Report by the European Parliament’s Think Tank: “The EU’s approach to digital transformation is rooted in protecting fundamental rights, sustainability, ethics and fairness. With this human-centric vision of the digital economy and society, the EU seeks to empower citizens and businesses, regardless of their size. In the EU’s view, the internet should remain open, fair, inclusive and focused on people. Digital technologies should work for citizens and help them to engage in society. Companies should be able to compete on equal terms, and consumers should be confident that their rights are respected. The European Commission has published a number of strategies and action plans recently that outline the EU’s vision for the digital future and set concrete targets for achieving it. The Commission has also proposed several digital regulations, including the artificial intelligence act, the Digital Services Act and the Digital Markets Act. These regulations are intended to ensure a safe online environment and fair and open digital markets, strengthen Europe’s competitiveness, improve algorithmic transparency and give citizens better control over how they share their personal data. Although some of these regulations have not yet been adopted, and others have been in force for only a short time, they are expected to have impact not only in the EU but also beyond its borders. For instance, several regulations target businesses – regardless of where they are based – that offer services to EU citizens or businesses. In addition, through the phenomenon known as ‘the Brussels effect’, these rules may influence tech business practices and national legislation around the world. The EU is an active participant in developing global digital cooperation and global governance frameworks for specific areas. Various international organisations are developing instruments to ensure that people and businesses can take advantage of artificial intelligence’s benefits and limit negative consequences. In these global negotiations, the EU promotes respect for various fundamental rights and freedoms, as well as compatibility with EU law….(More)”.
How Much of the World Is It Possible to Model?
Article by Dan Rockmore: “…Modelling, in general, is now routine. We model everything, from elections to economics, from the climate to the coronavirus. Like model cars, model airplanes, and model trains, mathematical models aren’t the real thing—they’re simplified representations that get the salient parts right. Like fashion models, model citizens, and model children, they’re also idealized versions of reality. But idealization and abstraction can be forms of strength. In an old mathematical-modelling joke, a group of experts is hired to improve milk production on a dairy farm. One of them, a physicist, suggests, “Consider a spherical cow.” Cows aren’t spheres any more than brains are jiggly sponges, but the point of modelling—in some ways, the joy of it—is to see how far you can get by using only general scientific principles, translated into mathematics, to describe messy reality.
To be successful, a model needs to replicate the known while generalizing into the unknown. This means that, as more becomes known, a model has to be improved to stay relevant. Sometimes new developments in math or computing enable progress. In other cases, modellers have to look at reality in a fresh way. For centuries, a predilection for perfect circles, mixed with a bit of religious dogma, produced models that described the motion of the sun, moon, and planets in an Earth-centered universe; these models worked, to some degree, but never perfectly. Eventually, more data, combined with more expansive thinking, ushered in a better model—a heliocentric solar system based on elliptical orbits. This model, in turn, helped kick-start the development of calculus, reveal the law of gravitational attraction, and fill out our map of the solar system. New knowledge pushes models forward, and better models help us learn.
Predictions about the universe are scientifically interesting. But it’s when models make predictions about worldly matters that people really pay attention.We anxiously await the outputs of models run by the Weather Channel, the Fed, and fivethirtyeight.com. Models of the stock market guide how our pension funds are invested; models of consumer demand drive production schedules; models of energy use determine when power is generated and where it flows. Insurers model our fates and charge us commensurately. Advertisers (and propagandists) rely on A.I. models that deliver targeted information (or disinformation) based on predictions of our reactions.
But it’s easy to get carried away..(More)”
Missing Evidence : Tracking Academic Data Use around the World
Worldbank Report: “Data-driven research on a country is key to producing evidence-based public policies. Yet little is known about where data-driven research is lacking and how it could be expanded. This paper proposes a method for tracking academic data use by country of subject, applying natural language processing to open-access research papers. The model’s predictions produce country estimates of the number of articles using data that are highly correlated with a human-coded approach, with a correlation of 0.99. Analyzing more than 1 million academic articles, the paper finds that the number of articles on a country is strongly correlated with its gross domestic product per capita, population, and the quality of its national statistical system. The paper identifies data sources that are strongly associated with data-driven research and finds that availability of subnational data appears to be particularly important. Finally, the paper classifies countries into groups based on whether they could most benefit from increasing their supply of or demand for data. The findings show that the former applies to many low- and lower-middle-income countries, while the latter applies to many upper-middle- and high-income countries…(More)”.
Are we entering a “Data Winter”?
Article by Stefaan G. Verhulst: “In an era where data drives decision-making, the accessibility of data for public interest purposes has never been more crucial. Whether shaping public policy, responding to disasters, or empowering research, data plays a pivotal role in our understanding of complex social, environmental, and economic issues. In 2015, I introduced the concept of Data Collaboratives to advance new and innovative partnerships between the public and private sectors that could make data more accessible for public interest purposes. More recently, I have been advocating for a reimagined approach to data stewardship to make data collaboration more systematic, agile, sustainable, and responsible.
Despite many advances toward data stewardship (especially during Covid19) and despite the creation of several important data collaboratives (e.g., the Industry Data for Society Partnership) the project of opening access to data is proving increasingly challenging. Indeed, unless we step up our efforts in 2024, we may be entering a prolonged data winter — analogous to previous Artificial Intelligence winters, marked by reduced funding and interest in AI research, in which data assets that could be leveraged for the common good are instead frozen and immobilized. Recent developments, such as a decline in access to social media data for research and the growing privatization of climate data, along with a decrease in open data policy activity, signify a worrying trend. This blog takes stock of these developments and, building on some recent expert commentary, raises a number of concerns about the current state of data accessibility and its implications for the public interest. We conclude by calling for a new Decade of Data — one marked by a reinvigorated commitment to open data and data reuse for the public interest…(More)”.
The world needs an International Decade for Data–or risk splintering into AI ‘haves’ and ‘have-nots,’ UN researchers warn
Article by Tshilidzi Marwala and David Passarelli: “The rapid rise in data-driven technologies is shaping how many of us live–from biometric data collected by our smartwatches, artificial intelligence (AI) tools and models changing how we work, to social media algorithms that seem to know more about our content preferences than we do. Greater amounts of data are affecting all aspects of our lives, and indeed, society at large.
This explosion in data risks creating new inequalities, equipping a new set of “haves” who benefit from the power of data while excluding, or even harming, a set of “have-nots”–and splitting the international community into “data-poor” and “data-rich” worlds.
We know that data, when harnessed correctly, can be a powerful tool for sustainable development. Intelligent and innovative use of data can support public health systems, improve our understanding of climate change and biodiversity loss, anticipate crises, and tackle deep-rooted structural injustices such as racism and economic inequality.
However, the vast quantity of data is fueling an unregulated Wild West. Instead of simply issuing more warnings, governments must instead work toward good governance of data on a global scale. Due to the rapid pace of technological innovation, policies intended to protect society will inevitably fall behind. We need to be more ambitious.
To begin with, governments must ensure that the benefits derived from data are equitably distributed by establishing global ground rules for data collection, sharing, taxation, and re-use. This includes dealing with synthetic data and cross-border data flows…(More)”.
The New Knowledge
Book by Blayne Haggart and Natasha Tusikov: “From the global geopolitical arena to the smart city, control over knowledge—particularly over data and intellectual property—has become a key battleground for the exercise of economic and political power. For companies and governments alike, control over knowledge—what scholar Susan Strange calls the knowledge structure—has become a goal unto itself.
The rising dominance of the knowledge structure is leading to a massive redistribution of power, including from individuals to companies and states. Strong intellectual property rights have concentrated economic benefits in a smaller number of hands, while the “internet of things” is reshaping basic notions of property, ownership, and control. In the scramble to create and control data and intellectual property, governments and companies alike are engaging in ever-more surveillance.
The New Knowledge is a guide to and analysis of these changes, and of the emerging phenomenon of the knowledge-driven society. It highlights how the pursuit of the control over knowledge has become its own ideology, with its own set of experts drawn from those with the ability to collect and manipulate digital data. Haggart and Tusikov propose a workable path forward—knowledge decommodification—to ensure that our new knowledge is not treated simply as a commodity to be bought and sold, but as a way to meet the needs of the individuals and communities that create this knowledge in the first place…(More)”.
Climate change may kill data sovereignty
Article by Trisha Ray: “Data centres are the linchpin of a nation’s technological progress, serving as the nerve centers that power the information age. The need for robust and reliable data centre infrastructure cuts across the UN Sustainable Development Goals (SDGs), serving as an essential foundation for e-government, innovation and entrepreneurship, decent work, and economic growth. It comes as no surprise then that data sovereignty has gained traction over the past decade, particularly in the Global South. However, climate change threatens the very infrastructure that underpins the digital future, and its impact on data centres is a multifaceted challenge, with rising temperatures, extreme weather events, and changing environmental conditions posing significant threats to their reliability and sustainability, even as developing countries begin rolling out ambitious strategies and incentives to attract data centres…(More)”.
Data Science for Social Impact in Higher Education: First Steps
Data.org playbook: “… was designed to help you expand opportunities for social impact data science learning. As you browse, you will see a range of these opportunities including courses, modules for other courses, research and internship opportunities, and a variety of events and activities. The playbook also offers lessons learned to guide you through your process. Additionally, the Playbook includes profiles of students who have engaged in data science for social impact, guidance for engaging partners, and additional resources relating to evaluation and courses. We hope that this playbook will inspire and support your efforts to bring social impact data science to your institutions…
As you look at the range of ways you might bring data science for social impact to your students, remember that the intention is not for you to replicate what is here, but rather adapt them to your local contexts and conditions. You might draw pieces from several activities and combine them to create a customized strategy that works for you. Consider the assets you have around you and how you might be able to leverage them. At the same time, imagine how some of the lessons learned might reflect barriers you might face, as well. Most importantly, know that it is possible for you to create data science for social impact at your institution to bring benefit to your students and society…(More)”.
Medical AI could be ‘dangerous’ for poorer nations, WHO warns
Article by David Adam: “The introduction of health-care technologies based on artificial intelligence (AI) could be “dangerous” for people in lower-income countries, the World Health Organization (WHO) has warned.
The organization, which today issued a report describing new guidelines on large multi-modal models (LMMs), says it is essential that uses of the developing technology are not shaped only by technology companies and those in wealthy countries. If models aren’t trained on data from people in under-resourced places, those populations might be poorly served by the algorithms, the agency says.
“The very last thing that we want to see happen as part of this leap forward with technology is the propagation or amplification of inequities and biases in the social fabric of countries around the world,” Alain Labrique, the WHO’s director for digital health and innovation, said at a media briefing today.
The WHO issued its first guidelines on AI in health care in 2021. But the organization was prompted to update them less than three years later by the rise in the power and availability of LMMs. Also called generative AI, these models, including the one that powers the popular ChatGPT chatbot, process and produce text, videos and images…(More)”.
Facial Recognition: Current Capabilities, Future Prospects, and Governance
A National Academies of Sciences, Engineering, and Medicine study: “Facial recognition technology is increasingly used for identity verification and identification, from aiding law enforcement investigations to identifying potential security threats at large venues. However, advances in this technology have outpaced laws and regulations, raising significant concerns related to equity, privacy, and civil liberties.
This report explores the current capabilities, future possibilities, and necessary governance for facial recognition technology. Facial Recognition Technology discusses legal, societal, and ethical implications of the technology, and recommends ways that federal agencies and others developing and deploying the technology can mitigate potential harms and enact more comprehensive safeguards…(More)”.