Paper by David Rolnick et al: “Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change….(More)”.
The Data Protection Officer Handbook
Handbook by Douwe Korff and Marie Georges: “This Handbook was prepared for and is used in the EU-funded “T4DATA” training‐of-trainers programme. Part I explains the history and development of European data protection law and provides an overview of European data protection instruments including the Council of Europe Convention and its “Modernisation” and the various EU data protection instruments relating to Justice and Home Affairs, the CFSP and the EU institutions, before focusing on the GDPR in Part II. The final part (Part III) consists of detailed practical advice on the various tasks of the Data Protection Officer now institutionalised by the GDPR. Although produced for the T4DATA programme that focusses on DPOs in the public sector, it is hoped that the Handbook will be useful also to anyone else interested in the application of the GDPR, including DPOs in the private sector….(More)”.
Guidance Note: Statistical Disclosure Control
Centre for Humanitarian Data: “Survey and needs assessment data, or what is known as ‘microdata’, is essential for providing adequate response to crisis-affected people. However, collecting this information does present risks. Even as great effort is taken to remove unique identifiers such as names and phone numbers from microdata so no individual persons or communities are exposed, combining key variables such as location or ethnicity can still allow for re-identification of individual respondents. Statistical Disclosure Control (SDC) is one method for reducing this risk.
The Centre has developed a Guidance Note on Statistical Disclosure Control that outlines the steps involved in the SDC process, potential applications for its use, case studies and key actions for humanitarian data practitioners to take when managing sensitive microdata. Along with an overview of what SDC is and what tools are available, the Guidance Note outlines how the Centre is using this process to mitigate risk for datasets shared on HDX. …(More)”.
Bringing machine learning to the masses
Matthew Hutson at Science: “Artificial intelligence (AI) used to be the specialized domain of data scientists and computer programmers. But companies such as Wolfram Research, which makes Mathematica, are trying to democratize the field, so scientists without AI skills can harness the technology for recognizing patterns in big data. In some cases, they don’t need to code at all. Insights are just a drag-and-drop away. One of the latest systems is software called Ludwig, first made open-source by Uber in February and updated last week. Uber used Ludwig for projects such as predicting food delivery times before releasing it publicly. At least a dozen startups are using it, plus big companies such as Apple, IBM, and Nvidia. And scientists: Tobias Boothe, a biologist at the Max Planck Institute of Molecular Cell Biology and Genetics in Dresden, Germany, uses it to visually distinguish thousands of species of flatworms, a difficult task even for experts. To train Ludwig, he just uploads images and labels….(More)”.
What can the labor flow of 500 million people on LinkedIn tell us about the structure of the global economy?
Paper by Jaehyuk Park et al: “…One of the most popular concepts for policy makers and business economists to understand the structure of the global economy is “cluster”, the geographical agglomeration of interconnected firms such as Silicon Valley, Wall Street, and Hollywood. By studying those well-known clusters, we become to understand the advantage of participating in a geo-industrial cluster for firms and how it is related to the economic growth of a region.
However, the existing definition of geo-industrial cluster is not systematic enough to reveal the whole picture of the global economy. Often, after defining as a group of firms in a certain area, the geo-industrial clusters are considered as independent to each other. As we should consider the interaction between accounting team and marketing team to understand the organizational structure of a firm, the relationships among those geo-industrial clusters are the essential part of the whole picture….
In this new study, my colleagues and I at Indiana University — with support from LinkedIn — have finally overcome these limitations by defining geo-industrial clusters through labor flow and constructing a global labor flow network from LinkedIn’s individual-level job history dataset. Our access to this data was made possible by our selection as one of 11 teams selected to participate in the LinkedIn Economic Graph Challenge.
The transitioning of workers between jobs and firms — also known as labor flow — is considered central in driving firms towards geo-industrial clusters due to knowledge spillover and labor market pooling. In response, we mapped the cluster structure of the world economy based on labor mobility between firms during the last 25 years, constructing a “labor flow network.”
To do this, we leverage LinkedIn’s data on professional demographics and employment histories from more than 500 million people between 1990 and 2015. The network, which captures approximately 130 million job transitions between more than 4 million firms, is the first-ever flow network of global labor.
The resulting “map” allows us to:
- identify geo-industrial clusters systematically and organically using network community detection
- verify the importance of region and industry in labor mobility
- compare the relative importance between the two constraints in different hierarchical levels, and
- reveal the practical advantage of the geo-industrial cluster as a unit of future economic analyses.
- show a better picture of what industry in what region leads the economic growth of the industry or the region, at the same time
- find out emerging and declining skills based on the representativeness of them in growing and declining geo-industrial clusters…(More)”.
Blockchain and Democracy
Literature Review by Jörn Erbguth: “Democratic states are entities where issues are decided by a large group – the people. There is a democratic process that builds upon elections, a legislative procedure, judicial review and separation of powers by checks and balances. Blockchains rely on decentralization, meaning they rely on a large group of participants as well. Blockchains are therefore confronted with similar problems. Even further, blockchains try to avoid central coordinating authorities.
Consensus methods ensure that the systems align with the majority of their participants. Above the layer of the consensus method, blockchain governance coordinates decisions about software updates, bugfixes and possibly other interventions. What are the strengths and weaknesses of this blockchain governance?
Should we use blockchain to secure e-voting? Blockchain governance has two central aspects. First, it is decentralized governance based on a large group of people, which resembles democratic decision-making. Second, it is algorithmic decision-making and limits unwanted human intervention
Cornerstones
Blockchain and democracy can be split into three areas:
First, the use of democratic principles in order to make blockchain work. This ranges from the basic concensus algorithm to the (self-)governance of a blockchain.
Second, blockchain is seen as providing a reliable tool for democracy. This ranges from the use of blockchain for electronic voting to the use in administration.
Third, to study possible impacts of blockchain technology on a democratic society. This focusses on regulatory and legal aspects as well as ethical aspects….(More)”
Hacking for Housing: How open data and civic hacking creates wins for housing advocates
Krista Chan at Sunlight: “…Housing advocates have an essential role to play in protecting residents from the consequences of real estate speculation. But they’re often at a significant disadvantage; the real estate lobby has access to a wealth of data and technological expertise. Civic hackers and open data could play an essential role in leveling the playing field.
Civic hackers have facilitated wins for housing advocates by scraping data or submitting FOIA requests where data is not open and creating apps to help advocates gain insights that they can turn into action.
Hackers at New York City’s Housing Data Coalition created a host of civic apps that identify problematic landlords by exposing owners behind shell companies, or flagging buildings where tenants are at risk of displacement. In a similar vein, Washington DC’s Housing Insights tool aggregates a wide variety of data to help advocates make decisions about affordable housing.
Barriers and opportunities
Today, the degree to which housing data exists, is openly available, and consistently reliable varies widely, even within cities themselves. Cities with robust communities of affordable housing advocacy groups may not be connected to people who can help open data and build usable tools. Even in cities with robust advocacy and civic tech communities, these groups may not know how to work together because of the significant institutional knowledge that’s required to understand how to best support housing advocacy efforts.
In cities where civic hackers have tried to create useful open housing data repositories, similar data cleaning processes have been replicated, such as record linkage of building owners or identification of rent-controlled units. Civic hackers need to take on these data cleaning and “extract, transform, load” (ETL) processes in order to work with the data itself, even if it’s openly available. The Housing Data Coalition has assembled NYC-DB, a tool which builds a postgres database containing a variety of housing related data pertaining to New York City, and Washington DC’s Housing Insights similarly ingests housing data into a postgres database and API for front-end access.
Since these tools are open source, civic hackers in a multitude of cities can use existing work to develop their own, locally relevant tools to support local housing advocates….(More)”.
Concerns About Online Data Privacy Span Generations
Internet Innovations Alliance: “Are Millennials okay with the collection and use of their data online because they grew up with the internet?
In an effort to help inform policymakers about the views of Americans across generations on internet privacy, the Internet Innovation Alliance, in partnership with Icon Talks, the Hispanic Technology & Telecommunications Partnership (HTTP), and the Millennial Action Project, commissioned a national study of U.S. consumers who have witnessed a steady stream of online privacy abuses, data misuses, and security breaches in recent years. The survey examined the concerns of U.S. adults—overall and separated by age group, as well as other demographics—regarding the collection and use of personal data and location information by tech and social media companies, including tailoring the online experience, the potential for their personal financial information to be hacked from online tech and social media companies, and the need for a single, national policy addressing consumer data privacy.
Download: “Concerns About Online Data Privacy Span Generations” IIA white paper pdf.
Download: “Consumer Data Privacy Concerns” Civic Science report pdf….(More)”
Value in the Age of AI
Project Syndicate: “Much has been written about Big Data, artificial intelligence, and automation. The Fourth Industrial Revolution will have far-reaching implications for jobs, ethics, privacy, and equality. But more than that, it will also transform how we think about value – where it comes from, how it is captured, and by whom.
In “Value in the Age of AI,” Project Syndicate, with support from the Dubai Future Foundation, GovLab (New York University), and the Centre for Data & Society (Brussels), will host an ongoing debate about the changing nature of value in the twenty-first century. In the commentaries below, leading thinkers at the intersection of technology, economics, culture, and politics discuss how new technologies are changing our societies, businesses, and individual lived experiences, and what that might mean for our collective future….(More)”.
Strategies and limitations in app usage and human mobility
Paper by Marco De Nadai, Angelo Cardoso, Antonio Lima, Bruno Lepri, and Nuria Oliver: “Cognition has been found to constrain several aspects of human behaviour, such as the number of friends and the number of favourite places a person keeps stable over time. this limitation has been empirically defined in the physical and social spaces. But do people exhibit similar constraints in the digital space? We address this question through the analysis of pseudonymised mobility and mobile application (app) usage data of 400,000 individuals in a European country for six months. Despite the enormous heterogeneity of apps usage, we find that individuals exhibit a conserved capacity that limits the number of applications they regularly use. Moreover, we find that this capacity steadily decreases with age, as does the capacity in the physical space but with more complex dynamics. Even though people might have the same capacity, applications get added and removed over time.
In this respect, we identify two profiles of individuals: app keepers and explorers, which differ in their stable (keepers) vs exploratory (explorers) behaviour regarding their use of mobile applications. Finally, we show that the capacity of applications predicts mobility capacity and vice-versa. By contrast, the behaviour of keepers and explorers may considerably vary across the two domains. Our empirical findings provide an intriguing picture linking human behaviour in the physical and digital worlds which bridges research studies from Computer Science, Social Physics and Computational Social Sciences…(More)”.