Open data in action: initiatives during the initial stage of the COVID-19 pandemic


Report by OECD and The GovLab: “The COVID-19 pandemic has increased the demand for access to timely, relevant, and quality data. This demand has been driven by several needs: taking informed policy actions quickly, improving communication on the current state of play, carrying out scientific analysis of a dynamic threat, understanding its social and economic impact, and enabling civil society oversight and reporting.


This report…assesses how open government data (OGD) was used to react and respond to the COVID-19 pandemic during initial stage of the crisis (March-July 2020) based on initiatives collected through an open call for evidence. It also seeks to transform lessons learned into considerations for policy makers on how to improve OGD policies to better prepare for future shocks…(More)”.

The (Im)possibility of Fairness: Different Value Systems Require Different Mechanisms For Fair Decision Making


Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian at Communications of the ACM: “Automated decision-making systems (often machine learning-based) now commonly determine criminal sentences, hiring choices, and loan applications. This widespread deployment is concerning, since these systems have the potential to discriminate against people based on their demographic characteristics. Current sentencing risk assessments are racially biased, and job advertisements discriminate on gender. These concerns have led to an explosive growth in fairness-aware machine learning, a field that aims to enable algorithmic systems that are fair by design.

To design fair systems, we must agree precisely on what it means to be fair. One such definition is individual fairness: individuals who are similar (with respect to some task) should be treated similarly (with respect to that task). Simultaneously, a different definition states that demographic groups should, on the whole, receive similar decisions. This group fairness definition is inspired by civil rights law in the U.S. and U.K. Other definitions state that fair systems should err evenly across demographic groups. Many of these definitions have been incorporated into machine learning pipelines.

In this article, we introduce a framework for understanding these different definitions of fairness and how they relate to each other. Crucially, our framework shows these definitions and their implementations correspond to different axiomatic beliefs about the world. We present two such worldviews and will show they are fundamentally incompatible. First, one can believe the observation processes that generate data for machine learning are structurally biased. This belief provides a justification for seeking non-discrimination. When one believes that demographic groups are, on the whole, fundamentally similar, group fairness mechanisms successfully guarantee the top-level goal of non-discrimination: similar groups receiving similar treatment. Alternatively, one can assume the observed data generally reflects the true underlying reality about differences between people. These worldviews are in conflict; a single algorithm cannot satisfy either definition of fairness under both worldviews. Thus, researchers and practitioners ought to be intentional and explicit about world-views and value assumptions: the systems they design will always encode some belief about the world….(More)”.

The World Happiness Report 2021


Report by the Sustainable Development Solutions Network: “There has been surprising resilience in how people rate their lives overall. The Gallup World Poll data are confirmed for Europe by the separate Eurobarometer surveys and several national surveys.

  • The change from 2017-2019 to 2020 varied considerably among countries, but not enough to change rankings in any significant fashion materially. The same countries remain at the top.
  • Emotions changed more than did life satisfaction during the first year of COVID-19, worsening more during lockdown and recovering faster, as illustrated by large samples of UK data. For the world as a whole, based on the annual data from the Gallup World Poll, there was no overall change in positive affect, but there was a roughly 10% increase in the number of people who said they were worried or sad the previous day.
  • Trust and the ability to count on others are major supports to life evaluations, especially in the face of crises. To feel that your lost wallet would be returned if found by a police officer, by a neighbour, or a stranger, is estimated to be more important for happiness than income, unemployment, and major health risks (see Figure 2.4 in chapter 2)
  • Trust is even more important in explaining the very large international differences in COVID-19 death rates, which were substantially higher in the Americas and Europe than in East Asia, Australasia, and Africa, as shown here (see Figure 2.5 of chapter 2). These differences were almost half due to differences in the age structure of populations (COVID-19 much more deadly for the old), whether the country is an island, and how exposed each country was, early in the pandemic, to large numbers of infections in nearby countries. Whatever the initial circumstances, the most effective strategy for controlling COVID-19 was to drive community transmission to zero and to keep it there. Countries adopting this strategy had death rates close to zero, and were able to avoid deadly second waves, and ended the year with less loss of income and lower death rates.
  • Factors supporting successful COVID-19 strategies include
    • confidence in public institutions. Trusted public institutions were more likely to choose the right strategy and have their populations support the required actions. For example, Brazil’s death rate was 93 per 100,000, higher than in Singapore, and of this difference, over a third could be explained by the difference in public trust….(More)”

Coming wave of video games could build empathy on racism, environment and aftermath of war


Mike Snider at USA Today: “Some of the newest video games in development aren’t really games at all, but experiences that seek to build empathy for others.

Among the five such projects getting funding grants and support from 3D software engine maker Unity is “Our America,” in which the player takes the role of a Black man who is driving with his son when their car is pulled over by a police officer.

The father worries about getting his car registration from the glove compartment because the officer “might think it’s a gun or something,” the character says in the trailer.

On the project’s website, the developers describe “Our America” as “an autobiographical VR Experience” in which “the audience must make quick decisions, answer questions – but any wrong move is the difference between life and death.”…

The other Unity for Humanity winners include:

  • Ahi Kā Rangers: An ecological mobile game with development led by Māori creators. 
  • Dot’s Home: A game that explores historical housing injustices faced by Black and brown home buyers. 
  • Future Aleppo: A VR experience for children to rebuild homes and cities destroyed by war. 
  • Samudra: A children’s environmental puzzle game that takes the player across a polluted sea to learn about pollution and plastic waste.

While “Our America” may serve best as a VR experience, other projects such as “Dot’s Home” may be available on mobile devices to expand its accessibility….(More)”.

European Data Economy: Between Competition and Regulation


Report by René Arnold, Christian Hildebrandt, and Serpil Taş: “Data and its economic impact permeates all sectors of the economy. The data economy is not a new sector, but more like a challenge for all firms to compete and innovate as part of a new wave of economic value creation.

With data playing an increasingly important role across all sectors of the economy, the results of this report point European policymakers to promote the development and adoption of unified reference architectures. These architectures constitute a technology-neutral and cross-sectoral approach that will enable companies small and large to compete and to innovate—unlocking the economic potential of data capture in an increasingly digitized world.

Data access appears to be less of a hindrance to a thriving data economy due to the net increase in capabilities in data capture, elevation, and analysis. What does prove difficult for firms is discovering existing datasets and establishing their suitability for achieving their economic objectives. Reference architectures can facilitate this process as they provide a framework to locate potential providers of relevant datasets and carry sufficient additional information (metadata) about datasets to enable firms to understand whether a particular dataset, or parts of it, fits their purpose.

Whether third-party data access is suitable to solve a specific business task in the first place ought to be a decision at the discretion of the economic actors involved. As our report underscores, data captured in one context with a specific purpose may not be fit for another context or another purpose. Consequently, a firm has to evaluate case-by-case whether first-party data capture, third-party data access, or a mixed approach is the best solution. This evaluation will naturally depend on whether there is any other firm capturing data suitable for the task that is willing to negotiate conditions for third-party access to this data. Unified data architectures may also lower the barriers for a firm capturing suitable data to engage in negotiations, since its adoption will lower the costs of making the data ready for a successful exchange. Such architectures may further integrate licensing provisions ensuring that data, once exchanged, is not used beyond the agreed purpose. It can also bring in functions that improve the discoverability of potential data providers….(More)”.

How can we measure productivity in the public sector?


Ravi Somani at the World Bank: “In most economies, the public sector is a major purchaser of goods, services and labor. According to the Worldwide Bureaucracy Indicators, globally the public sector accounts for around 25% of GDP and 38% of formal employment. Generating efficiency gains in the public sector can, therefore, have important implications for a country’s overall economic performance.  

Public-sector productivity measures the rate with which inputs are converted into desirable outputs in the public sector. Measures can be developed at the level of the employee, organization, or overall public sector, and can be tracked over time. Such information allows policymakers to identify good and bad performers, understand what might be correlated with good performance, and measure the returns to different types of public expenditures. This knowledge can be used to improve the allocation of public resources in the future and maximize the impact of the public purse.

But how can we measure it?

However, measuring productivity in the public sector can be tricky because:

  • There are often no market transactions for public services, or they are distorted by subsidies and other market imperfections.
  • Many public services are complex, requiring (often immeasurable) inputs from multiple individuals and organizations.
  • There is often a substantial time lag between investments in inputs and the realization of outputs and outcomes.

This recent World Bank publication provides a summary of the different approaches to measuring productivity in the public sector, presented in the table below.  For simplicity, the approaches are separated into: ‘macro’ approaches, which provide aggregate information at the level of an organization, sector, or service as a whole; and ‘micro’ approaches, which can be applied to the individual employee, task, project, and process.   
 

Macro and Micro Approaches to measure public-sector productivity

There is no silver bullet for accurately measuring public-sector productivity – each approach has its own limitations.  For example, the cost-weighted-output approach requires activity-level data, necessitates different approaches for different sectors, and results in metrics with difficult-to-interpret absolute levels.  Project-completion rates require access to project-level data and may not fully account for differences in the quality and complexity of projects. The publication includes a list of the pros, cons, and implementation requirements for each approach….(More)”.

Wikipedia Is Finally Asking Big Tech to Pay Up


Noam Cohen at Wired: “From the start, Google and Wikipedia have been in a kind of unspoken partnership: Wikipedia produces the information Google serves up in response to user queries, and Google builds up Wikipedia’s reputation as a source of trustworthy information. Of course, there have been bumps, including Google’s bold attempt to replace Wikipedia with its own version of user-generated articles, under the clumsy name “Knol,” short for knowledge. Knol never did catch on, despite Google’s offer to pay the principal author of an article a share of advertising money. But after that failure, Google embraced Wikipedia even tighter—not only linking to its articles but reprinting key excerpts on its search result pages to quickly deliver Wikipedia’s knowledge to those seeking answers.

The two have grown in tandem over the past 20 years, each becoming its own household word. But whereas one mushroomed into a trillion-dollar company, the other has remained a midsize nonprofit, depending on the generosity of individual users, grant-giving foundations, and the Silicon Valley giants themselves to stay afloat. Now Wikipedia is seeking to rebalance its relationships with Google and other big tech firms like Amazon, Facebook, and Apple, whose platforms and virtual assistants lean on Wikipedia as a cost-free virtual crib sheet.

Today, the Wikimedia Foundation, which operates the Wikipedia project in more than 300 languages as well as other wiki-projects, is announcing the launch of a commercial product, Wikimedia Enterprise. The new service is designed for the sale and efficient delivery of Wikipedia’s content directly to these online behemoths (and eventually, to smaller companies too)….(More)”.

Using Data and Citizen Science for Gardening Success


Article by Elizabeth Waddington: “…Data can help you personally by providing information you can use. And it also allows you to play a wider role in boosting understanding of our planet and tackling the global crises we face in a collaborative way. Consider the following examples.

Grow Observatory

This is one great example of data gathering and citizen science. Grow Observatory is a European citizen’s observatory through which people work together to take action on climate change, build better soil, grow healthier food and corroborate data from the new generation of Copernicus satellites.

Twenty-four Grow communities in 13 European countries created a network of over 6,500 ground-based soil sensors and collected a lot of soil-related data. And many insights have helped people learn about and test regenerative food growing techniques.

On their website, you can explore sensor locations, or make use of dynamic soil moisture maps. With the Grow Observatory app, you can get crop and planting advice tailored to your location, and get detailed, science-based information about regenerative growing practices. Their water planner also allows small-scale growers to learn more about how much water their plants will need in their location over the coming months if they live in one of the areas which currently have available data…

Cooperative Citizen Science: iNaturalist, Bioblitzes, Bird Counts, and More

Wherever you live, there are many different ways to get involved and help build data. From submitting observations on wildlife in your garden through apps like iNaturalist to taking part in local Bioblitzes, bird counts, and more – there are plenty of ways we can collect data that will help us – and others – down the road.

Collecting data through our observations, and, crucially, sharing that data with others can help us create the future we all want to see. We, as individuals, can often feel powerless. But citizen science projects help us to see the collective power we can wield when we work together. Modern technology means we can be hyper-connected, and affect wider systems, even when we are alone in our own gardens….(More)”

Lessons from all democracies


David Stasavage at Aeon: “Today, many people see democracy as under threat in a way that only a decade ago seemed unimaginable. Following the fall of the Berlin Wall in 1989, it seemed like democracy was the way of the future. But nowadays, the state of democracy looks very different; we hear about ‘backsliding’ and ‘decay’ and other descriptions of a sort of creeping authoritarianism. Some long-established democracies, such as the United States, are witnessing a violation of governmental norms once thought secure, and this has culminated in the recent insurrection at the US Capitol. If democracy is a torch that shines for a time before then burning out – think of Classical Athens and Renaissance city republics – it all feels as if we might be heading toward a new period of darkness. What can we do to reverse this apparent trend and support democracy?

First, we must dispense with the idea that democracy is like a torch that gets passed from one leading society to another. The core feature of democracy – that those who rule can do so only with the consent of the people – wasn’t invented in one place at one time: it evolved independently in a great many human societies.

Over several millennia and across multiple continents, early democracy was an institution in which rulers governed jointly with councils and assemblies of the people. From the Huron (who called themselves the Wendats) and the Iroquois (who called themselves the Haudenosaunee) in the Northeastern Woodlands of North America, to the republics of Ancient India, to examples of city governance in ancient Mesopotamia, these councils and assemblies were common. Classical Greece provided particularly important instances of this democratic practice, and it’s true that the Greeks gave us a language for thinking about democracy, including the word demokratia itself. But they didn’t invent the practice. If we want to better understand the strengths and weaknesses of our modern democracies, then early democratic societies from around the world provide important lessons.

The core feature of early democracy was that the people had power, even if multiparty elections (today, often thought to be a definitive feature of democracy) didn’t happen. The people, or at least some significant fraction of them, exercised this power in many different ways. In some cases, a ruler was chosen by a council or assembly, and was limited to being first among equals. In other instances, a ruler inherited their position, but faced constraints to seek consent from the people before taking actions both large and small. The alternative to early democracy was autocracy, a system where one person ruled on their own via bureaucratic subordinates whom they had recruited and remunerated. The word ‘autocracy’ is a bit of a misnomer here in that no one in this position ever truly ruled on their own, but it does signify a different way of organising political power.

Early democratic governance is clearly apparent in some ancient societies in Mesopotamia as well as in India. It flourished in a number of places in the Americas before European conquest, such as among the Huron and the Iroquois in the Northeastern Woodlands and in the ‘Republic of Tlaxcala’ that abutted the Triple Alliance, more commonly known as the Aztec Empire. It was also common in precolonial Africa. In all of these societies there were several defining features that tended to reinforce early democracy: small scale, a need for rulers to depend on the people for knowledge, and finally the ability of members of society to exit to other locales if they were unhappy with a ruler. These three features were not always present in the same measure, but collectively they helped to underpin early democracy….(More)”

The Techlash and Tech Crisis Communication


Book by Nirit Weiss-Blatt: “This book provides an in-depth analysis of the evolution of tech journalism. The emerging tech-backlash is a story of pendulum swings: We are currently in tech-dystopianism after a long period spent in tech-utopianism. Tech companies were used to ‘cheerleading’ coverage of product launches. This long tech-press honeymoon ended, and was replaced by a new era of mounting criticism focused on tech’s negative impact on society. When and why did tech coverage shift? How did tech companies respond to the rise of tech criticism?

The book depicts three main eras: Pre-Techlash, Techlash, and Post-Techlash. The reader is taken on a journey from computer magazines, through tech blogs to the upsurge of tech investigative reporting. It illuminates the profound changes in the power dynamics between the media and the tech giants it covers.

The interplay between tech journalism and tech PR was underexplored. Through analyses of both tech media and the corporates’ crisis responses, this book examines the roots and characteristics of the Techlash, and provides explanations to ‘How did we get here?’. Insightful observations by tech journalists and tech public relations professionals are added to the research data, and together – they tell the story of the TECHLASH. It includes theoretical and practical implications for both tech enthusiasts and critics….(More)”.