. Furthermore, with modern computer technology we could simulate what 300 million agents are doing, simulate the economy at the level of the individuals. We can simulate what every company is doing and what every bank is doing in the United States. The model we could build could be much, much better than what we have now. This is an achievable goal.

But we’re not doing that, nothing close to that. We could achieve what I just said with a technological system that’s simpler than Google search. But we’re not doing that. We need to do it. We need to start creating a new technology for economic prediction that runs side-by-side with the old one, that makes its predictions in a very different way. This could give us a lot more guidance about where we’re going and help keep the economic shit from hitting the fan as often as it does….(More)”.

A model to help tech companies make responsible technology a reality

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Sam Brown at DotEveryone: “..adopting a Responsible Technology approach isn’t straightforward. There’s currently no roadmap, or even any common language, about how to embed responsible technology practices in practical and tangible ways.

That’s why Doteveryone has spent the last year researching the issues organisations face and we’re now developing a model that will help organisations do just that.

The 3C model helps to guide organisations on how to assess the level of responsibility of their technology products or services as they develop them.

It’s not an ethical bible which dictates right from wrong, but a framework which gives teams space and parameters to foresee the potential impacts their technologies could have and to consider how to handle them.

Our 3C Model of Responsible Technology considers:

  1. the Context of the wider world a technology product or service exists within
  2. the potential ways technology can have unintended Consequences
  3. the different Contribution people make to a technology — how value is given and received

We are developing a number of assessment tools which product teams can work through to help them examine and evaluate each of these areas in real time during the development cycle. The form of the assessments range from checklists to step-by-step information mapping to team board games….(More)”.

How Charities Are Using Artificial Intelligence to Boost Impact

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Nicole Wallace at the Chronicle of Philanthropy: “The chaos and confusion of conflict often separate family members fleeing for safety. The nonprofit Refunite uses advanced technology to help loved ones reconnect, sometimes across continents and after years of separation.

Refugees register with the service by providing basic information — their name, age, birthplace, clan and subclan, and so forth — along with similar facts about the people they’re trying to find. Powerful algorithms search for possible matches among the more than 1.1 million individuals in the Refunite system. The analytics are further refined using the more than 2,000 searches that the refugees themselves do daily.

The goal: find loved ones or those connected to them who might help in the hunt. Since Refunite introduced the first version of the system in 2010, it has helped more than 40,000 people reconnect.

One factor complicating the work: Cultures define family lineage differently. Refunite co-founder Christopher Mikkelsen confronted this problem when he asked a boy in a refugee camp if he knew where his mother was. “He asked me, ‘Well, what mother do you mean?’ ” Mikkelsen remembers. “And I went, ‘Uh-huh, this is going to be challenging.’ ”

Fortunately, artificial intelligence is well suited to learn and recognize different family patterns. But the technology struggles with some simple things like distinguishing the image of a chicken from that of a car. Mikkelsen believes refugees in camps could offset this weakness by tagging photographs — “car” or “not car” — to help train algorithms. Such work could earn them badly needed cash: The group hopes to set up a system that pays refugees for doing such work.

“To an American, earning $4 a day just isn’t viable as a living,” Mikkelsen says. “But to the global poor, getting an access point to earning this is revolutionizing.”

Another group, Wild Me, a nonprofit created by scientists and technologists, has created an open-source software platform that combines artificial intelligence and image recognition, to identify and track individual animals. Using the system, scientists can better estimate the number of endangered animals and follow them over large expanses without using invasive techniques….

To fight sex trafficking, police officers often go undercover and interact with people trying to buy sex online. Sadly, demand is high, and there are never enough officers.

Enter Seattle Against Slavery. The nonprofit’s tech-savvy volunteers created chatbots designed to disrupt sex trafficking significantly. Using input from trafficking survivors and law-enforcement agencies, the bots can conduct simultaneous conversations with hundreds of people, engaging them in multiple, drawn-out conversations, and arranging rendezvous that don’t materialize. The group hopes to frustrate buyers so much that they give up their hunt for sex online….

A Philadelphia charity is using machine learning to adapt its services to clients’ needs.

Benefits Data Trust helps people enroll for government-assistance programs like food stamps and Medicaid. Since 2005, the group has helped more than 650,000 people access $7 billion in aid.

The nonprofit has data-sharing agreements with jurisdictions to access more than 40 lists of people who likely qualify for government benefits but do not receive them. The charity contacts those who might be eligible and encourages them to call the Benefits Data Trust for help applying….(More)”.

What is a data trust?

Curated on Posted on by Stefaan Verhulst

Essay by Jack Hardinges at ODI: “There are different interpretations of what a data trust is, or should be…

There’s not a well-used definition of ‘a data trust’, or even consensus on what one is. Much of the recent interest in data trusts in the UK has been fuelled by them being recommended as a way to ‘share data in a fair, safe and equitable way’ by a UK government-commissioned independent review into Artificial Intelligence (AI) in 2017. However, there has been wider international interest in the concept for some time.

At a very high level, the aim of data trusts appears to be to give people and organisations confidence when enabling access to data in ways that provide them with some value (either directly or indirectly) in return. Beyond that high level goal, there are a variety of thoughts about what form they should take. In our work so far, we’ve found different interpretations of the term ‘data trust’:

  • A data trust as a repeatable framework of terms and mechanisms.
  • A data trust as a mutual organisation.
  • A data trust as a legal structure.
  • A data trust as a store of data.
  • A data trust as public oversight of data access….(More)”

Hope for Democracy: 30 years of Participatory Budgeting Worldwide

Curated on Posted on by Stefaan Verhulst

Book edited by Nelson Dias: “Hope for Democracy” is not only the title of this book, but also the translation of a state of mind infected by innovation and transformative action of many people who in different parts of the world, are engaged in the construction of more lasting and intense ways of living democracy.

The articles found within this publication are “scales” of a fascinating journey through the paths of participatory democracy, from North America to Asia, Oceania to Europe, and Latin America to Africa.

With no single directions, it is up to the readers to choose the route they want to travel, being however invited to reinforce this “democratizing wave”, encouraging the emergence of new and renewed spaces of participation in the territories where they live and work….(More)

What if people were paid for their data?

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The Economist: “Data Slavery” Jennifer Lyn Morone, an American artist, thinks this is the state in which most people now live. To get free online services, she laments, they hand over intimate information to technology firms. “Personal data are much more valuable than you think,” she says. To highlight this sorry state of affairs, Ms Morone has resorted to what she calls “extreme capitalism”: she registered herself as a company in Delaware in an effort to exploit her personal data for financial gain. She created dossiers containing different subsets of data, which she displayed in a London gallery in 2016 and offered for sale, starting at £100 ($135). The entire collection, including her health data and social-security number, can be had for £7,000.

Only a few buyers have taken her up on this offer and she finds “the whole thing really absurd”. ..Given the current state of digital affairs, in which the collection and exploitation of personal data is dominated by big tech firms, Ms Morone’s approach, in which individuals offer their data for sale, seems unlikely to catch on. But what if people really controlled their data—and the tech giants were required to pay for access? What would such a data economy look like?…

Labour, like data, is a resource that is hard to pin down. Workers were not properly compensated for labour for most of human history. Even once people were free to sell their labour, it took decades for wages to reach liveable levels on average. History won’t repeat itself, but chances are that it will rhyme, Mr Weyl predicts in “Radical Markets”, a provocative new book he has co-written with Eric Posner of the University of Chicago. He argues that in the age of artificial intelligence, it makes sense to treat data as a form of labour.

To understand why, it helps to keep in mind that “artificial intelligence” is something of a misnomer. Messrs Weyl and Posner call it “collective intelligence”: most AI algorithms need to be trained using reams of human-generated examples, in a process called machine learning. Unless they know what the right answers (provided by humans) are meant to be, algorithms cannot translate languages, understand speech or recognise objects in images. Data provided by humans can thus be seen as a form of labour which powers AI. As the data economy grows up, such data work will take many forms. Much of it will be passive, as people engage in all kinds of activities—liking social-media posts, listening to music, recommending restaurants—that generate the data needed to power new services. But some people’s data work will be more active, as they make decisions (such as labelling images or steering a car through a busy city) that can be used as the basis for training AI systems….

But much still needs to happen for personal data to be widely considered as labour, and paid for as such. For one thing, the right legal framework will be needed to encourage the emergence of a new data economy. The European Union’s new General Data Protection Regulation, which came into effect in May, already gives people extensive rights to check, download and even delete personal data held by companies. Second, the technology to keep track of data flows needs to become much more capable. Research to calculate the value of particular data to an AI service is in its infancy.

Third, and most important, people will have to develop a “class consciousness” as data workers. Most people say they want their personal information to be protected, but then trade it away for nearly nothing, something known as the “privacy paradox”. Yet things may be changing: more than 90% of Americans think being in control of who can get data on them is important, according to the Pew Research Centre, a think-tank….(More)”.

Smart Cities: Digital Solutions for a More Livable Future

Curated on Posted on by Stefaan Verhulst

Report by the McKinsey Global Institute (MGI): “After a decade of experimentation, smart cities are entering a new phase. Although they are only one part of the full tool kit for making a city great, digital solutions are the most powerful and cost-effective additions to that tool kit in many years. This report analyzes dozens of current applications and finds that cities could use them to improve some quality-of-life indicators by 10–30 percent.It also finds that even the most cutting-edge smart cities on the planet are still at the beginning of their journey. ƒ

Smart cities add digital intelligence to existing urban systems, making it possible to do more with less. Connected applications put real-time, transparent information into the hands of users to help them make better choices. These tools can save lives, prevent crime, and reduce the disease burden. They can save time, reduce waste, and even help boost social connectedness. When cities function more efficiently, they also become more productive places to do business. ƒ

MGI assessed how dozens of current smart city applications could perform in three sample cities with varying legacy infrastructure systems and baseline starting points. We found that these tools could reduce fatalities by 8–10 percent, accelerate emergency response times by 20–35 percent, shave the average commute by 15–20 percent, lower the disease burden by 8–15 percent, and cut greenhouse gas emissions by 10–15 percent, among other positive outcomes. ƒ

Our snapshot of deployment in 50 cities around the world shows that wealthier urban areas are generally transforming faster, although many have low public awareness and usage of the applications they have implemented. Asian megacities, with their young populations of digital natives and big urban problems to solve, are achieving exceptionally high adoption. Measured against what is possible today, even the global leaders have more work to do in building out the technology base, rolling out the full range of possible applications, and boosting adoption and user satisfaction. Many cities have not yet implemented some of the applications that could have the biggest potential impact. Since technology never stands still, the bar will only get higher. ƒ

The public sector would be the natural owner of 70 percent of the applications we examined. But 60 percent of the initial investment required to implement the full range of applications could come from private actors. Furthermore, more than half of the initial investment made by the public sector could generate a positive return, whether in direct savings or opportunities to produce revenue. ƒ

The technologies analyzed in this report can help cities make moderate or significant progress toward 70 percent of the Sustainable Development Goals. Yet becoming a smart city is less effective as an economic development strategy for job creation. ƒ Smart cities may disrupt some industries even as they present substantial market opportunities. Customer needs will force a reevaluation of current products and services to meet higher expectations of quality, cost, and efficiency in everything from mobility to healthcare.

Smart city solutions will shift value across the landscape of cities and throughout value chains. Companies looking to enter smart city markets will need different skill sets, creative financing models, and a sharper focus on civic engagement.

Becoming a smart city is not a goal but a means to an end. The entire point is to respond more effectively and dynamically to the needs and desires of residents. Technology is simply a tool to optimize the infrastructure, resources, and spaces they share. Few cities want to lag behind, but it is critical not to get caught up in technology for its own sake. Smart cities need to focus on improving outcomes for residents and enlisting their active participation in shaping the places they call home….(More)”.