Digital Government: Managing Public Sector Reform in the Digital Era

Curated on Posted on by Stefaan Verhulst

Book by Miriam Lips: “Digital Government: Managing Public Sector Reform in the Digital Era presents a public management perspective on digital government and technology-enabled change in the public sector. It incorporates theoretical and empirical insights to provide students with a broader and deeper understanding of the complex and multidisciplinary nature of digital government initiatives, impacts, and implications.

The rise of digital government and its increasingly integral role in many government processes and activities, including overseeing fundamental changes at various levels across government, means that it is no longer perceived as just a technology issue. In this book Miriam Lips provides students with practical approaches and perspectives to better understand digital government. The text also explores emerging issues and barriers as well as strategies to more effectively manage digital government and technology-enabled change in the public sector….(More)”.

The State of Open Data

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Open Access Book edited by Tim Davies, Stephen B. Walker, Mor Rubinstein and Fernando Perini: “It’s been ten years since open data first broke onto the global stage. Over the past decade, thousands of programmes and projects around the world have worked to open data and use it to address a myriad of social and economic challenges. Meanwhile, issues related to data rights and privacy have moved to the centre of public and political discourse. As the open data movement enters a new phase in its evolution, shifting to target real-world problems and embed open data thinking into other existing or emerging communities of practice, big questions still remain. How will open data initiatives respond to new concerns about privacy, inclusion, and artificial intelligence? And what can we learn from the last decade in order to deliver impact where it is most needed? 

The State of Open Data brings together over 60 authors from around the world to address these questions and to take stock of the real progress made to date across sectors and around the world, uncovering the issues that will shape the future of open data in the years to come….(More)”.

The Third Pillar: How Markets and the State leave the Community behind

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Book by Raghuram Rajan: “….In The Third Pillar he offers up a magnificent big-picture framework for understanding how these three forces–the state, markets, and our communities–interact, why things begin to break down, and how we can find our way back to a more secure and stable plane. 

The “third pillar” of the title is the community we live in. Economists all too often understand their field as the relationship between markets and the state, and they leave squishy social issues for other people. That’s not just myopic, Rajan argues; it’s dangerous. All economics is actually socioeconomics – all markets are embedded in a web of human relations, values and norms. As he shows, throughout history, technological phase shifts have ripped the market out of those old webs and led to violent backlashes, and to what we now call populism. Eventually, a new equilibrium is reached, but it can be ugly and messy, especially if done wrong. 

Right now, we’re doing it wrong. As markets scale up, the state scales up with it, concentrating economic and political power in flourishing central hubs and leaving the periphery to decompose, figuratively and even literally. Instead, Rajan offers a way to rethink the relationship between the market and civil society and argues for a return to strengthening and empowering local communities as an antidote to growing despair and unrest. Rajan is not a doctrinaire conservative, so his ultimate argument that decision-making has to be devolved to the grass roots or our democracy will continue to wither, is sure to be provocative. But even setting aside its solutions, The Third Pillar is a masterpiece of explication, a book that will be a classic of its kind for its offering of a wise, authoritative and humane explanation of the forces that have wrought such a sea change in our lives….(More)”.

As Surveys Falter Big Data Polling Narrows Our Societal Understanding

Curated on Posted on by Stefaan Verhulst

Kalev Leetaru at Forbes: “One of the most talked-about stories in the world of polling and survey research in recent years has been the gradual death of survey response rates and the reliability of those insights….

The online world’s perceived anonymity has offered some degree of reprieve in which online polls and surveys have often bested traditional approaches in assessing views towards society’s most controversial issues. Yet, here as well increasing public understanding of phishing and online safety are ever more problematic.

The answer has been the rise of “big data” analysis of society’s digital exhaust to fill in the gaps….

Is it truly the same answer though?

Constructing and conducting a well-designed survey means being able to ask the public exactly the questions of interest. Most importantly, it entails being able to ensure representative demographics of respondents.

An online-only poll is unlikely to accurately capture the perspectives of the three quarters of the earth’s population that the digital revolution has left behind. Even within the US, social media platforms are extraordinarily skewed.

The far greater problem is that society’s data exhaust is rarely a perfect match for the questions of greatest interest to policymakers and public.

Cellphone mobility records can offer an exquisitely detailed look at how the people of a city go about their daily lives, but beneath all that blinding light are the invisible members of society not deemed valuable to advertisers and thus not counted. Even for the urban society members whose phones are their ever-present companions, mobility data only goes so far. It can tell us that occupants of a particular part of the city during the workday spend their evenings in a particular part of the city, allowing us to understand their work/life balance, but it offers few insights into their political leanings.

One of the greatest challenges of today’s “big data” surveying is that it requires us to narrow our gaze to only those questions which can be easily answered from the data at hand.

Much as AI’s crisis of bias comes from the field’s steadfast refusal to pay for quality data, settling for highly biased free data, so too has “big data” surveying limited itself largely to datasets it can freely and easily acquire.

The result is that with traditional survey research, we are free to ask the precise questions we are most interested in. With data exhaust research, we must imperfectly shoehorn our questions into the few available metrics. With sufficient creativity it is typically possible to find some way of proxying the given question, but the resulting proxies may be highly unstable, with little understanding of when and where they may fail.

Much like how the early rise of the cluster computing era caused “big data” researchers to limit the questions they asked of their data to just those they could fit into a set of tiny machines, so too has the era of data exhaust surveying forced us to greatly restrict our understanding of society.

Most dangerously, however, big data surveying implicitly means we are measuring only the portion of society our vast commercial surveillance state cares about.

In short, we are only able to measure those deemed of greatest interest to advertisers and thus the most monetizable.

Putting this all together, the decline of traditional survey research has led to the rise of “big data” analysis of society’s data exhaust. Instead of giving us an unprecedented new view into the heartbeat of daily life, this reliance on the unintended output of our digital lives has forced researchers to greatly narrow the questions they can explore and severely skews them to the most “monetizable” portions of society.

In the end, the shift of societal understanding from precision surveys to the big data revolution has led not to an incredible new understanding of what makes us tick, but rather a far smaller, less precise and less accurate view than ever before, just our need to understand ourselves has never been greater….(More)”.

Facebook will open its data up to academics to see how it impacts elections

Curated on Posted on by Stefaan Verhulst

MIT Technology Review: “More than 60 researchers from 30 institutions will get access to Facebook user data to study its impact on elections and democracy, and how it’s used by advertisers and publishers.

A vast trove: Facebook will let academics see which websites its users linked to from January 2017 to February 2019. Notably, that means they won’t be able to look at the platform’s impact on the US presidential election in 2016, or on the Brexit referendum in the UK in the same year.

Despite this slightly glaring omission, it’s still hard to wrap your head around the scale of the data that will be shared, given that Facebook is used by 1.6 billion people every day. That’s more people than live in all of China, the most populous country on Earth. It will be one of the largest data sets on human behavior online to ever be released.

The process: Facebook didn’t pick the researchers. They were chosen by the Social Science Research Council, a US nonprofit. Facebook has been working on this project for over a year, as it tries to balance research interests against user privacy and confidentiality.

Privacy: In a blog post, Facebook said it will use a number of statistical techniques to make sure the data set can’t be used to identify individuals. Researchers will be able to access it only via a secure portal that uses a VPN and two-factor authentication, and there will be limits on the number of queries they can each run….(More)”.

Revisiting the causal effect of democracy on long-run development

Curated on Posted on by Stefaan Verhulst

Blog post by Markus Eberhardt: “In a recent paper, Acemoglu et al. (2019), henceforth “ANRR”, demonstrated a significant and large causal effect of democracy on long-run growth. By adopting a simple binary indicator for democracy, and accounting for the dynamics of development, these authors found that a shift to democracy leads to a 20% higher level of development in the long run.1

The findings are remarkable in three ways: 

  1. Previous research often emphasised that a simple binary measure for democracy was perhaps “too blunt a concept” (Persson and Tabellini 2006) to provide robust empirical evidence.
  2.  Positive effects of democracy on growth were typically only a “short-run boost” (Rodrik and Wacziarg 2005). 
  3. The empirical findings are robust across a host of empirical estimators with different assumptions about the data generating process, including one adopting a novel instrumentation strategy (regional waves of democratisation).

ANRR’s findings are important because, as they highlight in a column on Vox, there is “a belief that democracy is bad for economic growth is common in both academic political economy as well as the popular press.” For example, Posner (2010) wrote that “[d]ictatorship will often be optimal for very poor countries”. 

The simplicity of ANRR’s empirical setup, the large sample of countries, the long time horizon (1960 to 2010), and the robust positive – and remarkably stable – results across the many empirical methods they employ send a very powerful message against such doubts that democracy does cause growth.

I agree with their conclusion, but with qualifications. …(More)”.

Computational Social Science of Disasters: Opportunities and Challenges

Curated on Posted on by Stefaan Verhulst

Paper by Annetta Burger, Talha Oz , William G. Kennedy and Andrew T. Crooks: “Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research.

We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research.

Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments….(More)”.

A weather tech startup wants to do forecasts based on cell phone signals

Curated on Posted on by Stefaan Verhulst

Douglas Heaven at MIT Technology Review: “On 14 April more snow fell on Chicago than it had in nearly 40 years. Weather services didn’t see it coming: they forecast one or two inches at worst. But when the late winter snowstorm came it caused widespread disruption, dumping enough snow that airlines had to cancel more than 700 flights across all of the city’s airports.

One airline did better than most, however. Instead of relying on the usual weather forecasts, it listened to ClimaCell – a Boston-based “weather tech” start-up that claims it can predict the weather more accurately than anyone else. According to the company, its correct forecast of the severity of the coming snowstorm allowed the airline to better manage its schedules and minimize losses due to delays and diversions. 

Founded in 2015, ClimaCell has spent the last few years developing the technology and business relationships that allow it to tap into millions of signals from cell phones and other wireless devices around the world. It uses the quality of these signals as a proxy for local weather conditions, such as precipitation and air quality. It also analyzes images from street cameras. It is offering a weather forecasting service to subscribers that it claims is 60 percent more accurate than that of existing providers, such as NOAA.

The internet of weather

The approach makes sense, in principle. Other forecasters use proxies, such as radar signals. But by using information from millions of everyday wireless devices, ClimaCell claims it has a far more fine-grained view of most of the globe than other forecasters get from the existing network of weather sensors, which range from ground-based devices to satellites. (ClimaCell also taps into these, too.)…(More)”.

How Technology Could Revolutionize Refugee Resettlement

Curated on Posted on by Stefaan Verhulst

Krishnadev Calamur in The Atlantic: “… For nearly 70 years, the process of interviewing, allocating, and accepting refugees has gone largely unchanged. In 1951, 145 countries came together in Geneva, Switzerland, to sign the Refugee Convention, the pact that defines who is a refugee, what refugees’ rights are, and what legal obligations states have to protect them.

This process was born of the idealism of the postwar years—an attempt to make certain that those fleeing war or persecution could find safety so that horrific moments in history, such as the Holocaust, didn’t recur. The pact may have been far from perfect, but in successive years, it was a lifeline to Afghans, Bosnians, Kurds, and others displaced by conflict.

The world is a much different place now, though. The rise of populism has brought with it a concomitant hostility toward immigrants in general and refugees in particular. Last October, a gunman who had previously posted anti-Semitic messages online against HIAS killed 11 worshippers in a Pittsburgh synagogue. Many of the policy arguments over resettlement have shifted focus from humanitarian relief to security threats and cost. The Trump administration has drastically cut the number of refugees the United States accepts, and large parts of Europe are following suit.

If it works, Annie could change that dynamic. Developed at Worcester Polytechnic Institute in Massachusetts, Lund University in Sweden, and the University of Oxford in Britain, the software uses what’s known as a matching algorithm to allocate refugees with no ties to the United States to their new homes. (Refugees with ties to the United States are resettled in places where they have family or community support; software isn’t involved in the process.)

Annie’s algorithm is based on a machine learning model in which a computer is fed huge piles of data from past placements, so that the program can refine its future recommendations. The system examines a series of variables—physical ailments, age, levels of education and languages spoken, for example—related to each refugee case. In other words, the software uses previous outcomes and current constraints to recommend where a refugee is most likely to succeed. Every city where HIAS has an office or an affiliate is given a score for each refugee. The higher the score, the better the match.

This is a drastic departure from how refugees are typically resettled. Each week, HIAS and the eight other agencies that allocate refugees in the United States make their decisions based largely on local capacity, with limited emphasis on individual characteristics or needs….(More)”.

AI & Global Governance: Robots Will Not Only Wage Future Wars but also Future Peace

Curated on Posted on by Stefaan Verhulst

Daanish Masood & Martin Waehlisch at the United Nations University: “At the United Nations, we have been exploring completely different scenarios for AI: its potential to be used for the noble purposes of peace and security. This could revolutionize the way of how we prevent and solve conflicts globally.

Two of the most promising areas are Machine Learning and Natural Language Processing. Machine Learning involves computer algorithms detecting patterns from data to learn how to make predictions and recommendations. Natural Language Processing involves computers learning to understand human languages.

At the UN Secretariat, our chief concern is with how these emerging technologies can be deployed for the good of humanity to de-escalate violence and increase international stability.

This endeavor has admirable precedent. During the Cold War, computer scientists used multilayered simulations to predict the scale and potential outcome of the arms race between the East and the West.

Since then, governments and international agencies have increasingly used computational models and advanced Machine Learning to try to understand recurrent conflict patterns and forecast moments of state fragility.

But two things have transformed the scope for progress in this field.

The first is the sheer volume of data now available from what people say and do online. The second is the game-changing growth in computational capacity that allows us to crunch unprecedented, inconceivable quantities data with relative speed and ease.

So how can this help the United Nations build peace? Three ways come to mind.

Firstly, overcoming cultural and language barriers. By teaching computers to understand human language and the nuances of dialects, not only can we better link up what people write on social media to local contexts of conflict, we can also more methodically follow what people say on radio and TV. As part of the UN’s early warning efforts, this can help us detect hate speech in a place where the potential for conflict is high. This is crucial because the UN often works in countries where internet coverage is low, and where the spoken languages may not be well understood by many of its international staff.

Natural Language Processing algorithms can help to track and improve understanding of local debates, which might well be blind spots for the international community. If we combine such methods with Machine Learning chatbots, the UN could conduct large-scale digital focus groups with thousands in real-time, enabling different demographic segments in a country to voice their views on, say, a proposed peace deal – instantly testing public support, and indicating the chances of sustainability.

Secondly, anticipating the deeper drivers of conflict. We could combine new imaging techniques – whether satellites or drones – with automation. For instance, many parts of the world are experiencing severe groundwater withdrawal and water aquifer depletion. Water scarcity, in turn, drives conflicts and undermines stability in post-conflict environments, where violence around water access becomes more likely, along with large movements of people leaving newly arid areas.

One of the best predictors of water depletion is land subsidence or sinking, which can be measured by satellite and drone imagery. By combining these imaging techniques with Machine Learning, the UN can work in partnership with governments and local communities to anticipate future water conflicts and begin working proactively to reduce their likelihood.

Thirdly, advancing decision making. In the work of peace and security, it is surprising how many consequential decisions are still made solely on the basis of intuition.

Yet complex decisions often need to navigate conflicting goals and undiscovered options, against a landscape of limited information and political preference. This is where we can use Deep Learning – where a network can absorb huge amounts of public data and test it against real-world examples on which it is trained while applying with probabilistic modeling. This mathematical approach can help us to generate models of our uncertain, dynamic world with limited data.

With better data, we can eventually make better predictions to guide complex decisions. Future senior peace envoys charged with mediating a conflict would benefit from such advances to stress test elements of a peace agreement. Of course, human decision-making will remain crucial, but would be informed by more evidence-driven robust analytical tools….(More)”.