Emerging approaches for data-driven innovation in Europe


Report by Granell, C. et al: “Europe’s digital transformation of the economy and society is framed by the European strategy for data through the establishment of a common European data space based on domain-specific data spaces in strategic sectors such as environment, agriculture, industry, health and transportation. Acknowledging the key role that emerging technologies and innovative approaches for data sharing and use can play to make European data spaces a reality, this document presents a set of experiments that explore emerging technologies and tools for data-driven innovation, and also deepen in the socio-technical factors and forces that occur in data-driven innovation. Experimental results shed some light in terms of lessons learned and practical recommendations towards the establishment of European data spaces…(More)”.

Building machines that work for everyone – how diversity of test subjects is a technology blind spot, and what to do about it


Article by Tahira Reid and James Gibert: “People interact with machines in countless ways every day. In some cases, they actively control a device, like driving a car or using an app on a smartphone. Sometimes people passively interact with a device, like being imaged by an MRI machine. And sometimes they interact with machines without consent or even knowing about the interaction, like being scanned by a law enforcement facial recognition system.

Human-Machine Interaction (HMI) is an umbrella term that describes the ways people interact with machines. HMI is a key aspect of researching, designing and building new technologies, and also studying how people use and are affected by technologies.

Researchers, especially those traditionally trained in engineering, are increasingly taking a human-centered approach when developing systems and devices. This means striving to make technology that works as expected for the people who will use it by taking into account what’s known about the people and by testing the technology with them. But even as engineering researchers increasingly prioritize these considerations, some in the field have a blind spot: diversity.

As an interdisciplinary researcher who thinks holistically about engineering and design and an expert in dynamics and smart materials with interests in policy, we have examined the lack of inclusion in technology design, the negative consequences and possible solutions….

It is possible to use a homogenous sample of people in publishing a research paper that adds to a field’s body of knowledge. And some researchers who conduct studies this way acknowledge the limitations of homogenous study populations. However, when it comes to developing systems that rely on algorithms, such oversights can cause real-world problems. Algorithms are as only as good as the data that is used to build them.

Algorithms are often based on mathematical models that capture patterns and then inform a computer about those patterns to perform a given task. Imagine an algorithm designed to detect when colors appear on a clear surface. If the set of images used to train that algorithm consists of mostly shades of red, the algorithm might not detect when a shade of blue or yellow is present…(More)”.

Data Sharing in Transport


Technical Note by the European Investment Board: “Traveller and transport related data are essential for planning efficient urban mobility and delivering an effective public transport services while adequately managing infrastructure investment costs. It also supports local authorities in their efforts towards decarbonisation of transport as well as improving air quality.

Nowadays, most of the data are generated by location-based mobile phone applications and connected vehicles or other mobility equipment like scooters and bikes. This opens up new opportunities in public sector engagement with private sector and partnerships.

This report, through an extensive literature review and interviews, identifies seven Data Partnership Models that could be used by public and private sector entities in the field of transport. It also provides a concise roadmap for local authorities as a guidance in their efforts when engaging with private sector in transport data sharing…(More)”.

Counting Crimes: An Obsolete Paradigm


Paul Wormeli at The Criminologist: “To the extent that a paradigm is defined as the way we view things, the crime statistics paradigm in the United States is inadequate and requires reinvention….The statement—”not all crime is reported to the police”—lies at the very heart of why our current crime data are inherently incomplete. It is a direct reference to the fact that not all “street crime” is reported and that state and local law enforcement are not the only entities responsible for overseeing violations of societally established norms (“street crime” or otherwise). Two significant gaps exist, in that: 1) official reporting of crime from state and local law enforcement agencies cannot provide insight into unreported incidents, and 2) state and local law enforcement may not have or acknowledge jurisdiction over certain types of matters, such as cybercrime, corruption, environmental crime, or terrorism, and therefore cannot or do not report on those incidents…

All of these gaps in crime reporting mask the portrait of crime in the U.S. If there was a complete accounting of crime that could serve as the basis of policy formulation, including the distribution of federal funds to state and local agencies, there could be a substantial impact across the nation. Such a calculation would move the country toward a more rational basis for determining federal support for communities based on a comprehensive measure of community wellness.

In its deliberations, the NAS Panel recognized that it is essential to consider both the concepts of classification and the rules of counting as we seek a better and more practical path to describing crime in the U.S. and its consequences. The panel postulated that a meaningful classification of incidents found to be crimes would go beyond the traditional emphasis on street crime and include all crime categories.

The NAS study identified the missing elements of a national crime report as including more complete data on crimes involving drugrelated offenses, criminal acts where juveniles are involved, so-called white-collar crimes such as fraud and corruption, cybercrime, crime against businesses, environmental crimes, and crimes against animals. Just as one example, it is highly unlikely that we will know the full extent of fraudulent claims against all federal, state, and local governments in the face of the massive influx of funding from recent and forthcoming Congressional action.

In proposing a set of crime classifications, the NAS panel recommended 11 major categories, 5 of which are not addressed in our current crime data collection systems. While there are parallel data systems that collect some of the missing data within these five crime categories, it remains unclear which federal agency, if any, has the authority to gather the information and aggregate it to give us anywhere near a complete estimate of crime in the United States. No federal or national entity has the assignment of estimating the total amount of crime that takes place in the United States. Without such leadership, we are left with an uninformed understanding of the health and wellness of communities throughout the country…(More)”

Breakthrough: The Promise of Frontier Technologies for Sustainable Development


Book edited by Homi Kharas, John McArthur, and Izumi Ohno: “Looking into the future is always difficult and often problematic—but sometimes it’s useful to imagine what innovations might resolve today’s problems and make tomorrow better. In this book, 15 distinguished international experts examine how technology will affect the human condition and natural world within the next ten years. Their stories reflect major ambitions for what the future could bring and offer a glimpse into the possibilities for achieving the UN’s ambitious Sustainable Development Goals.

The authors were asked to envision future success in their respective fields, given the current state of technology and potential progress over the next decade. The central question driving their research: What are likely technological advances that could contribute  to the Sustainable Development Goals at major scale, affecting the lives of hundreds of millions of people or substantial geographies around the globe.

One overall takeaway is that gradualist approaches will not achieve those goals by 2030. Breakthroughs will be necessary in science, in the development of new products and services, and in institutional systems. Each of the experts responded with stories that reflect big ambitions for what the future may bring. Their stories are not projections or forecasts as to what will happen; they are reasoned and reasonable conjectures about what could happen. The editors’ intent is to provide a glimpse into the possibilities for the future of sustainable development.

At a time when many people worry about stalled progress on the economic, social, and environmental challenges of sustainable development, Breakthrough is a reminder that the promise of a better future is within our grasp, across a range of domains. It will interest anyone who wonders about the world’s economic, social, and environmental future…(More)”

Enlightenment’s dimming light


Anthony Painter at the RSA: “…The project of the Enlightenment is dimming and more of the same values and the political economy and society they surface cannot enable us to resolve the global problems we face. One America is already too much and with China heading that way in consumption and environmental degradation terms the global impacts will be devastating. Something must evolve and fast if we are not to crash into these limits that have become apparent. COP26 was a step; many, many more are required. First there was the unravelling, but unless we face it then there will be reckoning – for many, though innocent, there already is.

There is a volume of documentary evidence behind the nature of these multiple crises. Whilst we should constantly remind ourselves of the depth of the challenge, and it is at scale, there are two urgent questions that are needed if we are to find a way through. In the words of Arundhati Roy, who do we want to be at the other side – through the portal? How do we travel with that sense of purpose and deep values as we confront the future? Survival requires us as societies to rapidly learn together and evolve.

To make the transition relies on developing three inter-connected and mutually reinforcing values: home, community and democracy. Through these we will develop a sense of the ‘lifeworld’ we wish to safeguard. The German philosopher, Jurgen Habermas, sees the lifeworld as a space of human interaction and civic community and see its interface with big systems of money and power – human creations but distinct forces from the ‘lifeworld’ – as the critical site of human progress and well-being. Creativity happens at the frontier between the lifeworld and big systems.

What is meant by ‘home’? Some elements of home are in proximity. They are our close relations, those we care for directly and receive care from, as deep commitment rather than reciprocated self-interest. Home is a state of what Michael Tomasello has termed, collective intentionality. Any account of the future will need to have a convincing account of close relations. Increasingly these relationships are mediated by technology and we need to develop a more conscious account of how technology can and should act as a bond rather than a thinner of human relations.

There are seemingly more distant aspects of ‘home’ too – most particularly the natural environmental into which we are woven. And there we have been committing acts of domestic harm: polluting the atmosphere, depleting the stock of species, and poisoning the water and the ground with toxic waste. This two century long destructive streak is now visible and realised. There is a common understanding that change must come: but how and how rapidly? How can we develop an even greater collective sense of the need for rapid and radical change? And how can we begin to evolve systems of money, power and technology to respond to this new ‘common sense’? How can our future be one that regenerates nature as well as ourselves?…(More)”

New and updated building footprints


Bing Blogs: “…The Microsoft Maps Team has been leveraging that investment to identify map features at scale and produce high-quality building footprint data sets with the overall goal to add to the OpenStreetMap and MissingMaps humanitarian efforts.

As of this post, the following locations are available and Microsoft offers access to this data under the Open Data Commons Open Database License (ODbL).

Country/RegionMillion buildings
United States of America129.6
Nigeria and Kenya50.5
South America44.5
Uganda and Tanzania17.9
Canada11.8
Australia11.3

As you might expect, the vintage of the footprints depends on the collection date of the underlying imagery. Bing Maps Imagery is a composite of multiple sources with different capture dates (ranging 2012 to 2021). To ensure we are setting the right expectation for that building, each footprint has a capture date tag associated if we could deduce the vintage of imagery used…(More)”

Octagon Measurement: Public Attitudes toward AI Ethics


Paper by Yuko Ikkatai, Tilman Hartwig, Naohiro Takanashi & Hiromi M. Yokoyama: “Artificial intelligence (AI) is rapidly permeating our lives, but public attitudes toward AI ethics have only partially been investigated quantitatively. In this study, we focused on eight themes commonly shared in AI guidelines: “privacy,” “accountability,” “safety and security,” “transparency and explainability,” “fairness and non-discrimination,” “human control of technology,” “professional responsibility,” and “promotion of human values.” We investigated public attitudes toward AI ethics using four scenarios in Japan. Through an online questionnaire, we found that public disagreement/agreement with using AI varied depending on the scenario. For instance, anxiety over AI ethics was high for the scenario where AI was used with weaponry. Age was significantly related to the themes across the scenarios, but gender and understanding of AI differently related depending on the themes and scenarios. While the eight themes need to be carefully explained to the participants, our Octagon measurement may be useful for understanding how people feel about the risks of the technologies, especially AI, that are rapidly permeating society and what the problems might be…(More)”.

Data Re-Use and Collaboration for Development


Stefaan G. Verhulst at Data & Policy: “It is often pointed out that we live in an era of unprecedented data, and that data holds great promise for development. Yet equally often overlooked is the fact that, as in so many domains, there exist tremendous inequalities and asymmetries in where this data is generated, and how it is accessed. The gap that separates high-income from low-income countries is among the most important (or at least most persistent) of these asymmetries…

Data collaboratives are an emerging form of public-private partnership that, when designed responsibly, can offer a potentially innovative solution to this problem. Data collaboratives offer at least three key benefits for developing countries:

1. Cost Efficiencies: Data and data analytic capacity are often hugely expensive and beyond the limited capacities of many low-income countries. Data reuse, facilitated by data collaboratives, can bring down the cost of data initiatives for development projects.

2. Fresh insights for better policy: Combining data from various sources by breaking down silos has the potential to lead to new and innovative insights that can help policy makers make better decisions. Digital data can also be triangulated with existing, more traditional sources of information (e.g., census data) to generate new insights and help verify the accuracy of information.

3. Overcoming inequalities and asymmetries: Social and economic inequalities, both within and among countries, are often mapped onto data inequalities. Data collaboratives can help ease some of these inequalities and asymmetries, for example by allowing costs and analytical tools and techniques to be pooled. Cloud computing, which allows information and technical tools to be easily shared and accessed, are an important example. They can play a vital role in enabling the transfer of skills and technologies between low-income and high-income countries…(More)”. See also: Reusing data responsibly to achieve development goals (OECD Report).

How digital transformation is driving economic change


Blog (and book) by Zia Qureshi: “We are living in a time of exciting technological innovations. Digital technologies are driving transformative change. Economic paradigms are shifting. The new technologies are reshaping product and factor markets and profoundly altering business and work. The latest advances in artificial intelligence and related innovations are expanding the frontiers of the digital revolution. Digital transformation is accelerating in the wake of the COVID-19 pandemic. The future is arriving faster than expected.

A recently published book, “Shifting Paradigms: Growth, Finance, Jobs, and Inequality in the Digital Economy,” examines the implications of the unfolding digital metamorphosis for economies and public policy agendas….

Firms at the technological frontier have broken away from the rest, acquiring dominance in increasingly concentrated markets and capturing the lion’s share of the returns from the new technologies. While productivity growth in these firms has been strong, it has stagnated or slowed in other firms, depressing aggregate productivity growth. Increasing automation of low- to middle-skill tasks has shifted labor demand toward higher-level skills, hurting wages and jobs at the lower end of the skill spectrum. With the new technologies favoring capital, winner-take-all business outcomes, and higher-level skills, the distribution of both capital and labor income has tended to become more unequal, and income has been shifting from labor to capital.

One important reason for these outcomes is that policies and institutions have been slow to adjust to the unfolding transformations. To realize the promise of today’s smart machines, policies need to be smarter too. They must be more responsive to change to fully capture potential gains in productivity and economic growth and address rising inequality as technological disruptions create winners and losers.

As technology reshapes markets and alters growth and distributional dynamics, policies must ensure that markets remain inclusive and support wide access to the new opportunities for firms and workers. The digital economy must be broadened to disseminate new technologies and opportunities to smaller firms and wider segments of the labor force…(More)”.