Working document by the European Commission’s High-Level Expert Group on Artificial Intelligence (AI HLEG): “…Artificial Intelligence (AI) is one of the most transformative forces of our time, and is bound to alter the fabric of society. It presents a great opportunity to increase prosperity and growth, which Europe must strive to achieve. Over the last decade, major advances were realised due to the availability of vast amounts of digital data, powerful computing architectures, and advances in AI techniques such as machine learning. Major AI-enabled developments in autonomous vehicles, healthcare, home/service robots, education or cybersecurity are improving the quality of our lives every day. Furthermore, AI is key for addressing many of the grand challenges facing the world, such as global health and wellbeing, climate change, reliable legal and democratic systems and others expressed in the United Nations Sustainable Development Goals.
Having the capability to generate tremendous benefits for individuals and society, AI also gives rise to certain risks that should be properly managed. Given that, on the whole, AI’s benefits outweigh its risks, we must ensure to follow the road that maximises the benefits of AI while minimising its risks. To ensure that we stay on the right track, a human-centric approach to AI is needed, forcing us to keep in mind that the development and use of AI should not be seen as a means in itself, but as having the goal to increase human well-being. Trustworthy AI will be our north star, since human beings will only be able to confidently and fully reap the benefits of AI if they can trust the technology.
Trustworthy AI has two components: (1) it should respect fundamental rights, applicable regulation and core principles and values, ensuring an “ethical purpose” and (2) it should be technically robust and reliable since, even with good intentions, a lack of technological mastery can cause unintentional harm.
These Guidelines therefore set out a framework for Trustworthy AI:
Chapter I deals with ensuring AI’s ethical purpose, by setting out the fundamental rights, principles and values that it should comply with.
From those principles, Chapter II derives guidance on the realisation of Trustworthy AI, tackling both ethical purpose and technical robustness. This is done by listing the requirements for Trustworthy AI and offering an overview of technical and non-technical methods that can be used for its implementation.
Chapter III subsequently operationalises the requirements by providing a concrete but nonexhaustive assessment list for Trustworthy AI. This list is then adapted to specific use cases. …(More)”
Booklet by Mimi Onuoha and Diana Nucera: “..this booklet aims to fill the gaps in information about AI by creating accessible materials that inform communities and allow them to identify what their ideal futures with AI can look like. Although the contents of this booklet focus on demystifying AI, we find it important to state that the benefits of any technology should be felt by all of us. Too often, the challenges presented by new technology spell out yet another tale of racism, sexism, gender inequality, ableism, and lack of consent within digital culture.
The path to a fair future starts with the humans behind the machines, not the machines themselves. Self-reflection and a radical transformation of our relationships to our environment and each other are at the heart of combating structural inequality. But understanding what it takes to create a fair and just society is the first step. In creating this booklet, we start from the belief that equity begins with education…For those who wish to learn more about specific topics, we recommend looking at the table of contents and choosing sections to read. For more hands-on learners, we have also included a number of workbook activities that allow the material to be explored in a more active fashion.
We hope that this booklet inspires and informs those who are developing emerging technologies to reflect on how these technologies can impact our societies. We also hope that this booklet inspires and informs black, brown, indigenous, and immigrant communities to reclaim technology as a tool of liberation…(More)”.
Danny Lämmerhirt at Open Knowledge Foundation: “Citizen-generated data (CGD) expands what gets measured, how, and for what purpose. As the collection and engagement with CGD increases in relevance and visibility, public institutions can learn from existing initiatives about what CGD initiatives do, how they enable different forms of sense-making and how this may further progress around the Sustainable Development Goals.
Our report, as well as a guide for governments (find the layouted version here, as well as a living document here) shall help start conversations around the different approaches of doing and organising CGD. When CGD becomes good enough depends on the purpose it is used for but also how CGD is situated in relation to other data.
As our work wishes to be illustrative rather than comprehensive, we started with a list of over 230 projects that were associated with the term “citizen-generated data” on Google Search, using an approach known as “search as research” (Rogers, 2013). Outgoing from this list, we developed case studies on a range of prominent CGD examples.
The report identifies several benefits CGD can bring for implementing and monitoring the SDGs, underlining the importance for public institutions to further support these initiatives.
Figure 1: Illustration of tasks underpinning CGD initiatives and their workflows
Key findings:
Dealing with data is usually much more than ‘just producing’ data. CGD initiativesopen up new types of relationships between individuals, civil society and public institutions. This includes local development and educational programmes, community outreach, and collaborative strategies for monitoring, auditing, planning and decision-making.
Generating data takes many shapes, from collecting new data in the field, to compiling, annotating, and structuring existing data to enable new ways of seeing things through data. Accessing and working with existing (government) data is often an important enabling condition for CGD initiatives to start in the first place.
CGD initiatives can help gathering data in regions otherwise not reachable. Some CGD approaches may provide updated and detailed data at lower costs and faster than official data collections.
Beyond filling data gaps, official measurements can be expanded, complemented, or cross-verified. This includes pattern and trend identification and the creation of baseline indicators for further research. CGD can help governments detect anomalies, test the accuracy of existing monitoring processes, understand the context around phenomena, and initiate its own follow-up data collections.
CGD can inform several actions to achieve the SDGs. Beyond education, community engagement and community-based problem solving, this includes baseline research, planning and strategy development, allocation and coordination of public and private programs, as well as improvement to public services.
CGD must be ‘good enough’ for different (and varying) purposes. Governments already develop pragmatic ways to negotiate and assess the usefulness of data for a specific task. CGD may be particularly useful when agencies have a clear remit or responsibility to manage a problem.
Data quality can be comparable to official data collections, provided tasks are sufficiently easy to conduct, tool quality is high enough, and sufficient training, resources and quality assurance are provided….(More)”.
Paper by Klievink, Bram, van der Voort, Haiko and Veeneman, Wijnand: “Driven by the technological capabilities that ICTs offer, data enable new ways to generate value for both society and the parties that own or offer the data. This article looks at the idea of data collaboratives as a form of cross-sector partnership to exchange and integrate data and data use to generate public value. The concept thereby bridges data-driven value creation and collaboration, both current themes in the field.
To understand how data collaboratives can add value in a public governance context, we exploratively studied the qualitative longitudinal case of an infomobility platform. We investigated the ability of a data collaborative to produce results while facing significant challenges and tensions between the goals of parties, each having the conflicting objectives of simultaneously retaining control whilst allowing for generativity. Taken together, the literature and case study findings help us to understand the emergence and viability of data collaboratives. Although limited by this study’s explorative nature, we find that conditions such as prior history of collaboration and supportive rules of the game are key to the emergence of collaboration. Positive feedback between trust and the collaboration process can institutionalise the collaborative, which helps it survive if conditions change for the worse….(More)”.
Book by Daniel Neyland: “This open access book begins with an algorithm–a set of IF…THEN rules used in the development of a new, ethical, video surveillance architecture for transport hubs. Readers are invited to follow the algorithm over three years, charting its everyday life. Questions of ethics, transparency, accountability and market value must be grasped by the algorithm in a series of ever more demanding forms of experimentation. Here the algorithm must prove its ability to get a grip on everyday life if it is to become an ordinary feature of the settings where it is being put to work. Through investigating the everyday life of the algorithm, the book opens a conversation with existing social science research that tends to focus on the power and opacity of algorithms. In this book we have unique access to the algorithm’s design, development and testing, but can also bear witness to its fragility and dependency on others….(More)”.
Prediction by Geoff Mulgan, Eva Grobbink and Vincent Straub: “The USSR’s launch of the Sputnik 1 satellite in 1958 was a major psychological blow to the United States. The US had believed it was technologically far ahead of its rival, but was confronted with proof that the USSR was pulling ahead in some fields. After a bout of soul-searching the country responded with extraordinary vigour, massively increasing investment in space technologies and promising to put a man on the Moon by the end of the 1960s.
In 2019, China’s success in smart cities could prompt a similar “Sputnik Moment” for the rest of the world. It may not be as dramatic as that of 1958. But unlike beeping satellites and Moon landings, it could be coming to a town near you….
The concept of a “smart city” has been around for several decades, often associated with hype, grandiose failures, and an overemphasis on hardware rather than people (Nesta has previously written on how we can rethink smart cities and ensure digital innovation realises the potential of technology and people). But various technologies are now coming of age which bring the vision of a smart city closer to fruition. China is in the forefront, investing heavily in sensors and infrastructures, and its ET City Brain project shows just how far the country’s thinking has progressed.
First launched in September 2016, ET City Brain is a collaboration between Chinese technology giant Alibaba and several cities. It was first trialled in Hangzhou, the hometown of Alibaba’s executive chairman, Jack Ma, but has since expanded to other Chinese cities. Earlier this year, Kuala Lumpurbecame the first city outside of China to import the ET City Brain model.
The ET City Brain system gathers large amounts of data (including logs, videos, and data stream) from sensors. These are then processed by algorithms in supercomputers and fed back into control centres around the city for administrators to act on—in some cases, automation means the system works without any human intervention at all.
So far, the project has been used to monitor congestion in Hangzhou, improve the response of emergency services in Guangzhou, and detect traffic accidents in Suzhou. In Hangzhou, Alibaba was given control of 104 traffic light junctions in the city’s Xiaoshan district and tasked with managing traffic flows. By combining mass video surveillance with live data from public transportation systems, ET City Brain was able to autonomously change traffic lights so that emergency vehicles could travel to accident scenes without interruption. As a result, arrival times for ambulances improved by 49 percent….(More)”.
Peter Andras et al in IEEE Technology and Society Magazine: “Intelligent machines have reached capabilities that go beyond a level that a human being can fully comprehend without sufficiently detailed understanding of the underlying mechanisms. The choice of moves in the game Go (generated by Deep Mind’s Alpha Go Zero) are an impressive example of an artificial intelligence system calculating results that even a human expert for the game can hardly retrace. But this is, quite literally, a toy example. In reality, intelligent algorithms are encroaching more and more into our everyday lives, be it through algorithms that recommend products for us to buy, or whole systems such as driverless vehicles. We are delegating ever more aspects of our daily routines to machines, and this trend looks set to continue in the future. Indeed, continued economic growth is set to depend on it. The nature of human-computer interaction in the world that the digital transformation is creating will require (mutual) trust between humans and intelligent, or seemingly intelligent, machines. But what does it mean to trust an intelligent machine? How can trust be established between human societies and intelligent machines?…(More)”.
Blog Post by Beth Noveck: “…The Inter-American Development Bank (IADB) has published an important, practical and prescriptive report with recommendations for every sector of society from government to individuals on innovative and effective approaches to combatting corruption. While focused on Latin America, the report’s proposals, especially those on the application of new technology in the fight against corruption, are relevant around the world….
The recommendations about the use of new technologies, including big data, blockchain and collective intelligence, are drawn from an effort undertaken last year by the Governance Lab at New York University’s Tandon School of Engineering to crowdsource such solutions and advice on how to implement them from a hundred global experts. (See the Smarter Crowdsourcing against Corruption report here.)…
Big data, when published as open data, namely in a form that can be re-used without legal or technical restriction and in a machine-readable format that computers can analyze, is another tool in the fight against corruption. With machine readable, big and open data, those outside of government can pinpoint and measure irregularities in government contracting, as Instituto Observ is doing in Brazil.
Opening up judicial data, such as information about case processing times, judges’ and prosecutors’ salaries, information about selection processes, such as CV’s, professional and academic backgrounds, and written and oral exam scores provides activists and reformers with the tools to fight judicial corruption. The Civil Association for Equality and Justice (ACIJ) (a non-profit advocacy group) in Argentina uses such open justice data in its Concursos Transparentes (Transparent Contests) to fight judicial corruption. Jusbrasil is a private open justice company also using open data to reform the courts in Brazil….(More)”
Yves-Alexandre de Montjoye et al in Nature: “The breadcrumbs we leave behind when using our mobile phones—who somebody calls, for how long, and from where—contain unprecedented insights about us and our societies. Researchers have compared the recent availability of large-scale behavioral datasets, such as the ones generated by mobile phones, to the invention of the microscope, giving rise to the new field of computational social science.
With mobile phone penetration rates reaching 90% and under-resourced national statistical agencies, the data generated by our phones—traditional Call Detail Records (CDR) but also high-frequency x-Detail Record (xDR)—have the potential to become a primary data source to tackle crucial humanitarian questions in low- and middle-income countries. For instance, they have already been used to monitor population displacement after disasters, to provide real-time traffic information, and to improve our understanding of the dynamics of infectious diseases. These data are also used by governmental and industry practitioners in high-income countries.
While there is little doubt on the potential of mobile phone data for good, these data contain intimate details of our lives: rich information about our whereabouts, social life, preferences, and potentially even finances. A BCG study showed, e.g., that 60% of Americans consider location data and phone number history—both available in mobile phone data—as “private”.
Historically and legally, the balance between the societal value of statistical data (in aggregate) and the protection of privacy of individuals has been achieved through data anonymization. While hundreds of different anonymization algorithms exist, most of them are variations and improvements of the seminal k-anonymity algorithm introduced in 1998. Recent studies have, however, shown that pseudonymization and standard de-identification are not sufficient to prevent users from being re-identified in mobile phone data. Four data points—approximate places and times where an individual was present—have been shown to be enough to uniquely re-identify them 95% of the time in a mobile phone dataset of 1.5 million people. Furthermore, re-identification estimations using unicity—a metric to evaluate the risk of re-identification in large-scale datasets—and attempts at k-anonymizing mobile phone data ruled out de-identification as sufficient to truly anonymize the data. This was echoed in the recent report of the [US] President’s Council of Advisors on Science and Technology on Big Data Privacy which consider de-identification to be useful as an “added safeguard, but [emphasized that] it is not robust against near-term future re-identification methods”.
The limits of the historical de-identification framework to adequately balance risks and benefits in the use of mobile phone data are a major hindrance to their use by researchers, development practitioners, humanitarian workers, and companies. This became particularly clear at the height of the Ebola crisis, when qualified researchers (including some of us) were prevented from accessing relevant mobile phone data on time despite efforts by mobile phone operators, the GSMA, and UN agencies, with privacy being cited as one of the main concerns.
These privacy concerns are, in our opinion, due to the failures of the traditional de-identification model and the lack of a modern and agreed upon framework for the privacy-conscientious use of mobile phone data by third-parties especially in the context of the EU General Data Protection Regulation (GDPR). Such frameworks have been developed for the anonymous use of other sensitive data such as census, household survey, and tax data. The positive societal impact of making these data accessible and the technical means available to protect people’s identity have been considered and a trade-off, albeit far from perfect, has been agreed on and implemented. This has allowed the data to be used in aggregate for the benefit of society. Such thinking and an agreed upon set of models has been missing so far for mobile phone data. This has left data protection authorities, mobile phone operators, and data users with little guidance on technically sound yet reasonable models for the privacy-conscientious use of mobile phone data. This has often resulted in suboptimal tradeoffs if any.
In this paper, we propose four models for the privacy-conscientious use of mobile phone data (Fig. 1). All of these models 1) focus on a use of mobile phone data in which only statistical, aggregate information is ultimately needed by a third-party and, while this needs to be confirmed on a per-country basis, 2) are designed to fall under the legal umbrella of “anonymous use of the data”. Examples of cases in which only statistical aggregated information is ultimately needed by the third-party are discussed below. They would include, e.g., disaster management, mobility analysis, or the training of AI algorithms in which only aggregate information on people’s mobility is ultimately needed by agencies, and exclude cases in which individual-level identifiable information is needed such as targeted advertising or loans based on behavioral data.
Figure 1: Matrix of the four models for the privacy-conscientious use of mobile phone data.
First, it is important to insist that none of these models is a silver bullet…(More)”.
Paper by Daniel Kondor, Behrooz Hashemian, Yves-Alexandre de Montjoye and Carlo Ratti: “The problem of unicity and reidentifiability of records in large-scale databases has been studied in different contexts and approaches, with focus on preserving privacy or matching records from different data sources. With an increasing number of service providers nowadays routinely collecting location traces of their users on unprecedented scales, there is a pronounced interest in the possibility of matching records and datasets based on spatial trajectories. Extending previous work on reidentifiability of spatial data and trajectory matching, we present the first large-scale analysis of user matchability in real mobility datasets on realistic scales, i.e. among two datasets that consist of several million people’s mobility traces, coming from a mobile network operator and transportation smart card usage. We extract the relevant statistical properties which influence the matching process and analyze their impact on the matchability of users. We show that for individuals with typical activity in the transportation system (those making 3-4 trips per day on average), a matching algorithm based on the co-occurrence of their activities is expected to achieve a 16.8% success only after a one-week long observation of their mobility traces, and over 55% after four weeks. We show that the main determinant of matchability is the expected number of co-occurring records in the two datasets. Finally, we discuss different scenarios in terms of data collection frequency and give estimates of matchability over time. We show that with higher frequency data collection becoming more common, we can expect much higher success rates in even shorter intervals….(More)”.
