Introduction to Special Issue of Politics and Governance by Sarah Giest and Reuben Ng: ” Recent literature has been trying to grasp the extent as to which big data applications affect the governance and policymaking of countries and regions (Boyd & Crawford, 2012; Giest, 2017; Höchtl, Parycek, & Schöllhammer, 2015; Poel, Meyer, & Schroeder, 2018). The discussion includes the comparison to e-government and evidence-based policymaking developments that existed long before the idea of big data entered the policy realm. The theoretical extent of this discussion however lacks some of the more practical consequences that come with the active use of data-driven applications. In fact, much of the work focuses on the input-side of policymaking, looking at which data and technology enters the policy process, however very little is dedicated to the output side.
In short, how has big data shaped data governance and policymaking? The contributions to this thematic issue shed light on this question by looking at a range of factors, such as campaigning in the US election (Trish, 2018) or local government data projects (Durrant, Barnett, & Rempel, 2018). The goal is to unpack the mixture of big data applications and existing policy processes in order to understand whether these new tools and applications enhance or hinder policymaking….(More)”.
The New York Times: “Companies and governments are gaining new powers to follow people across the internet and around the world, and even to peer into their genomes. The benefits of such advances have been apparent for years; the costs — in anonymity, even autonomy — are now becoming clearer. The boundaries of privacy are in dispute, and its future is in doubt. Citizens, politicians and business leaders are asking if societies are making the wisest tradeoffs. The Times is embarking on this months long project to explore the technology and where it’s taking us, and to convene debate about how it can best help realize human potential….(More)”
Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions: “Artificial intelligence (AI) has the potential to transform our world for the better: it can improve healthcare, reduce energy consumption, make cars safer, and enable farmers to use water and natural resources more efficiently. AI can be used to predict environmental and climate change, improve financial risk management and provides the tools to manufacture, with less waste, products tailored to our needs. AI can also help to detect fraud and cybersecurity threats, and enables law enforcement agencies to fight crime more efficiently.
AI can benefit the whole of society and the economy. It is a strategic technology that is now being developed and used at a rapid pace across the world. Nevertheless, AI also brings with it new challenges for the future of work, and raises legal and ethical questions.
To address these challenges and make the most of the opportunities which AI offers, the Commission published a European strategy in April 2018. The strategy places people at the centre of the development of AI — human-centric AI. It is a three-pronged approach to boost the EU’s technological and industrial capacity and AI uptake across the economy, prepare for socio-economic changes, and ensure an appropriate ethical and legal framework.
To deliver on the AI strategy, the Commission developed together with Member States a coordinated plan on AI, which it presented in December 2018, to create synergies, pool data — the raw material for many AI applications — and increase joint investments. The aim is to foster cross-border cooperation and mobilise all players to increase public and private investments to at least EUR 20 billion annually over the next decade.
The Commission doubled its investments in AI in Horizon 2020 and plans to invest EUR 1 billion annually from Horizon Europe and the Digital Europe Programme, in support notably of common data spaces in health, transport and manufacturing, and large experimentation facilities such as smart hospitals and infrastructures for automated vehicles and a strategic research agenda.
To implement such a common strategic research, innovation and deployment agenda the Commission has intensified its dialogue with all relevant stakeholders from industry, research institutes and public authorities. The new Digital Europe programme will also be crucial in helping to make AI available to small and medium-size enterprises across all Member States through digital innovation hubs, strengthened testing and experimentation facilities, data spaces and training programmes.
Building on its reputation for safe and high-quality products, Europe’s ethical approach to AI strengthens citizens’ trust in the digital development and aims at building a competitive advantage for European AI companies. The purpose of this Communication is to launch a comprehensive piloting phase involving stakeholders on the widest scale in order to test the practical implementation of ethical guidance for AI development and use…(More)”.
Paper by Hanna C Norberg: “Blockchain technology is still in its infancy, but already it has begun to revolutionize global trade. Its lure is irresistible because of the simplicity with which it can replace the standard methods of documentation, smooth out logistics, increase transparency, speed up transactions, and ameliorate the planning and tracking of trade.
Blockchain essentially provides the supply chain with an unalterable ledger of verified transactions, and thus enables trust every step of the way through the trade process. Every stakeholder involved in that process – from producer to warehouse worker to shipper to financial institution to recipient at the final destination – can trust that the information contained in that indelible ledger is accurate. Fraud will no longer be an issue, middlemen can be eliminated, shipments tracked, quality control maintained to highest standards and consumers can make decisions based on more than the price. Blockchain dramatically reduces the amount of paperwork involved, along with the myriad of agents typically involved in the process, all of this resulting in soaring efficiencies. Making the most of this new technology, however, requires solid policy. Most people have only a vague idea of what blockchain is. There needs to be a basic understanding of what blockchain can and can’t do, and how it works in the economy and in trade. Once they become familiar with the technology, policy-makers must move on to thinking about what technological issues could be mitigated, solved or improved.
Governments need to explore blockchain’s potential through its use in public-sector projects that demonstrate its workings, its potential and its inevitable limitations. Although blockchain is not nearly as evolved now as the internet was in 2005, co-operation among all stakeholders on issues like taxonomy or policy guides on basic principles is crucial. Those stakeholders include government, industry, academia and civil society. All this must be done while keeping in mind the global nature of blockchain and that blockchain regulations need to be made in synch with regulations on other issues are adjacent to the technology, such as electronic signatures. However, work can be done in the global arena through international initiatives and organizations such as the ISO….(More)”.
NBER Paper by Daron Acemoglu and Pascual Restrepo: “Artificial Intelligence is set to influence every aspect of our lives, not least the way production is organized. AI, as a technology platform, can automate tasks previously performed by labor or create new tasks and activities in which humans can be productively employed. Recent technological change has been biased towards automation, with insufficient focus on creating new tasks where labor can be productively employed. The consequences of this choice have been stagnating labor demand, declining labor share in national income, rising inequality and lower productivity growth. The current tendency is to develop AI in the direction of further automation, but this might mean missing out on the promise of the “right” kind of AI with better economic and social outcomes….(More)”.
Paper by Danielle Citron and Ryan Calo: “The administrative state has undergone radical change in recent decades. In the twentieth century, agencies in the United States generally relied on computers to assist human decision-makers. In the twenty-first century, computers are making agency decisions themselves. Automated systems are increasingly taking human beings out of the loop. Computers terminate Medicaid to cancer patients and deny food stamps to individuals. They identify parents believed to owe child support and initiate collection proceedings against them. Computers purge voters from the rolls and deem small businesses ineligible for federal contracts [1].
Automated systems built in the early 2000s eroded procedural safeguards at the heart of the administrative state. When government makes important decisions that affect our lives, liberty, and property, it owes us “due process”— understood as notice of, and a chance to object to, those decisions. Automated systems, however, frustrate these guarantees. Some systems like the “no-fly” list were designed and deployed in secret; others lacked record-keeping audit trails, making review of the law and facts supporting a system’s decisions impossible. Because programmers working at private contractors lacked training in the law, they distorted policy when translating it into code [2].
Some of us in the academy sounded the alarm as early as the 1990s, offering an array of mechanisms to ensure the accountability and transparency of automated administrative state [3]. Yet the same pathologies continue to plague government decision-making systems today. In some cases, these pathologies have deepened and extended. Agencies lean upon algorithms that turn our personal data into predictions, professing to reflect who we are and what we will do. The algorithms themselves increasingly rely upon techniques, such as deep learning, that are even less amenable to scrutiny than purely statistical models. Ideals of what the administrative law theorist Jerry Mashaw has called “bureaucratic justice” in the form of efficiency with a “human face” feel impossibly distant [4].
The trend toward more prevalent and less transparent automation in agency decision-making is deeply concerning. For a start, we have yet to address in any meaningful way the widening gap between the commitments of due process and the actual practices of contemporary agencies [5]. Nonetheless, agencies rush to automate (surely due to the influence and illusive promises of companies seeking lucrative contracts), trusting algorithms to tell us if criminals should receive probation, if public school teachers should be fired, or if severely disabled individuals should receive less than the maximum of state-funded nursing care [6]. Child welfare agencies conduct intrusive home inspections because some system, which no party to the interaction understands, has rated a poor mother as having a propensity for violence. The challenges of preserving due process in light of algorithmic decision-making is an area of renewed and active attention within academia, civil society, and even the courts [7].
Second, and routinely overlooked, we are applying the new affordances of artificial intelligence in precisely the wrong contexts…(More)”.
Paper by Tarun Ramadorai, Antoine Uettwiller and Ansgar Walther: “We scrape a comprehensive set of US firms’ privacy policies to facilitate research on the supply of data privacy. We analyze these data with the help of expert legal evaluations, and also acquire data on firms’ web tracking activities. We find considerable and systematic variation in privacy policies along multiple dimensions including ease of access, length, readability, and quality, both within and between industries. Motivated by a simple theory of big data acquisition and usage, we analyze the relationship between firm size, knowledge capital intensity, and privacy supply. We find that large firms with intermediate data intensity have longer, legally watertight policies, but are more likely to share user data with third parties….(More)”.
Devin Coldewey at TechCrunch: “A new map of nearly all of Africa shows exactly where the continent’s 1.3 billion people live, down to the meter, which could help everyone from local governments to aid organizations. The map joins others like it from Facebook created by running satellite imagery through a machine learning model.
It’s not exactly that there was some mystery about where people live, but the degree of precision matters. You may know that a million people live in a given region, and that about half are in the bigger city and another quarter in assorted towns. But that leaves hundreds of thousands only accounted for in the vaguest way.
Fortunately, you can always inspect satellite imagery and pick out the spots where small villages and isolated houses and communities are located. The only problem is that Africa is big. Really big. Manually labeling the satellite imagery even from a single mid-sized country like Gabon or Malawi would take a huge amount of time and effort. And for many applications of the data, such as coordinating the response to a natural disaster or distributing vaccinations, time lost is lives lost.
Better to get it all done at once then, right? That’s the idea behind Facebook’s Population Density Maps project, which had already mapped several countries over the last couple of years before the decision was made to take on the entire African continent….
“The maps from Facebook ensure we focus our volunteers’ time and resources on the places they’re most needed, improving the efficacy of our programs,” said Tyler Radford, executive director of the Humanitarian OpenStreetMap Team, one of the project’s partners.
The core idea is straightforward: Match census data (how many people live in a region) with structure data derived from satellite imagery to get a much better idea of where those people are located.
“With just the census data, the best you can do is assume that people live everywhere in the district – buildings, fields, and forests alike,” said Facebook engineer James Gill. “But once you know the building locations, you can skip the fields and forests and only allocate the population to the buildings. This gives you very detailed 30 meter by 30 meter population maps.”
That’s several times more accurate than any extant population map of this size. The analysis is done by a machine learning agent trained on OpenStreetMap data from all over the world, where people have labeled and outlined buildings and other features.
First the huge amount of Africa’s surface that obviously has no structure had to be removed from consideration, reducing the amount of space the team had to evaluate by a factor of a thousand or more. Then, using a region-specific algorithm (because things look a lot different in coastal Morocco than they do in central Chad), the model identifies patches that contain a building….(More)”.
Helen Margetts and Cosmina Dorobantu at Nature: “People produce more than 2.5 quintillion bytes of data each day. Businesses are harnessing these riches using artificial intelligence (AI) to add trillions of dollars in value to goods and services each year. Amazon dispatches items it anticipates customers will buy to regional hubs before they are purchased. Thanks to the vast extractive might of Google and Facebook, every bakery and bicycle shop is the beneficiary of personalized targeted advertising.
But governments have been slow to apply AI to hone their policies and services. The reams of data that governments collect about citizens could, in theory, be used to tailor education to the needs of each child or to fit health care to the genetics and lifestyle of each patient. They could help to predict and prevent traffic deaths, street crime or the necessity of taking children into care. Huge costs of floods, disease outbreaks and financial crises could be alleviated using state-of-the-art modelling. All of these services could become cheaper and more effective.
This dream seems rather distant. Governments have long struggled with much simpler technologies. Flagship policies that rely on information technology (IT) regularly flounder. The Affordable Care Act of former US president Barack Obama nearly crumbled in 2013 when HealthCare.gov, the website enabling Americans to enrol in health insurance plans, kept crashing. Universal Credit, the biggest reform to the UK welfare state since the 1940s, is widely regarded as a disaster because of its failure to pay claimants properly. It has also wasted £837 million (US$1.1 billion) on developing one component of its digital system that was swiftly decommissioned. Canada’s Phoenix pay system, introduced in 2016 to overhaul the federal government’s payroll process, has remunerated 62% of employees incorrectly in each fiscal year since its launch. And My Health Record, Australia’s digital health-records system, saw more than 2.5 million people opt out by the end of January this year over privacy, security and efficacy concerns — roughly 1 in 10 of those who were eligible.
Such failures matter. Technological innovation is essential for the state to maintain its position of authority in a data-intensive world. The digital realm is where citizens live and work, shop and play, meet and fight. Prices for goods are increasingly set by software. Work is mediated through online platforms such as Uber and Deliveroo. Voters receive targeted information — and disinformation — through social media.
Thus the core tasks of governments, such as enforcing regulation, setting employment rights and ensuring fair elections require an understanding of data and algorithms. Here we highlight the main priorities, drawn from our experience of working with policymakers at The Alan Turing Institute in London….(More)”.
Open Access Book by Ben Green: “Smart cities, where technology is used to solve every problem, are hailed as futuristic urban utopias. We are promised that apps, algorithms, and artificial intelligence will relieve congestion, restore democracy, prevent crime, and improve public services. In The Smart Enough City, Ben Green warns against seeing the city only through the lens of technology; taking an exclusively technical view of urban life will lead to cities that appear smart but under the surface are rife with injustice and inequality. He proposes instead that cities strive to be “smart enough”: to embrace technology as a powerful tool when used in conjunction with other forms of social change—but not to value technology as an end in itself….(More)”.
