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Welcome to a New Era

In only a few short years since the emergence of the World Wide Web, society has transitioned into a revolutionary new era — that of the “data economy.” Such a large-scale transformation has happened seemingly overnight. In a 2017 EU report, it was estimated that by 2020 the value of the EU’s ‘data economy’ alone would approach EUR 643 billion (approx. 3.17% of total EU GDP), up from an estimated EUR 257 billion in just 2014.

The consequences of increased datafication of our world have altered society in dramatic ways, both productive and divisive. The products of the data economy have increased the personalization of healthcare; opened up universal, remote learning opportunities; offered us glimpses into natural environments previously off-limits to humans; and broadened our understanding of the world we’re living in through the progressively advanced small computers we call smart phones that many of us now carry every day, to name just a few examples. Yet the same tools that have led to these advances have also allowed engineers to design deliberately addictive social media platforms; led to the increased quantification of nearly every aspect of our waking and sleeping daily lives; crafted a bombardment of hyper-personalized advertisements extending beyond the confines of the internet; and even developed extremely skilled remote and depersonalized weapons systems.

These developments are read by stakeholders in fundamentally different ways. Some, like increasingly personalized healthcare, are perceived to be moving human society towards a utopia like that found in the Star Trek universe. Others consider these same steps to be a threat to humanity, spiraling us into a technological dystopia not unlike the 1997 film Gattaca. When you’re amid such monumental changes, it is often hard to know which is which. And yet, this confusion perfectly encompasses two core aspects of the data economy: It has already had significant effects on the world, and it has created more questions than answers. …


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Advances in modern transportation technology rely heavily on collected data, whether for simply notifying users of free parking spaces or for more complex applications like calculating rideshare apps’ surge-pricing. In this week’s essay, GDI Research Analyst and engineering masters student Shahvez Ul Haq takes us through several examples of the oft-overlooked technical side behind how different types of data are collected for use in optimizing travel and reducing urban traffic congestion.

Mapping the City with Smartphones

The first common method of collecting transportation data is right in your hand. Small and mighty, smartphones enable their users to take an active role in planning routes using common mapping applications including Google Maps, Apple Maps, or smaller privately developed public transport apps (including Citymapper, Transit, or Moovit). These applications prompt users to agree to terms and conditions whereby location services within each smartphone track the user’s location and use this resulting data to both advise users on their best route, as well as provide additional anonymized and aggregated data when analyzing on-the-ground conditions. When combined with existing map data (see Wired’s 2014 article on the Google Maps ‘Ground Truth’ effort for a look behind the scenes), these live data points create new variables as each app’s algorithm seeks to find the shortest path between two points. …


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Welcome to 2021! Over the holidays, our team at GDI has been reflecting on the increasing impact of algorithms on everyday decision processes, especially given current world events. Most media coverage discussing algorithmic biases has focused on social and cultural factors — yet technical biases also play a critical, yet largely invisible role. In this third GDI Deep Read, Research Analyst Anna Sappington reflects on her own experiences with machine learning to explore the nuanced concept of ‘algorithmic fairness’ as a way to prevent both socially and technically embedded bias within real-world tools.

In 2019, self-proclaimed “techno-sociologist” Zeynep Tufekci stood on stage at one of the largest machine learning conferences, sponsored by the likes of Facebook, Google, and Amazon, and in front of hundreds of tech employees…


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The dust has barely had time to settle over the last five months since the Court of Justice of the European Union (CJEU) issued arguably the most important judgement on data privacy this year: That of Case-C-311/18 (Schrems II), concerning the EU-US Privacy Shield. In short, the US was found to provide insufficient data subject rights relative to the EU — essentially meaning that there will no longer be a free flow of data from Europe to the US.

Despite the vast scope of its impact, many open questions remain. On the 11th and 12th of November 2020 the European Data Protection Board (EDBP) and the European Commission (EC) finally provided some guidance on the implementation of this monumental judgement. No grace period has been offered to the 5,300 companies affected by this ruling, 70 percent of which are small- and medium-sized enterprises (SMEs). The Schrems II judgement has the potential to transform almost everything about how EU companies conduct international data transfers. …


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GDI Shorts are an ongoing series where we explore interesting ideas affecting the data economy through three simple questions: What is the idea? Where is it located? And why should I care?

What are maker spaces?

“Maker spaces” (sometimes called “hackerspaces”) are collaborative working environments equipped with a variety of tools — ranging from the highly technical (e.g., editing software and 3D-printers) to basic instruments (e.g., hammers and table saws) — that enable their users to exchange ideas and use these shared tools to ‘make’ new creations.

These spaces are also sometimes referred to by specific names, depending on the specialty of each space: Physical spaces include “fab labs” (i.e., small-scale personal digital fabrication laboratories), “sewing cafés” (i.e., for micro-production working with textiles), “repair cafés” (i.e., for repairing existing commercial products), and “DIYBio labs” (i.e., for democratizing access to research in biotechnology). Digital spaces often include catalogues of creations that are part of the Open Design movement, such as the Open Source software blueprints for Covid-19 face shields that were shared during early 2020. …


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The push for cashless societies is real. The reasons to embrace digital payments are widely advertised, but the advantages of cash are rarely appreciated. Cash empowers people with freedom, privacy, and control over their spending. As GDI Research Analyst Meghan Keenan argues in this first GDI Deep Read, at the end of the day, the elimination of cash is not in the interest of the general public.

In 2019, global non-cash transactions rose by almost 14% — the highest growth rate since 2010. Banks are closing down branches and ATM machines in favour of digital payments and online banking infrastructure. Instead of cash, people are using smart phones, credit cards, debit cards, and more. Many countries, such as Sweden and India, are explicitly endeavouring towards a cashless future. The COVID-19 pandemic has accelerated the rise of digital payments, as many stores no longer accept cash for hygiene-related reasons, and consumers are increasingly shopping online. …


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Following the revolutions in military affairs brought about by gunpowder and nuclear weapons, we find ourselves once again at the dawn of a new era of warfare: The Age of Autonomous Systems. Using cutting-edge technologies for military purposes, especially from the field of Artificial Intelligence, will radically transform how wars will be fought in the near future.

LAWS (Lethal Autonomous Weapon Systems) is a critical acronym to understand warfare in the 21st century. LAWS encompass any weapon system with autonomy in its critical functions, namely one which can select (i.e., search for or detect, identify, track, and select) and attack (i.e., …


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Data — a resource some consider the “new oil” — has played a central role in our daily lives for millennia. Ancient states used censuses to inform tax levying; colonial banks used local land registries to inform loan issuance. While the scale of human activities has changed over time leading to more data, arguably the most drastic change has been in the ways we capture and store this information about our activities and the environment.

Recent technological advances — especially widespread digitalisation — have radically reduced the cost of collecting and storing the large volumes of data that result from our economic and social activities.


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Listening to the Not-So-Silent Waters

In the dark, cold, not-so-silent waters of the Canadian Atlantic, twenty-odd hydrophones — microphones that detect sound waves underwater — anchored to the ocean floor are continuously recording.

In my work as a marine biologist, I listened to hours of these ocean soundscapes: I learned to recognize the staccato of rain drops; the high-pitched frenzied whistles of dolphins; the loud and regular clicking of a sperm whale. The near-constant background thrum of vessel noise would crescendo into a roar with a passing boat, dominating the soundscape for several minutes until the vessel moved off.

While my research focused on identifying and monitoring cetaceans by their vocalizations or echolocation clicks, these acoustic data also contain a plethora of other information about the ocean and the creatures in it. Increasingly complex oceanic technologies and acoustic analyses are allowing us to ‘see’ this information we would not otherwise have access to. …


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Photo: Coffee bean farmer in Tanzania

Can Artificial Intelligence be used for successful drought prediction in Sub-Saharan Africa?

Droughts — what we describe as regional periods of water deficits in some stage of the water cycle — are natural disasters that have become increasingly common over the last 30 years as a direct result of anthropogenic climate change [1]. Yet, the impacts of droughts are not evenly spread across the globe. With over 50% percent of world-wide drought occurrences between 2001 and 2011 taking place in Africa [2], and droughts leading to more deaths worldwide than any other physical hazard [3], mounting evidence suggests that they will likely have disproportionate impacts on the African population and economies.

For the estimated 33 million smallholder farmers in Sub-Saharan Africa[4], this is a prime example of how many of the most vulnerable people worldwide are experiencing the heaviest effects of climate change — right now. Advances in applying artificial intelligence (AI) to help improve the accuracy and accessibility of drought predictions may be able to change the depth of these impacts, as ongoing innovations are suggesting in Sub-Saharan Africa. …

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Good Data Initiative

Think tank led by students from the Univ. of Cambridge. Building the leading platform for intergenerational and interdisciplinary debate on the #dataeconomy

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