future of work

Intragovernmental Collaborations: Pipedreams or the Future of the Public Sector?

Curated on September 10, 2017August 20, 2018 by Stefaan Verhulst

Sarah Worthing at the Stanford Social Innovation Review: “Despite the need for concerted, joint efforts among public sector leaders, those working with or in government know too well that such collaborations are rare. The motivation and ability to collaborate in government is usually lacking. So how did these leaders—some with competing agendas—manage to do it?

A new tool for collaboration

Policy labs are units embedded within the public sector—“owned” by one or several ministries—that anchor systematic public sector innovation efforts by facilitating creative approaches to policymaking. Since the inception of the first labs over a decade ago, many innovation experts and academics have touted labs as the leading-edge of public policy innovation. They can generate novel, citizen-centric, effective policies and service provisions, because they include a wide range of governmental and, in many cases, non-governmental actors in tackling complex public policy issues like social inequality, mass migration, and terrorism. MindLab in Denmark, for example, brought together government decision makers from across five ministries in December 2007 to co-create policy strategies on tackling climate change while also propelling new business growth. The collaboration resulted in a range of business strategies for climate change that were adopted during the 2009 UN COP15 Summit in Copenhagen. Under normal circumstances, these government leaders often push conflicting agendas, compete over resources, and are highly risk-adverse in undertaking intragovermental partnerships—all “poison pills” for the kind of collaboration successful public sector innovation needs. However, policy labs like MindLab, Policy Lab UK, and almost 100 similar cross-governmental units are finding ways to overcome these barriers and drive public sector innovation.

Five ways policy labs facilitate successful intragovermental collaboration

To examine how labs do this, we conducted a multiple-case analysis of policy labs in the European Union and United States.

1. Reducing potential future conflict through experiential on-boarding processes. Policy labs conduct extensive screening and induction activities to provide policymakers with both knowledge of and faith in the policy lab’s approach to policymaking. …

2. Utilization of spatial cues to flatten hierarchical and departmental differences. Policy labs strategically use non-traditional spatial elements such as moveable whiteboards, tactile and colorful prototyping materials, and sitting cubes, along with the absence of expected elements such as conference tables and chairs, to indicate that unconventional norms—non-hierarchical and relational norms—govern lab spaces….

3. Reframing policy issues to focus on affected citizens. Policy labs highlight individual citizens’ stories to help reconstruct policymakers’ perceptions toward a more common and human-centered understanding of a policy issue…

4. Politically neutral, process-focused facilitation. Lab practitioners employ design methods that can help bring together divided policymakers and break scripted behavior patterns. Many policy labs use variations of design thinking and foresight methods, with a focus on iterative prototyping and testing, stressing the need for skilled but politically neutral facilitation to work through points of conflict and reach consensus on solutions. …

5. Mitigating risk through policy lab branding….(More)”.

Systems Approaches to Public Sector Challenges

Curated on September 8, 2017August 3, 2018 by Stefaan Verhulst

New Report by the OECD: “Complexity is a core feature of most policy issues today and in this context traditional analytical tools and problem-solving methods no longer work. This report, produced by the OECD Observatory of Public Sector Innovation, explores how systems approaches can be used in the public sector to solve complex or “wicked” problems . Consisting of three parts, the report discusses the need for systems thinking in the public sector; identifies tactics that can be employed by government agencies to work towards systems change; and provides an in-depth examination of how systems approaches have been applied in practice. Four cases of applied systems approaches are presented and analysed: preventing domestic violence (Iceland), protecting children (the Netherlands), regulating the sharing economy (Canada) and designing a policy framework to conduct experiments in government (Finland). The report highlights the need for a new approach to policy making that accounts for complexity and allows for new responses and more systemic change that deliver greater value, effectiveness and public satisfaction….(More)”.

These 3 barriers make it hard for policymakers to use the evidence that development researchers produce

Curated on September 7, 2017May 29, 2019 by Stefaan Verhulst

Michael Callen, Adnan Khan, Asim I. Khwaja, Asad Liaqat and Emily Myers at the Monkey Cage/Washington Post: “In international development, the “evidence revolution” has generated a surge in policy research over the past two decades. We now have a clearer idea of what works and what doesn’t. In India, performance pay for teachers works: students in schools where bonuses were on offer got significantly higher test scores. In Kenya, charging small fees for malaria bed nets doesn’t work — and is actually less cost-effective than free distribution. The American Economic Association’s registry for randomized controlled trials now lists 1,287 studies in 106 countries, many of which are testing policies that very well may be expanded.

But can policymakers put this evidence to use?

Here’s how we did our research

We assessed the constraints that keep policymakers from acting on evidence. We surveyed a total of 1,509 civil servants in Pakistan and 108 in India as part of a program called Building Capacity to Use Research Evidence (BCURE), carried out by Evidence for Policy Design (EPoD)at Harvard Kennedy School and funded by the British government. We found that simply presenting evidence to policymakers doesn’t necessarily improve their decision-making. The link between evidence and policy is complicated by several factors.

1. There are serious constraints in policymakers’ ability to interpret evidence….

2. Organizational and structural barriers get in the way of using evidence….

3. When presented with quantitative vs. qualitative evidence, policymakers update their beliefs in unexpected ways....(More)

Automation Beyond the Physical: AI in the Public Sector

Curated on September 6, 2017August 3, 2018 by Stefaan Verhulst

Ben Miller at Government Technology: “…The technology is, by nature, broadly applicable. If a thing involves data — “data” itself being a nebulous word — then it probably has room for AI. AI can help manage the data, analyze it and find patterns that humans might not have thought of. When it comes to big data, or data sets so big that they become difficult for humans to manually interact with, AI leverages the speedy nature of computing to find relationships that might otherwise be proverbial haystack needles.

One early area of government application is in customer service chatbots. As state and local governments started putting information on websites in the past couple of decades, they found that they could use those portals as a means of answering questions that constituents used to have to call an office to ask.

Ideally that results in a cyclical victory: Government offices didn’t have as many calls to answer, so they could devote more time and resources to other functions. And when somebody did call in, their call might be answered faster.

With chatbots, governments are betting they can answer even more of those questions. When he was the chief technology and innovation officer of North Carolina, Eric Ellis oversaw the setup of a system that did just that for IT help desk calls.

Turned out, more than 80 percent of the help desk’s calls were people who wanted to change their passwords. For something like that, where the process is largely the same each time, a bot can speed up the process with a little help from AI. Then, just like with the government Web portal, workers are freed up to respond to the more complicated calls faster….

Others are using AI to recognize and report objects in photographs and videos — guns, waterfowl, cracked concrete, pedestrians, semi-trucks, everything. Others are using AI to help translate between languages dynamically. Some want to use it to analyze the tone of emails. Some are using it to try to keep up with cybersecurity threats even as they morph and evolve. After all, if AI can learn to beat professional poker players, then why can’t it learn how digital black hats operate?

Castro sees another use for the technology, a more introspective one. The problem is this: The government workforce is a lot older than the private sector, and that can make it hard to create culture change. According to U.S. Census Bureau data, about 27 percent of public-sector workers are millennials, compared with 38 percent in the private sector.

“The traditional view [of government work] is you fill out a lot of forms, there are a lot of boring meetings. There’s a lot of bureaucracy in government,” Castro said. “AI has the opportunity to change a lot of that, things like filling out forms … going to routine meetings and stuff.”

As AI becomes more and more ubiquitous, people who work both inside and with government are coming up with an ever-expanding list of ways to use it. Here’s an inexhaustive list of specific use cases — some of which are already up and running and some of which are still just ideas….(More)”.

The Internet of Us

Curated on September 3, 2017July 18, 2019 by Stefaan Verhulst

Book by Michael P. Lynch: “We used to say “seeing is believing”; now, googling is believing. With 24/7 access to nearly all of the world’s information at our fingertips, we no longer trek to the library or the encyclopedia shelf in search of answers. We just open our browsers, type in a few keywords and wait for the information to come to us. Now firmly established as a pioneering work of modern philosophy, The Internet of Us has helped revolutionize our understanding of what it means to be human in the digital age. Indeed, demonstrating that knowledge based on reason plays an essential role in society and that there is more to “knowing” than just acquiring information, leading philosopher Michael P. Lynch shows how our digital way of life makes us value some ways of processing information over others, and thus risks distorting the greatest traits of mankind. Charting a path from Plato’s cave to Google Glass, the result is a necessary guide on how to navigate the philosophical quagmire that is the “Internet of Things.”…(More)”.

Dictionaries and crowdsourcing, wikis and user-generated content

Curated on September 1, 2017August 3, 2018 by Stefaan Verhulst

Living Reference Work Entry by Michael Rundel: “It is tempting to dismiss crowdsourcing as a largely trivial recent development which has nothing useful to contribute to serious lexicography. This temptation should be resisted. When applied to dictionary-making, the broad term “crowdsourcing” in fact describes a range of distinct methods for creating or gathering linguistic data. A provisional typology is proposed, distinguishing three approaches which are often lumped under the heading “crowdsourcing.” These are: user-generated content (UGC), the wiki model, and what is referred to here as “crowd-sourcing proper.” Each approach is explained, and examples are given of their applications in linguistic and lexicographic projects. The main argument of this chapter is that each of these methods – if properly understood and carefully managed – has significant potential for lexicography. The strengths and weaknesses of each model are identified, and suggestions are made for exploiting them in order to facilitate or enhance different operations within the process of developing descriptions of language. Crowdsourcing – in its various forms – should be seen as an opportunity rather than as a threat or diversion….(More)”.

The Case for Sharing All of America’s Data on Mosquitoes

Curated on August 30, 2017August 20, 2018 by Stefaan Verhulst

Ed Yong in the Atlantic: “The U.S. is sitting on one of the largest data sets on any animal group, but most of it is inaccessible and restricted to local agencies….For decades, agencies around the United States have been collecting data on mosquitoes. Biologists set traps, dissect captured insects, and identify which species they belong to. They’ve done this for millions of mosquitoes, creating an unprecedented trove of information—easily one of the biggest long-term attempts to monitor any group of animals, if not the very biggest.

The problem, according to Micaela Elvira Martinez from Princeton University and Samuel Rund from the University of Notre Dame, is that this treasure trove of data isn’t all in the same place, and only a small fraction of it is public. The rest is inaccessible, hoarded by local mosquito-control agencies around the country.

Currently, these agencies can use their data to check if their attempts to curtail mosquito populations are working. Are they doing enough to remove stagnant water, for example? Do they need to spray pesticides? But if they shared their findings, Martinez and Rund say that scientists could do much more. They could better understand the ecology of these insects, predict the spread of mosquito-borne diseases like dengue fever or Zika, coordinate control efforts across states and counties, and quickly spot the arrival of new invasive species.

That’s why Martinez and Rund are now calling for the creation of a national database of mosquito records that anyone can access. “There’s a huge amount of taxpayer investment and human effort that goes into setting traps, checking them weekly, dissecting all those mosquitoes under a microscope, and tabulating the data,” says Martinez. “It would be a big bang for our buck to collate all that data and make it available.”

Martinez is a disease modeler—someone who uses real-world data to build simulations that reveal how infections rise, spread, and fall. She typically works with childhood diseases like measles and polio, where researchers are almost spoiled for data. Physicians are legally bound to report any cases, and the Centers for Disease Control and Prevention (CDC) compiles and publishes this information as a weekly report.

The same applies to cases of mosquito-borne diseases like dengue and Zika, but not to populations of the insects themselves. So, during last year’s Zika epidemic, when Martinez wanted to study the Aedes aegypti mosquito that spreads the disease, she had a tough time. “I was really surprised that I couldn’t find data on Aedes aegypti numbers,” she says. Her colleagues explained that scientists use climate variables like temperature and humidity to predict where mosquitoes are going to be abundant. That seemed ludicrous to her, especially since organizations collect information on the actual insects. It’s just that no one ever gathers those figures together….

Together with Rund and a team of undergraduate students, she found that there are more than 1,000 separate agencies in the United States that collect mosquito data—at least one in every county or jurisdiction. Only 152 agencies make their data publicly available in some way. The team collated everything they could find since 2009, and ended up with information about more than 15 million mosquitoes. Imagine what they’d have if all the datasets were open, especially since some go back decades.

A few mosquito-related databases do exist, but none are quite right. ArboNET, which is managed by the CDC and state health departments, mainly stores data about mosquito-borne diseases, and whatever information it has on the insects themselves isn’t precise enough in either time or space to be useful for modeling. MosquitoNET, which was developed by the CDC, does track mosquitoes, but “it’s a completely closed system, and hardly anyone has access to it,” says Rund. The Smithsonian Institution’s VectorMap is better in that it’s accessible, “but it lacks any real-time data from the continental United States,” says Rund. “When I checked a few months ago, it had just one record of Aedes aegypti since 2013.”…

Some scientists who work on mosquito control apparently disagree, and negative reviews have stopped Martinez and Rund from publishing their ideas in prominent academic journals. (For now, they’ve uploaded a paper describing their vision to the preprint repository bioRxiv.) “Some control boards say: What if people want to sue us because we’re showing that they have mosquito vectors near their homes, or if their house prices go down?” says Martinez. “And one mosquito-control scientist told me that no one should be able to work with mosquito data unless they’ve gone out and trapped mosquitoes themselves.”…

“Data should be made available without having to justify exactly what’s going to be done with it,” Martinez says. “We should put it out there for scientists to start unlocking it. I think there are a ton of biologists who will come up with cool things to do.”…(More)”.

Debating big data: A literature review on realizing value from big data

Curated on August 30, 2017August 3, 2018 by Stefaan Verhulst

Wendy Arianne Günther et al in The Journal of Strategic Information Systems: “Big data has been considered to be a breakthrough technological development over recent years. Notwithstanding, we have as yet limited understanding of how organizations translate its potential into actual social and economic value. We conduct an in-depth systematic review of IS literature on the topic and identify six debates central to how organizations realize value from big data, at different levels of analysis. Based on this review, we identify two socio-technical features of big data that influence value realization: portability and interconnectivity. We argue that, in practice, organizations need to continuously realign work practices, organizational models, and stakeholder interests in order to reap the benefits from big data. We synthesize the findings by means of an integrated model….(More)”.

From Katrina To Harvey: How Disaster Relief Is Evolving With Technology

Curated on August 29, 2017August 3, 2018 by Stefaan Verhulst

Cale Guthrie Weissman at Fast Company: “Open data may sound like a nerdy thing, but this weekend has proven it’s also a lifesaver in more ways than one.

As Hurricane Harvey pelted the southern coast of Texas, a local open-data resource helped provide accurate and up-to-date information to the state’s residents. Inside Harris County’s intricate bayou system–intended to both collect water and effectively drain it–gauges were installed to sense when water is overflowing. The sensors transmit the data to a website, which has become a vital go-to for Houston residents….

This open access to flood gauges is just one of the many ways new tech-driven projects have helped improve responses to disasters over the years. “There’s no question that technology has played a much more significant role,” says Lemaitre, “since even Hurricane Sandy.”

While Sandy was noted in 2012 for its ability to connect people with Twitter hashtags and other relatively nascent social apps like Instagram, the last few years have brought a paradigm shift in terms of how emergency relief organizations integrate technology into their responses….

Social media isn’t just for the residents. Local and national agencies–including FEMA–rely on this information and are using it to help create faster and more effective disaster responses. Following the disaster with Hurricane Katrina, FEMA worked over the last decade to revamp its culture and methods for reacting to these sorts of situations. “You’re seeing the federal government adapt pretty quickly,” says Lemaitre.

There are a few examples of this. For instance, FEMA now has an app to push necessary information about disaster preparedness. The agency also employs people to cull the open web for information that would help make its efforts better and more effective. These “social listeners” look at all the available Facebook, Snapchat, and other social media posts in aggregate. Crews are brought on during disasters to gather intelligence, and then report about areas that need relief efforts–getting “the right information to the right people,” says Lemaitre.

There’s also been a change in how this information is used. Often, when disasters are predicted, people send supplies to the affected areas as a way to try and help out. Yet they don’t know exactly where they should send it, and local organizations sometimes become inundated. This creates a huge logistical nightmare for relief organizations that are sitting on thousands of blankets and tarps in one place when they should be actively dispersing them across hundreds of miles.

“Before, you would just have a deluge of things dropped on top of a disaster that weren’t particularly helpful at times,” says Lemaitre. Now people are using sites like Facebook to ask where they should direct the supplies. For example, after a bad flood in Louisiana last year, a woman announced she had food and other necessities on Facebook and was able to direct the supplies to an area in need. This, says Lemaitre, is “the most effective way.”

Put together, Lemaitre has seen agencies evolve with technology to help create better systems for quicker disaster relief. This has also created a culture of learning updates and reacting in real time. Meanwhile, more data is becoming open, which is helping both people and agencies alike. (The National Weather Service, which has long trumpeted its open data for all, has become a revered stalwart for such information, and has already proven indispensable in Houston.)

Most important, the pace of technology has caused organizations to change their own procedures. Twelve years ago, during Katrina, the protocol was to wait until an assessment before deploying any assistance. Now organizations like FEMA know that just doesn’t work. “You can’t afford to lose time,” says Lemaitre. “Deploy as much as you can and be fast about it–you can always scale back.”

It’s important to note that, even with rapid technological improvements, there’s no way to compare one disaster response to another–it’s simply not apples to apples. All the same, organizations are still learning about where they should be looking and how to react, connecting people to their local communities when they need them most….(More)”.