Can AI tools replace feds?

Curated on Posted on by Stefaan Verhulst

Derek B. Johnson at FCW: “The Heritage Foundation…is calling for increased reliance on automation and the potential creation of a “contractor cloud” offering streamlined access to private sector labor as part of its broader strategy for reorganizing the federal government.

Seeking to take advantage of a united Republican government and a president who has vowed to reform the civil service, the foundation drafted a pair of reports this year attempting to identify strategies for consolidating, merging or eliminating various federal agencies, programs and functions. Among those strategies is a proposal for the Office of Management and Budget to issue a report “examining existing government tasks performed by generously-paid government employees that could be automated.”

Citing research on the potential impacts of automation on the United Kingdom’s civil service, the foundation’s authors estimated that similar efforts across the U.S. government could yield $23.9 billion in reduced personnel costs and a reduction in the size of the federal workforce by 288,000….

The Heritage report also called on the federal government to consider a “contracting cloud.” The idea would essentially be for a government version of TaskRabbit, where agencies could select from a pool of pre-approved individual contractors from the private sector who could be brought in for specialized or seasonal work without going through established contracts. Greszler said the idea came from speaking with subcontractors who complained about having to kick over a certain percentage of their payments to prime contractors even as they did all the work.

Right now the foundation is only calling for the government to examine the potential of the issue and how it would interact with existing or similar vehicles for contracting services like the GSA schedule. Greszler emphasized that any pool of workers would need to be properly vetted to ensure they met federal standards and practices.

“There has to be guidelines or some type of checks, so you’re not having people come off the street and getting access to secure government data,” she said….(More)

Crowdsourcing citizen science: exploring the tensions between paid professionals and users

Curated on Posted on by Stefaan Verhulst

Jamie Woodcock et al in the Journal of Peer Production: “This paper explores the relationship between paid labour and unpaid users within the Zooniverse, a crowdsourced citizen science platform. The platform brings together a crowd of users to categorise data for use in scientific projects. It was initially established by a small group of academics for a single astronomy project, but has now grown into a multi-project platform that has engaged over 1.3 million users so far. The growth has introduced different dynamics to the platform as it has incorporated a greater number of scientists, developers, links with organisations, and funding arrangements—each bringing additional pressures and complications. The relationships between paid/professional and unpaid/citizen labour have become increasingly complicated with the rapid expansion of the Zooniverse. The paper draws on empirical data from an ongoing research project that has access to both users and paid professionals on the platform. There is the potential through growing peer-to-peer capacity that the boundaries between professional and citizen scientists can become significantly blurred. The findings of the paper, therefore, address important questions about the combinations of paid and unpaid labour, the involvement of a crowd in citizen science, and the contradictions this entails for an online platform. These are considered specifically from the viewpoint of the users and, therefore, form a new contribution to the theoretical understanding of crowdsourcing in practice….(More)”.

Open & Shut

Curated on Posted on by Stefaan Verhulst

Harsha Devulapalli: “Welcome to Open & Shut — a new blog dedicated to exploring the opportunities and challenges of working with open data in closed societies around the world. Although we’ll be exploring questions relevant to open data practitioners worldwide, we’re particularly interested in seeing how civil society groups and actors in the Global South are using open data to push for greater government transparency, and tackle daunting social and economic challenges facing their societies….Throughout this series we’ll be profiling and interviewing organisations working with open data worldwide, and providing do-it-yourself data tutorials that will be useful for beginners as well as data experts. …

What do we mean by the terms ‘open data’ and ‘closed societies’?

It’s important to be clear about what we’re dealing with, here. So let’s establish some key terms. When we talk about ‘open data’, we mean data that anyone can access, use and share freely. And when we say ‘closed societies’, we’re referring to states or regions in which the political and social environment is actively hostile to notions of openness and public scrutiny, and which hold principles of freedom of information in low esteem. In closed societies, data is either not published at all by the government, or else is only published in inaccessible formats, is missing data, is hard to find or else is just not digitised at all.

Iran is one such state that we would characterise as a ‘closed society’. At Small Media, we’ve had to confront the challenges of poor data practice, secrecy, and government opaqueness while undertaking work to support freedom of information and freedom of expression in the country. Based on these experiences, we’ve been working to build Iran Open Data — a civil society-led open data portal for Iran, in an effort to make Iranian government data more accessible and easier for researchers, journalists, and civil society actors to work with.

Iran Open Data — an open data portal for Iran, created by Small Media

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..Open & Shut will shine a light on the exciting new ways that different groups are using data to question dominant narratives, transform public opinion, and bring about tangible change in closed societies. At the same time, it’ll demonstrate the challenges faced by open data advocates in opening up this valuable data. We intend to get the community talking about the need to build cross-border alliances in order to empower the open data movement, and to exchange knowledge and best practices despite the different needs and circumstances we all face….(More)“

Inside the Lab That’s Quantifying Happiness

Curated on Posted on by Stefaan Verhulst

Rowan Jacobsen at Outside: “In Mississippi, people tweet about cake and cookies an awful lot; in Colorado, it’s noodles. In Mississippi, the most-tweeted activity is eating; in Colorado, it’s running, skiing, hiking, snowboarding, and biking, in that order. In other words, the two states fall on opposite ends of the behavior spectrum. If you were to assign a caloric value to every food mentioned in every tweet by the citizens of the United States and a calories-burned value to every activity, and then totaled them up, you would find that Colorado tweets the best caloric ratio in the country and Mississippi the worst.

Sure, you’d be forgiven for doubting people’s honesty on Twitter. On those rare occasions when I destroy an entire pint of Ben and Jerry’s, I most assuredly do not tweet about it. Likewise, I don’t reach for my phone every time I strap on a pair of skis.

And yet there’s this: Mississippi has the worst rate of diabetes and heart disease in the country and Colorado has the best. Mississippi has the second-highest percentage of obesity; Colorado has the lowest. Mississippi has the worst life expectancy in the country; Colorado is near the top. Perhaps we are being more honest on social media than we think. And perhaps social media has more to tell us about the state of the country than we realize.

That’s the proposition of Peter Dodds and Chris Danforth, who co-direct the University of Vermont’s Computational Story Lab, a warren of whiteboards and grad students in a handsome brick building near the shores of Lake Champlain. Dodds and Danforth are applied mathematicians, but they would make a pretty good comedy duo. When I stopped by the lab recently, both were in running clothes and cracking jokes. They have an abundance of curls between them and the wiry energy of chronic thinkers. They came to UVM in 2006 to start the Vermont Complex Systems Center, which crunches big numbers from big systems and looks for patterns. Out of that, they hatched the Computational Story Lab, which sifts through some of that public data to discern the stories we’re telling ourselves. “It took us a while to come up with the name,” Dodds told me as we shotgunned espresso and gazed into his MacBook. “We were going to be the Department of Recreational Truth.”

This year, they teamed up with their PhD student Andy Reagan to launch the Lexicocalorimeter, an online tool that uses tweets to compute the calories in and calories out for every state. It’s no mere party trick; the Story Labbers believe the Lexicocalorimeter has important advantages over slower, more traditional methods of gathering health data….(More)”.

Ireland Opens E-Health Open Data Portal

Curated on Posted on by Stefaan Verhulst

Adi Gaskell at HuffPost: “… an open data portal has been launched by eHealth Ireland.  The portal aims to bring together some 300 different open data sources into one place, making it easier to find data from across the Irish Health Sector.

The portal includes data from a range of sources, including statistics on hospital day and inpatient cases, waiting list statistics and information around key new digital initiatives.

Open data

The resource features datasets from both the Department of Health and HealthLink, so the team believe that the data is of the highest quality, and also compliant with the Open Health Data Policy.  This ensures that the approach taken with the release of data is consistent and in accordance with national and international guidelines.

“I am delighted to welcome the launch of the eHealth Ireland Open Data Portal today. The aim of Open Data is twofold; on the one hand facilitating transparency of the Public Sector and on the other providing a valuable resource that can drive innovation. The availability of Open Data can empower citizens and support clinicians, care providers, and researchers make better decisions, spur new innovations and identify efficiencies while ensuring that personal data remains confidential,” Richard Corbridge, CIO at the Health Service Executive says.

Data from both HealthLink and the National Treatment Purchase Fund (NTPF) will be uploaded to the portal each month, with new datasets due to be added on a regular basis….

The project follows a number of clearly defined Open Health Data Principles that are designed to support the health service in the provision of better patient care and in the support of new innovations in the sector, all whilst ensuring that patient data is secured and governed appropriately…(More)”.

Artificial Intelligence for Citizen Services and Government

Curated on Posted on by Stefaan Verhulst

Paper by Hila Mehr: “From online services like Netflix and Facebook, to chatbots on our phones and in our homes like Siri and Alexa, we are beginning to interact with artificial intelligence (AI) on a near daily basis. AI is the programming or training of a computer to do tasks typically reserved for human intelligence, whether it is recommending which movie to watch next or answering technical questions. Soon, AI will permeate the ways we interact with our government, too. From small cities in the US to countries like Japan, government agencies are looking to AI to improve citizen services.

While the potential future use cases of AI in government remain bounded by government resources and the limits of both human creativity and trust in government, the most obvious and immediately beneficial opportunities are those where AI can reduce administrative burdens, help resolve resource allocation problems, and take on significantly complex tasks. Many AI case studies in citizen services today fall into five categories: answering questions, filling out and searching documents, routing requests, translation, and drafting documents. These applications could make government work more efficient while freeing up time for employees to build better relationships with citizens. With citizen satisfaction with digital government offerings leaving much to be desired, AI may be one way to bridge the gap while improving citizen engagement and service delivery.

Despite the clear opportunities, AI will not solve systemic problems in government, and could potentially exacerbate issues around service delivery, privacy, and ethics if not implemented thoughtfully and strategically. Agencies interested in implementing AI can learn from previous government transformation efforts, as well as private-sector implementation of AI. Government offices should consider these six strategies for applying AI to their work: make AI a part of a goals-based, citizen-centric program; get citizen input; build upon existing resources; be data-prepared and tread carefully with privacy; mitigate ethical risks and avoid AI decision making; and, augment employees, do not replace them.

This paper explores the various types of AI applications, and current and future uses of AI in government delivery of citizen services, with a focus on citizen inquiries and information. It also offers strategies for governments as they consider implementing AI….(More)”

Algorithmic regulation: A critical interrogation

Curated on Posted on by Stefaan Verhulst

Karen Yeung in Regulation and Governance: “Innovations in networked digital communications technologies, including the rise of “Big Data,” ubiquitous computing, and cloud storage systems, may be giving rise to a new system of social ordering known as algorithmic regulation. Algorithmic regulation refers to decisionmaking systems that regulate a domain of activity in order to manage risk or alter behavior through continual computational generation of knowledge by systematically collecting data (in real time on a continuous basis) emitted directly from numerous dynamic components pertaining to the regulated environment in order to identify and, if necessary, automatically refine (or prompt refinement of) the system’s operations to attain a pre-specified goal. This study provides a descriptive analysis of algorithmic regulation, classifying these decisionmaking systems as either reactive or pre-emptive, and offers a taxonomy that identifies eight different forms of algorithmic regulation based on their configuration at each of the three stages of the cybernetic process: notably, at the level of standard setting (adaptive vs. fixed behavioral standards), information-gathering and monitoring (historic data vs. predictions based on inferred data), and at the level of sanction and behavioral change (automatic execution vs. recommender systems). It maps the contours of several emerging debates surrounding algorithmic regulation, drawing upon insights from regulatory governance studies, legal critiques, surveillance studies, and critical data studies to highlight various concerns about the legitimacy of algorithmic regulation….(More)”.

The Tech Revolution That’s Changing How We Measure Poverty

Curated on Posted on by Stefaan Verhulst

Alvin Etang Ndip at the Worldbank: “The world has an ambitious goal to end extreme poverty by 2030. But, without good poverty data, it is impossible to know whether we are making progress, or whether programs and policies are reaching those who are the most in need.

Countries, often in partnership with the World Bank Group and other agencies, measure poverty and wellbeing using household surveys that help give policymakers a sense of who the poor are, where they live, and what is holding back their progress. Once a paper-and-pencil exercise, technology is beginning to revolutionize the field of household data collection, and the World Bank is tapping into this potential to produce more and better poverty data….

“Technology can be harnessed in three different ways,” says Utz Pape, an economist with the World Bank. “It can help improve data quality of existing surveys, it can help to increase the frequency of data collection to complement traditional household surveys, and can also open up new avenues of data collection methods to improve our understanding of people’s behaviors.”

As technology is changing the field of data collection, researchers are continuing to find new ways to build on the power of mobile phones and tablets.

The World Bank’s Pulse of South Sudan initiative, for example, takes tablet-based data collection a step further. In addition to conducting the household survey, the enumerators also record a short, personalized testimonial with the people they are interviewing, revealing a first-person account of the situation on the ground. Such testimonials allow users to put a human face on data and statistics, giving a fuller picture of the country’s experience.

Real-time data through mobile phones

At the same time, more and more countries are generating real-time data through high-frequency surveys, capitalizing on the proliferation of mobile phones around the world. The World Bank’s Listening to Africa (L2A) initiative has piloted the use of mobile phones to regularly collect information on living conditions. The approach combines face-to-face surveys with follow-up mobile phone interviews to collect data that allows to monitor well-being.

The initiative hands out mobile phones and solar chargers to all respondents. To minimize the risk of people dropping out, the respondents are given credit top-ups to stay in the program. From monitoring health care facilities in Tanzania to collecting data on frequency of power outages in Togo, the initiative has been rolled out in six countries and has been used to collect data on a wide range of areas. …

Technology-driven data collection efforts haven’t been restricted to the Africa region alone. In fact, the approach was piloted early in Peru and Honduras with the Listening 2 LAC program. In Europe and Central Asia, the World Bank has rolled out the Listening to Tajikistan program, which was designed to monitor the impact of the Russian economic slowdown in 2014 and 2015. Initially a six-month pilot, the initiative has now been in operation for 29 months, and a partnership with UNICEF and JICA has ensured that data collection can continue for the next 12 months. Given the volume of data, the team is currently working to create a multidimensional fragility index, where one can monitor a set of well-being indicators – ranging from food security to quality jobs and public services – on a monthly basis…

There are other initiatives, such as in Mexico where the World Bank and its partners are using satellite imagery and survey data to estimate how many people live below the poverty line down to the municipal level, or guiding data collectors using satellite images to pick a representative sample for the Somali High Frequency Survey. However, despite the innovation, these initiatives are not intended to replace traditional household surveys, which still form the backbone of measuring poverty. When better integrated, they can prove to be a formidable set of tools for data collection to provide the best evidence possible to policymakers….(More)”

Mastercard’s Big Data For Good Initiative: Data Philanthropy On The Front Lines

Curated on Posted on by Stefaan Verhulst

Interview by Randy Bean of Shamina Singh: Much has been written about big data initiatives and the efforts of market leaders to derive critical business insights faster. Less has been written about initiatives by some of these same firms to apply big data and analytics to a different set of issues, which are not solely focused on revenue growth or bottom line profitability. While the focus of most writing has been on the use of data for competitive advantage, a small set of companies has been undertaking, with much less fanfare, a range of initiatives designed to ensure that data can be applied not just for corporate good, but also for social good.

One such firm is Mastercard, which describes itself as a technology company in the payments industry, which connects buyers and sellers in 210 countries and territories across the globe. In 2013 Mastercard launched the Mastercard Center for Inclusive Growth, which operates as an independent subsidiary of Mastercard and is focused on the application of data to a range of issues for social benefit….

In testimony before the Senate Committee on Foreign Affairs on May 4, 2017, Mastercard Vice Chairman Walt Macnee, who serves as the Chairman of the Center for Inclusive Growth, addressed issues of private sector engagement. Macnee noted, “The private sector and public sector can each serve as a force for good independently; however when the public and private sectors work together, they unlock the potential to achieve even more.” Macnee further commented, “We will continue to leverage our technology, data, and know-how in an effort to solve many of the world’s most pressing problems. It is the right thing to do, and it is also good for business.”…

Central to the mission of the Mastercard Center is the notion of “data philanthropy”. This term encompasses notions of data collaboration and data sharing and is at the heart of the initiatives that the Center is undertaking. The three cornerstones on the Center’s mandate are:

  • Sharing Data Insights– This is achieved through the concept of “data grants”, which entails granting access to proprietary insights in support of social initiatives in a way that fully protects consumer privacy.
  • Data Knowledge – The Mastercard Center undertakes collaborations with not-for-profit and governmental organizations on a range of initiatives. One such effort was in collaboration with the Obama White House’s Data-Driven Justice Initiative, by which data was used to help advance criminal justice reform. This initiative was then able, through the use of insights provided by Mastercard, to demonstrate the impact crime has on merchant locations and local job opportunities in Baltimore.
  • Leveraging Expertise – Similarly, the Mastercard Center has collaborated with private organizations such as DataKind, which undertakes data science initiatives for social good.Just this past month, the Mastercard Center released initial findings from its Data Exploration: Neighborhood Crime and Local Business initiative. This effort was focused on ways in which Mastercard’s proprietary insights could be combined with public data on commercial robberies to help understand the potential relationships between criminal activity and business closings. A preliminary analysis showed a spike in commercial robberies followed by an increase in bar and nightclub closings. These analyses help community and business leaders understand factors that can impact business success.Late last year, Ms. Singh issued A Call to Action on Data Philanthropy, in which she challenges her industry peers to look at ways in which they can make a difference — “I urge colleagues at other companies to review their data assets to see how they may be leveraged for the benefit of society.” She concludes, “the sheer abundance of data available today offers an unprecedented opportunity to transform the world for good.”….(More)

Data Responsibility: Social Responsibility for a Data Age

Curated on Posted on by Stefaan Verhulst

TED-X Talk by Stefaan Verhulst: “In April 2015, the Gorkha earthquake hit Nepal—the worst in more than 80 years. Hundreds of thousands of people were rendered homeless and entire villages were flattened. The earthquake also triggered massive avalanches on Mount Everest, and ultimately killed nearly 9,000 people across the country.

Yet for all the destruction, the toll could have been far greater. Without mitigating or in any way denying the horrible disaster that hit Nepal that day, the responsible use of data helped avoid a worse calamity and may offer lessons for other disasters around the world.

Following the earthquake, government and civil society organizations rushed in to address the humanitarian crisis. Notably, so did the private sector. Nepal’s largest mobile operator, Ncell, for example, decided to share its mobile data—in an aggregated, de-identified way—with the the nonprofit Swedish organization Flowminder. Flowminder then used this data to map population movements around the country; these real-time maps allowed the government and humanitarian organizations to better target aid and relief to affected communities, thus maximizing the impact of their efforts.

The initiative has been widely lauded as a model for cross-sector collaboration. But what is perhaps most striking about the initiative is the way it used data—in particular, how it repurposed data originally collected for private purposes for public ends. This use of corporate data for wider social impact reflects the emerging concept of “data responsibility.” …

 

The Three Pillars of Data Responsibility

1. Share. This is perhaps the most evident: Data holders have a duty to share private data when a clear case exists that it serves the public good. There now exists manifold evidence that data—with appropriate oversight—can help improve lives, as we saw in Nepal.

2. Protect. The consequences of failing to protect data are well documented. The most obvious problems occur when data is not properly anonymized or when de-anonymized data leaks into the public domain. But there are also more subtle cases, when ostensibly anonymized data is itself susceptible to de-anonymization, and information released for the public good ends up causing or potentially causing harm.

3. Act. For the data to really serve the public good, officials and others must create policies and interventions based on the insights they gain from it. Without action, the potential remains just that—mere potential, never translated into concrete results….(Watch TEDx Video).

See also International Data Responsibility Group and Data Collaboratives Project.