China seeks glimpse of citizens’ future with crime-predicting AI

Curated on Posted on by Stefaan Verhulst

Yuan Yang, Yingzhi Yang and Sherry Fei Ju in the Financial Times: “China, a surveillance state where authorities have unchecked access to citizens’ histories, is seeking to look into their future with technology designed to predict and prevent crime. Companies are helping police develop artificial intelligence they say will help them identify and apprehend suspects before criminal acts are committed. “If we use our smart systems and smart facilities well, we can know beforehand . . . who might be a terrorist, who might do something bad,” Li Meng, vice-minister of science and technology, said on Friday.

Facial recognition company Cloud Walk has been trialling a system that uses data on individuals’ movements and behaviour — for instance visits to shops where weapons are sold — to assess their chances of committing a crime. Its software warns police when a citizen’s crime risk becomes dangerously high, allowing the police to intervene. “The police are using a big-data rating system to rate highly suspicious groups of people based on where they go and what they do,” a company spokesperson told the Financial Times. Risks rise if the individual “frequently visits transport hubs and goes to suspicious places like a knife store”, the spokesperson added. China’s authoritarian government has always amassed personal data to monitor and control its citizens — whether they are criminals or suspected of politically sensitive activity. But new technology, from phones and computers to fast-developing AI software, is amplifying its capabilities. These are being used to crack down on even the most minor of infractions — facial recognition cameras, for instance, are also being used to identify and shame jaywalkers, according to state media. Mr Li said crime prediction would become an important use for AI technology in the government sphere.

China’s crime-prediction technology relies on several AI techniques, including facial recognition and gait analysis, to identify people from surveillance footage. In addition, “crowd analysis” can be used to detect “suspicious” patterns of behaviour in crowds, for example to single out thieves from normal passengers at a train stations. As well as tracking people with a criminal history, Cloud Walk’s technology is being used to monitor “high-risk” places such as hardware stores…(More)”

Crowdsourcing Expertise to Increase Congressional Capacity

Curated on Posted on by Stefaan Verhulst

Austin Seaborn at Beeck Center: “Members of Congress have close connections with their districts, and information arising from local organizations, such as professional groups, academia, industry as well as constituents with relevant expertise (like retirees, veterans or students) is highly valuable to them.  Today, congressional staff capacity is at a historic low, while at the same time, constituents in districts are often well equipped to address the underlying policy questions that Congress seeks to solve….

In meetings we have had with House and Senate staffers, they repeatedly express both the difficulty managing their substantial area-specific work loads and their interest in finding ways to substantively engage constituents to find good nuggets of information to help them in their roles as policymakers. At the same time, constituents are demanding more transparency and dialogue from their elected representatives. In many cases, our project brings these two together. It allows Members to tap the expertise in their districts while at the same time creating an avenue for constituents to contribute their knowledge and area expertise to the legislative process. It’s a win for constituents and a win for Member of Congress and their staffs.

It is important to note that the United States lags behind other democracies in experimenting with more inclusive methods during the policymaking process. In the United Kingdom, for example, the UK Parliament has experimented with a variety of new digital tools to engage with constituents. These methods range from Twitter hashtags, which are now quite common given the rise in social media use by governments and elected officials, to a variety of web forums on a variety of platforms. Since June of 2015, they have also been doing digital debates, where questions from the general public are crowdsourced and later integrated into a parliamentary debate by the Member of Parliament leading the debate. Estonia, South Africa, Taiwan, France also…notable examples.

One promising new development we hope to explore more thoroughly is the U.S. Library of Congress’s recently announced legislative data App Challenge. This competition is distinct from the many hackathons that have been held on behalf of Congress in the past, in that this challenge seeks new methods not only to innovate, but also to integrate and legislate. In his announcement, the Library’s Chief Information Officer, Bernard A. Barton, Jr., stated, “An informed citizenry is better able to participate in our democracy, and this is a very real opportunity to contribute to a better understanding of the work being done in Washington.  It may even provide insights for the people doing the work around the clock, both on the Hill, and in state and district offices.  Your innovation and integration may ultimately benefit the way our elected officials legislate for our future.” We believe these sorts of new methods will play a crucial role in the future of engaging citizens in their democracies….(More)”.

The DeepMind debacle demands dialogue on data

Curated on Posted on by Stefaan Verhulst

Hetan Shah in Nature: “Without public approval, advances in how we use data will stall. That is why a regulator’s ruling against the operator of three London hospitals is about more than mishandling records from 1.6 million patients. It is a missed opportunity to have a conversation with the public about appropriate uses for their data….

What can be done to address this deficit? Beyond meeting legal standards, all relevant institutions must take care to show themselves trustworthy in the eyes of the public. The lapses of the Royal Free hospitals and DeepMind provide, by omission, valuable lessons.

The first is to be open about what data are transferred. The extent of data transfer between the Royal Free and DeepMind came to light through investigative journalism. In my opinion, had the project proceeded under open contracting, it would have been subject to public scrutiny, and to questions about whether a company owned by Google — often accused of data monopoly — was best suited to create a relatively simple app.

The second lesson is that data transfer should be proportionate to the task. Information-sharing agreements should specify clear limits. It is unclear why an app for kidney injury requires the identifiable records of every patient seen by three hospitals over a five-year period.

Finally, governance mechanisms must be strengthened. It is shocking to me that the Royal Free did not assess the privacy impact of its actions before handing over access to records. DeepMind does deserve credit for (belatedly) setting up an independent review panel for health-care projects, especially because the panel has a designated budget and has not required members to sign non-disclosure agreements. (The two groups also agreed a new contract late last year, after criticism.)

More is needed. The Information Commissioner asked the Royal Free to improve its processes but did not fine it or require it to rescind data. This rap on the knuckles is unlikely to deter future, potentially worse, misuses of data. People are aware of the potential for over-reach, from the US government’s demands for state voter records to the Chinese government’s alleged plans to create a ‘social credit’ system that would monitor private behaviour.

Innovations such as artificial intelligence, machine learning and the Internet of Things offer great opportunities, but will falter without a public consensus around the role of data. To develop this, all data collectors and crunchers must be open and transparent. Consider how public confidence in genetic modification was lost in Europe, and how that has set back progress.

Public dialogue can build trust through collaborative efforts. A 14-member Citizen’s Reference Panel on health technologies was convened in Ontario, Canada in 2009. The Engage2020 programme incorporates societal input in the Horizon2020 stream of European Union science funding….(More)”

Active Citizenship in Europe: Practices and Demands in the EU, Italy, Turkey and the UK

Curated on Posted on by Stefaan Verhulst

Book by Cristiano Bee: “…provides an overview of key issues in the debate concerning the emergence of active citizenship in Europe.

The specific focus of enquiry is the promotion of patterns of civic and political engagement and civic and political participation by the EU and the relative responses drawn by organizations of the civil society operating at the supranational level and in Italy, Turkey and the UK. More specifically, it addresses key debates on the engagement and participation of organized civil society across the permanent state of euro-crisis, considering the production of policy discourses along the continuum that characterized three subsequent and interrelated emergency situations (democratic, financial and migration crises) that have hit Europe since 2005. …(More)”.

How Africa’s Data Revolution Can Deliver Sustainable Development Outcomes

Curated on Posted on by Stefaan Verhulst

Donald Mogeni at Huffington Post: “…As a demonstration of this political will, several governments in Africa are blazing the trail in numerous ways. For instance, the Government of Senegal now considers investment in data as important as it would treat investment in physical infrastructure such as roads. In Ghana and Sierra Leone, more policy-makers and legislators are now using data to inform their work and make planning is continuously evidence-based.

Despite the progressive developments, several cautionary statements are worth noting. Firstly, data is not a silver-bullet to addressing present development challenges and/or problems. To be transformative, use of data and evidence must include political agency and citizen mobilization. Thus, while data may highlight important development cleavages, it may not guarantee change if not used appropriately within the various political contexts. ‘Everyone Counts’, a new global initiative by CARE, KWANTU and World Vision (that was also showcased in the meeting) seeks to contribute to this agenda.

Secondly, there is need for data ‘experts’ to move beyond the chronic obsession with big numbers to ensure greater inclusion of marginalised and vulnerable segments of the population. Achieving this will require a ‘business unusual’ approach that devises better data collection methodologies and technologies that must collect more and better than ever before. This ‘new’ data should then be used together with administrative and open data to ensure that ‘no one is left behind’.

Thirdly, the utility of citizen-generated data is still contentious – especially within state institutions. Increasing the value of this data must therefore involve standardization of data collection tools and methodologies across the board (to the extent possible), making consideration for ethical approvals, subjecting this data to quality audits and triangulation, as well as adhering to quality assurance standards.

Fourthly, the emergence of various data communities within African countries has made the roles of National Statistical Offices in the data ecosystem even more crucial. However, significant capacity and technical disparities exist between the various National Statistical Offices (NSOs) in Africa. To realise the potential of data and statistics in achieving sustainable development outcomes, financial and human capacities of these institutions must to be enhanced….(More)”.

A New Framework for Free Movement of Data

Curated on Posted on by Stefaan Verhulst
Lisbon Council: “How can we make Europe a leader in the global data economy? How can we make sure that the important advances in data analytics – the diseases that will be cured, the traffic congestions alleviated, the social problems correctly analysed – are there for citizens to enjoy and companies and institutions to develop? In this ground-breaking study, the Lisbon Council explores A New Framework for Free Movement of Data in the Digital Age: Making Europe a Data Economy. The paper analyses an array of state-of-the-art proposals for facilitating data flows and proposes a three-point roadmap for improving the “free movement of data” in Europe: adopt “once-only” at the European level; strengthen European-level cyber security and crack down on unjustified data localisation; and develop more open and transparent policies for data sharing around a new concept of “co-ownership.”…(More)”.

Digital Government Units: Origins, Orthodoxy and Critical Considerations for Public Management Theory and Practice

Curated on Posted on by Stefaan Verhulst

Paper by Amanda Clarke: “From 2011 onwards, Digital Government Units (DGUs) have quickly emerged as a preferred solution for tackling the over-cost and under-performing digital services and lagging digital transformation agendas plaguing today’s governments. DGUs represent a common machinery of government phenomenon insofar as they all exist at the centre of the state, and adopt a shared orthodoxy, favouring agile, user-centric design, open-source technologies, pluralistic procurement, data-driven decision-making, horizontal ‘platform’ based solutions and a ‘delivery-first’ ethos. However, DGUs are differentiated in practice by their governance structures, resources and powers, adding notable complexity to this recent public management trend. Acknowledging the speedy policy transfer that has seen DGUs spread globally despite a lack of critical appraisal of their value and shortcomings, the paper highlights four critical considerations that governments and their observers should account for when assessing DGUs as a potential instrument of digital era public management renewal….(More)”.

Open data on universities – New fuel for transformation

Curated on Posted on by Stefaan Verhulst

François van Schalkwyk at University World News: “Accessible, usable and relevant open data on South African universities makes it possible for a wide range of stakeholders to monitor, advise and challenge the transformation of South Africa’s universities from an informed perspective.

Some describe data as the new oil while others suggest it is a new form of capital or compare it to electricity. Either way, there appears to be a groundswell of interest in the potential of data to fuel development.

Whether the proliferation of data is skewing development in favour of globally networked elites or disrupting existing asymmetries of information and power, is the subject of ongoing debate. Certainly, there are those who will claim that open data, from a development perspective, could catalyse disruption and redistribution.

Open data is data that is free to use without restriction. Governments and their agencies, universities and their researchers, non-governmental organisations and their donors, and even corporations, are all potential sources of open data.

Open government data, as a public rather than a private resource, embedded in principles of universal access, participation and transparency, is touted as being able to restore the deteriorating levels of trust between citizens and their governments.

Open data promises to do so by making the decisions and processes of the state more transparent and inclusive, empowering citizens to participate and to hold public institutions to account for the distribution of public services and resources.

Benefits of open data

Open data has other benefits over its more cloistered cousins (data in private networks, big data, etc). By democratising access, open data makes possible the use of data on, for example, health services, crime, the environment, procurement and education by a range of different users, each bringing their own perspective to bear on the data. This can expose bias in the data or may improve the quality of the data by surfacing data errors. Both are important when data is used to shape government policies.

By removing barriers to reusing data such as copyright or licence-fees, tech-savvy entrepreneurs can develop applications to assist the public to make more informed decisions by making available easy-to-understand information on medicine prices, crime hot-spots, air quality, beneficial ownership, school performance, etc. And access to open research data can improve quality and efficiency in science.

Scientists can check and confirm the data on which important discoveries are based if the data is open, and, in some cases, researchers can reuse open data from other studies, saving them the cost and effort of collecting the data themselves.

‘Open washing’

But access alone is not enough for open data to realise its potential. Open data must also be used. And data is used if it holds some value for the user. Governments have been known to publish server rooms full of data that no one is interested in to support claims of transparency and supporting the knowledge economy. That practice is called ‘open washing’. …(More)”

Democracy Promotion: An Objective of U.S. Foreign Assistance

Curated on Posted on by Stefaan Verhulst

New Report by Congressional Research Service: “Promoting democratic institutions, processes, and values has long been a U.S. foreign policy objective, though the priority given to this objective has been inconsistent. World events, competing priorities, and political change within the United States all shape the attention and resources provided to democracy promotion efforts and influence whether such efforts focus on supporting fair elections abroad, strengthening civil society, promoting rule of law and human rights, or other aspects of democracy promotion.

Proponents of democracy promotion often assert that such efforts are essential to global development and U.S. security because stable democracies tend to have better economic growth and stronger protection of human rights, and are less likely to go to war with one another. Critics contend that U.S. relations with foreign countries should focus exclusively on U.S. interests and stability in the world order. U.S. interest in global stability, regardless of the democratic nature of national political systems, could discourage U.S. support for democratic transitions—the implementation of which is uncertain and may lead to more, rather than less, instability.

Funding for democracy promotion assistance is deeply integrated into U.S. foreign policy institutions. More than $2 billion annually has been allocated from foreign assistance funds over the past decade for democracy promotion activities managed by the State Department, the U.S. Agency for International Development, the National Endowment for Democracy, and other entities. Programs promoting good governance (characterized by participation, transparency, accountability, effectiveness, and equity), rule of law, and promotion of human rights have typically received the largest share of this funding in contrast to lower funding to promote electoral processes and political competition. In recent years, increasing restrictions imposed by some foreign governments on civil society organizations have resulted in an increased emphasis in democracy promotion assistance for strengthening civil society.

Despite bipartisan support for the general concept of democracy promotion, policy debates in the 115th Congress continue to question the consistency, effectiveness, and appropriateness of such foreign assistance. With the Trump Administration indicating that democracy and human rights might not be a top foreign policy priority, advocates in Congress may be challenged to find common ground with the Administration on this issue.

As part of its budget and oversight responsibilities, the 115th Congress may consider the impact of the Trump Administration’s requested FY2018 foreign assistance spending cuts on U.S. democracy promotion assistance, review the effectiveness of democracy promotion activities, evaluate the various channels available for democracy promotion, and consider where democracy promotion ranks among a wide range of foreign policy and budget priorities….(More)”.

Uber Releases Open Source Project for Differential Privacy

Curated on Posted on by Stefaan Verhulst

Katie Tezapsidis at Uber Security: “Data analysis helps Uber continuously improve the user experience by preventing fraud, increasing efficiency, and providing important safety features for riders and drivers. Data gives our teams timely feedback about what we’re doing right and what needs improvement.

Uber is committed to protecting user privacy and we apply this principle throughout our business, including our internal data analytics. While Uber already has technical and administrative controls in place to limit who can access specific databases, we are adding additional protections governing how that data is used — even in authorized cases.

We are excited to give a first glimpse of our recent work on these additional protections with the release of a new open source tool, which we’ll introduce below.

Background: Differential Privacy

Differential privacy is a formal definition of privacy and is widely recognized by industry experts as providing strong and robust privacy assurances for individuals. In short, differential privacy allows general statistical analysis without revealing information about a particular individual in the data. Results do not even reveal whether any individual appears in the data. For this reason, differential privacy provides an extra layer of protection against re-identification attacks as well as attacks using auxiliary data.

Differential privacy can provide high accuracy results for the class of queries Uber commonly uses to identify statistical trends. Consequently, differential privacy allows us to calculate aggregations (averages, sums, counts, etc.) of elements like groups of users or trips on the platform without exposing information that could be used to infer details about a specific user or trip.

Differential privacy is enforced by adding noise to a query’s result, but some queries are more sensitive to the data of a single individual than others. To account for this, the amount of noise added must be tuned to the sensitivity of the query, which is defined as the maximum change in the query’s output when an individual’s data is added to or removed from the database.

As part of their job, a data analyst at Uber might need to know the average trip distance in a particular city. A large city, like San Francisco, might have hundreds of thousands of trips with an average distance of 3.5 miles. If any individual trip is removed from the data, the average remains close to 3.5 miles. This query therefore has low sensitivity, and thus requires less noise to enable each individual to remain anonymous within the crowd.

Conversely, the average trip distance in a smaller city with far fewer trips is more influenced by a single trip and may require more noise to provide the same degree of privacy. Differential privacy defines the precise amount of noise required given the sensitivity.

A major challenge for practical differential privacy is how to efficiently compute the sensitivity of a query. Existing methods lack sufficient support for the features used in Uber’s queries and many approaches require replacing the database with a custom runtime engine. Uber uses many different database engines and replacing these databases is infeasible. Moreover, custom runtimes cannot meet Uber’s demanding scalability and performance requirements.

Introducing Elastic Sensitivity

To address these challenges we adopted Elastic Sensitivity, a technique developed by security researchers at the University of California, Berkeley for efficiently calculating the sensitivity of a query without requiring changes to the database. The full technical details of Elastic Sensitivity are described here.

Today, we are excited to share a tool developed in collaboration with these researchers to calculate Elastic Sensitivity for SQL queries. The tool is available now on GitHub. It is designed to integrate easily with existing data environments and support additional state-of-the-art differential privacy mechanisms, which we plan to share in the coming months….(More)”.