Jennifer Gabrys at the Sociological Review: “Hydraulic fracturing, or fracking, is an emerging and growing industry that is having considerable effects on environments and health. Yet fracking often lacks environmental regulations that might be understood as governmental forms of care. In some locations in the US, citizens have taken up environmental monitoring as a way to address this perceived absence of care, and to evidence harm in order to argue for new infrastructures of care. This article documents the practices of residents engaged in monitoring air pollution near fracking sites in the US, as well as the participatory and practice-based research undertaken by the Citizen Sense research project to develop monitoring kits for residents to use and test over a period of seven months. Citizen sensing practices for monitoring air pollution can constitute ways of expressing care about environments, communities and individual and public health. Yet practices for documenting and evidencing harm through the ongoing collection of air pollution data are also speculative attempts to make relevant these unrecognised and overlooked considerations of the need for care. Working with the concept of speculation, this article advances alternative notions of evidence, care and policy that attend to citizens’ experiences of living in the gas fields. How do citizen sensing practices work towards alternative ways of evidencing harm? In what ways does monitoring with environmental sensors facilitate this process? And what new speculative practices emerge to challenge the uses of environmental sensors, as well as to expand the types of data gathered, along with their political impact?…(More)”.
How AI Is Crunching Big Data To Improve Healthcare Outcomes
PSFK: “The state of your health shouldn’t be a mystery, nor should patients or doctors have to wait long to find answers to pressing medical concerns. In PSFK’s Future of Health Report, we dig deep into the latest in AI, big data algorithms and IoT tools that are enabling a new, more comprehensive overview of patient data collection and analysis. Machine support, patient information from medical records and conversations with doctors are combined with the latest medical literature to help form a diagnosis without detracting from doctor-patient relations.
The impact of improved AI helps patients form a baseline for well-being and is making changes all across the healthcare industry. AI not only streamlines intake processes and reduces processing volume at clinics, it also controls input and diagnostic errors within a patient record, allowing doctors to focus on patient care and communication, rather than data entry. AI also improves pattern recognition and early diagnosis by learning from multiple patient data sets.
By utilizing deep learning algorithms and software, healthcare providers can connect various libraries of medical information and scan databases of medical records, spotting patterns that lead to more accurate detection and greater breadth of efficiency in medical diagnosis and research. IBM Watson, which has previously been used to help identify genetic markers and develop drugs, is applying its neural learning networks to help doctors correctly diagnose heart abnormalities from medical imaging tests. By scanning thousands of images and learning from correct diagnoses, Watson is able to increase diagnostic accuracy, supporting doctors’ cardiac assessments.
Outside of the doctor’s office, AI is also being used to monitor patient vitals to help create a baseline for well-being. By monitoring health on a day-to-day basis, AI systems can alert patients and medical teams to abnormalities or changes from the baseline in real time, increasing positive outcomes. Take xbird, a mobile platform that uses artificial intelligence to help diabetics understand when hypoglycemic attacks will occur. The AI combines personal and environmental data points from over 20 sensors within mobile and wearable devices to create an automated personal diary and cross references it against blood sugar levels. Patients then share this data with their doctors in order to uncover their unique hypoglycemic triggers and better manage their condition.
In China, meanwhile, web provider Baidu has debuted Melody, a chat-based medical assistant that helps individuals communicate their symptoms, learn of possible diagnoses and connect to medical experts….(More)”.
The DeepMind debacle demands dialogue on data
What can be done to address this deficit? Beyond meeting legal standards, all relevant institutions must take care to show themselves trustworthy in the eyes of the public. The lapses of the Royal Free hospitals and DeepMind provide, by omission, valuable lessons.
The first is to be open about what data are transferred. The extent of data transfer between the Royal Free and DeepMind came to light through investigative journalism. In my opinion, had the project proceeded under open contracting, it would have been subject to public scrutiny, and to questions about whether a company owned by Google — often accused of data monopoly — was best suited to create a relatively simple app.
The second lesson is that data transfer should be proportionate to the task. Information-sharing agreements should specify clear limits. It is unclear why an app for kidney injury requires the identifiable records of every patient seen by three hospitals over a five-year period.
Finally, governance mechanisms must be strengthened. It is shocking to me that the Royal Free did not assess the privacy impact of its actions before handing over access to records. DeepMind does deserve credit for (belatedly) setting up an independent review panel for health-care projects, especially because the panel has a designated budget and has not required members to sign non-disclosure agreements. (The two groups also agreed a new contract late last year, after criticism.)
More is needed. The Information Commissioner asked the Royal Free to improve its processes but did not fine it or require it to rescind data. This rap on the knuckles is unlikely to deter future, potentially worse, misuses of data. People are aware of the potential for over-reach, from the US government’s demands for state voter records to the Chinese government’s alleged plans to create a ‘social credit’ system that would monitor private behaviour.
Innovations such as artificial intelligence, machine learning and the Internet of Things offer great opportunities, but will falter without a public consensus around the role of data. To develop this, all data collectors and crunchers must be open and transparent. Consider how public confidence in genetic modification was lost in Europe, and how that has set back progress.
Public dialogue can build trust through collaborative efforts. A 14-member Citizen’s Reference Panel on health technologies was convened in Ontario, Canada in 2009. The Engage2020 programme incorporates societal input in the Horizon2020 stream of European Union science funding….(More)”
How open data can help the Global South, from disaster relief to voter turnout
Stefaan Verhulst and Andrew Young in The Conversation Global: “The modern era is marked by growing faith in the power of data. “Big data”, “open data”, and “evidence-based decision-making” have become buzzwords, touted as solutions to the world’s most complex and persistent problems, from corruption and famine to the refugee crisis.
While perhaps most pronounced in higher income countries, this trend is now emerging globally. In Africa, Latin America, Asia and beyond, hopes are high that access to data can help developing economies by increasing transparency, fostering sustainable development, building climate resiliency and the like.
This is an exciting prospect, but can opening up data actually make a difference in people’s lives?
Getting data-driven about data
The GovLab at New York University spent the last year trying to answer that question….
Our conclusion: the enthusiasm is justified – as long as it’s tempered with a good measure of realism, too. Here are our six major takeaways:
1. We need a framework – Overall, there is still little evidence to substantiate the enthusiastic claims that open data can foment sustainable development and transform governance. That’s not surprising given the early stage of most open data initiatives.
It may be early for impact evaluation, but it’s not too soon to develop a model that will eventually allow us to assess the impact of opening up data over time.
To that end, the GovLab has created an evidence-based framework that aims to better capture the role of open data in developing countries. The Open Data Logic Framework below focuses on various points in the open data value cycle, from data supply to demand, use and impact.
2. Open data has real promise – Based on this framework and the underlying evidence that fed into it, we can guardedly conclude that open data does in fact spur development – but only under certain conditions and within the right supporting ecosystem.
One well-known success took place after Nepal’s 2015 earthquake when open data helped NGOs map important landmarks such as health facilities and road networks, among other uses.
And in Colombia, the International Centre for Tropical Agriculture launched Aclímate Colombia, a tool that gives smallholder farmers data-driven insight into planting strategies that makes them more resilient to climate change….
3. Open data can improve people’s lives Examining projects in a number of sectors critical to development, including health, humanitarian aid, agriculture, poverty alleviation, energy and education, we found four main ways that data can have an impact….
4. Data can be an asset in development While these impacts are apparent in both developed and developing countries, we believe that open data can have a particularly powerful role in developing economies.
Where data is scarce, as it often is in poorer countries, open data can lead to an inherently more equitable and democratic distribution of information and knowledge. This, in turn, may activate a wider range of expertise to address complex problems; it’s what we in the field call “open innovation”.
This quality can allow resource-starved developing economies to access and leverage the best minds around.
And because trust in government is quite low in many developing economies, the transparency bred of releasing data can have after-effects that go well beyond the immediate impact of the data itself…
5. The ingredients matter To better understand why some open data projects fail while others succeed, we created a “periodic table” of open data (below), which includes 27 enabling factors divided into five broad categories….
6. We can plan for impact Our report ends by identifying how development organisations can catalyse the release and use of open data to make a difference on the ground.
Recommendations include:
· Define the problem, understand the user, and be aware of local conditions;
· Focus on readiness, responsiveness and change management;
· Nurture an open data ecosystem through collaboration and partnerships;
· Have a risk mitigation strategy;
· Secure resources and focus on sustainability; and
· Build a strong evidence base and support more research.
Next steps
In short, while it may still be too early to fully capture open data’s as-of-yet muted impact on developing economies, there are certainly reasons for optimism.
Much like blockchain, drones and other much-hyped technical advances, it’s time to start substantiating the excitement over open data with real, hard evidence.
The next step is to get systematic, using the kind of analytical framework we present here to gain comparative and actionable insight into if, when and how open data works. Only by getting data-driven about open data can we help it live up to its potential….(More)“
Open data on universities – New fuel for transformation
François van Schalkwyk at University World News: “Accessible, usable and relevant open data on South African universities makes it possible for a wide range of stakeholders to monitor, advise and challenge the transformation of South Africa’s universities from an informed perspective.
Some describe data as the new oil while others suggest it is a new form of capital or compare it to electricity. Either way, there appears to be a groundswell of interest in the potential of data to fuel development.
Whether the proliferation of data is skewing development in favour of globally networked elites or disrupting existing asymmetries of information and power, is the subject of ongoing debate. Certainly, there are those who will claim that open data, from a development perspective, could catalyse disruption and redistribution.
Open data is data that is free to use without restriction. Governments and their agencies, universities and their researchers, non-governmental organisations and their donors, and even corporations, are all potential sources of open data.
Open government data, as a public rather than a private resource, embedded in principles of universal access, participation and transparency, is touted as being able to restore the deteriorating levels of trust between citizens and their governments.
Open data promises to do so by making the decisions and processes of the state more transparent and inclusive, empowering citizens to participate and to hold public institutions to account for the distribution of public services and resources.
Benefits of open data
Open data has other benefits over its more cloistered cousins (data in private networks, big data, etc). By democratising access, open data makes possible the use of data on, for example, health services, crime, the environment, procurement and education by a range of different users, each bringing their own perspective to bear on the data. This can expose bias in the data or may improve the quality of the data by surfacing data errors. Both are important when data is used to shape government policies.
By removing barriers to reusing data such as copyright or licence-fees, tech-savvy entrepreneurs can develop applications to assist the public to make more informed decisions by making available easy-to-understand information on medicine prices, crime hot-spots, air quality, beneficial ownership, school performance, etc. And access to open research data can improve quality and efficiency in science.
Scientists can check and confirm the data on which important discoveries are based if the data is open, and, in some cases, researchers can reuse open data from other studies, saving them the cost and effort of collecting the data themselves.
‘Open washing’
But access alone is not enough for open data to realise its potential. Open data must also be used. And data is used if it holds some value for the user. Governments have been known to publish server rooms full of data that no one is interested in to support claims of transparency and supporting the knowledge economy. That practice is called ‘open washing’. …(More)”
Are innovation labs delivering on their promise?
Catherine Cheney at DEVEX: “Next month, a first-of-its-kind event will take place in Denmark, and it will draw on traditions and ways of living in one of the happiest countries in the world to unlock new perspectives on achieving the Sustainable Development Goals.
Called UNLEASH, the new initiative will gather 1,000 young people from around the world in the capital city of Copenhagen. Then the participants will be transported to “folk high schools,” which are learning institutions in the countryside aimed at adult education. There, they will break into teams to tackle issues such as urban sustainability or education and ICT. The most promising ideas will have access to resources, including mentoring, angel investors and business plan development. Finally, all UNLEASH participants will be connected through an alumni network of individuals who come together at the annual event that will move country to country until 2030.
UNLEASH is a global innovation lab. It is just one of a growing number of innovation labs, which bring people together to develop and test new methods to address challenges across the global health, international development and humanitarian response sectors. But while the initiative sounds new and exciting, the description reads much like many other initiatives springing up around the SDGs: identifying innovative, scalable, implementable solutions, supporting disruptive ideas, and accelerating development impact.
As the global development sector seeks to take on global problems as complex as those captured by the SDGs, innovation will certainly be necessary. But with the growing number of innovation labs not translating as quickly as expected to real progress on the SDGs, some in the industry are also starting to ask tough questions: How can these initiatives go beyond generating ideas, transition into growing and scaling, then go on to changing entire systems in order to, for example, achieve SDG 1 to end poverty in all its forms by 2030? Experts tell Devex the road to success will not be an easy one, but those who have tested out and improved upon models of innovation in this sector are sharing what is working, what is not, and what needs to change….(More)”.
Government at a Glance 2017
OECD: “Government at a Glance 2017 provides the latest available data on public administrations in OECD countries. Where possible, it also reports data for Brazil, China, Colombia, Costa Rica, India, Indonesia, Lithuania, the Russian Federation, and South Africa. This edition contains new indicators on public sector emploympent, institutions, budgeting practices and procedures, regulatory governance, risk management and communication, open government data and public sector innovation. This edition also includes for the first time a number of scorecards comparing the level of access, responsiveness and quality of services in three key areas: health care, education and justice.
Each indicator in the publication is presented in a user-friendly format, consisting of graphs and/or charts illustrating variations across countries and over time, brief descriptive analyses highlighting the major findings conveyed by the data, and a methodological section on the definition of the indicator and any limitations in data comparability. A database containing qualitative and quantitative indicators on government is available on line. It is updated twice a year as new data are released. The database, countries fact sheets and other online supplements can be found at www.oecd.org/gov/govataglance.htm.”
AI, people, and society
Eric Horvitz at Science: “In an essay about his science fiction, Isaac Asimov reflected that “it became very common…to picture robots as dangerous devices that invariably destroyed their creators.” He rejected this view and formulated the “laws of robotics,” aimed at ensuring the safety and benevolence of robotic systems. Asimov’s stories about the relationship between people and robots were only a few years old when the phrase “artificial intelligence” (AI) was used for the first time in a 1955 proposal for a study on using computers to “…solve kinds of problems now reserved for humans.” Over the half-century since that study, AI has matured into subdisciplines that have yielded a constellation of methods that enable perception, learning, reasoning, and natural language understanding.
Growing exuberance about AI has come in the wake of surprising jumps in the accuracy of machine pattern recognition using methods referred to as “deep learning.” The advances have put new capabilities in the hands of consumers, including speech-to-speech translation and semi-autonomous driving. Yet, many hard challenges persist—and AI scientists remain mystified by numerous capabilities of human intellect.
Excitement about AI has been tempered by concerns about potential downsides. Some fear the rise of superintelligences and the loss of control of AI systems, echoing themes from age-old stories. Others have focused on nearer-term issues, highlighting potential adverse outcomes. For example, data-fueled classifiers used to guide high-stakes decisions in health care and criminal justice may be influenced by biases buried deep in data sets, leading to unfair and inaccurate inferences. Other imminent concerns include legal and ethical issues regarding decisions made by autonomous systems, difficulties with explaining inferences, threats to civil liberties through new forms of surveillance, precision manipulation aimed at persuasion, criminal uses of AI, destabilizing influences in military applications, and the potential to displace workers from jobs and to amplify inequities in wealth.
As we push AI science forward, it will be critical to address the influences of AI on people and society, on short- and long-term scales. Valuable assessments and guidance can be developed through focused studies, monitoring, and analysis. The broad reach of AI’s influences requires engagement with interdisciplinary groups, including computer scientists, social scientists, psychologists, economists, and lawyers. On longer-term issues, conversations are needed to bridge differences of opinion about the possibilities of superintelligence and malevolent AI. Promising directions include working to specify trajectories and outcomes, and engaging computer scientists and engineers with expertise in software verification, security, and principles of failsafe design….Asimov concludes in his essay, “I could not bring myself to believe that if knowledge presented danger, the solution was ignorance. To me, it always seemed that the solution had to be wisdom. You did not refuse to look at danger, rather you learned how to handle it safely.” Indeed, the path forward for AI should be guided by intellectual curiosity, care, and collaboration….(More)”
Political Inequality in Affluent Democracies
Larry M. Bartels for the SSRC: “A key characteristic of a democracy,” according to Robert Dahl, is “the continuing responsiveness of the government to the preferences of its citizens, considered as political equals.” Much empirical research over the past half century, most of it focusing on the United States, has examined the relationship between citizens’ policy preferences and the policy choices of elected officials. According to Robert Shapiro, this research has generated “evidence for strong effects of public opinion on government policies,” providing “a sanguine picture of democracy at work.”
In recent years, however, scholars of American politics have produced striking evidence that the apparent “strong effects” of aggregate public opinion in these studies mask severe inequalities in responsiveness. As Martin Gilens put it, “The American government does respond to the public’s preferences, but that responsiveness is strongly tilted toward the most affluent citizens. Indeed, under most circumstances, the preferences of the vast majority of Americans appear to have essentially no impact on which policies the government does or doesn’t adopt.”
One possible interpretation of these findings is that the American political system is anomalous in its apparent disregard for the preferences of middle-class and poor people. In that case, the severe political inequality documented there would presumably be accounted for by distinctive features of the United States, such as its system of private campaign finance, its weak labor unions, or its individualistic political culture. But, what if severe political inequality is endemic in affluent democracies? That would suggest that fiddling with the political institutions of the United States to make them more like Denmark’s (or vice versa) would be unlikely to bring us significantly closer to satisfying Dahl’s standard of democratic equality. We would be forced to conclude either that Dahl’s standard is fundamentally misguided or that none of the political systems commonly identified as democratic comes anywhere close to meriting that designation.
Analyzing policy responsiveness
“I have attempted to test the extent to which policymakers in a variety of affluent democracies respond to the preferences of their citizens considered as political equals.”
To address this question, I have attempted to test the extent to which policymakers in a variety of affluent democracies respond to the preferences of their citizens considered as political equals. My analyses focus on the relationship between public opinion and government spending on social welfare programs, including pensions, health, education, and unemployment benefits. These programs represent a major share of government spending in every affluent democracy and, arguably, an important source of public well-being. Moreover, social spending figures prominently in the comparative literature on the political impact of public opinion in affluent democracies, with major scholarly works suggesting that it is significantly influenced by citizens’ preferences.
My analyses employ data on citizens’ views about social spending and the welfare state from three major cross-national survey projects—the International Social Survey Programme (ISSP), the World Values Survey (WVS), and the European Values Survey (EVS). In combination, these three sources provide relevant opinion data from 160 surveys conducted between 1985 and 2012 in 30 countries, including most of the established democracies of Western Europe and the English-speaking world and some newer democracies in Eastern Europe, Latin America, and Asia. I examine shifts in (real per capita) social spending in the two years following each survey. Does greater public enthusiasm for the welfare state lead to increases in social spending, other things being equal? And, more importantly here, do the views of low-income people have the same apparent influence on policy as the views of affluent people?…(More)”.
Intelligent sharing: unleashing the potential of health and care data in the UK to transform outcomes
Report by Future Care Capital: “….Data is often referred to as the ‘new oil’ – the 21st century raw material which, when hitched to algorithmic refinement, may be mined for insight and value – and ‘data flows’ are said to have exerted a greater impact upon global growth than traditional goods flows in recent years (Manyika et al, 2016). Small wonder, then, that governments around the world are endeavouring to strike a balance between individual privacy rights and protections on the one hand, and organisational permissions to facilitate the creation of social, economic and environmental value from broad-ranging data on the other: ‘data rights’ are now of critical importance courtesy of technological advancements. The tension between the two is particularly evident where health and care data in the UK is concerned. Individuals are broadly content with anonymised data from their medical records being used for public benefit but are, understandably, anxious about the implications of the most intimate aspects of their lives being hacked or, else, shared without their knowledge or consent….
The potential for health and care data to be transformative remains, and there is growing concern that opportunities to improve the use of health and care data in peoples’ interests are being missed….
we recommend additional support for digitisation efforts in social care settings. We call upon the Government to streamline processes associated with Information Governance (IG) modelling to help data sharing initiatives that traverse organisational boundaries. We also advocate for investment and additional legal safeguards to make more anonymised data sets available for research and innovation. Crucially, we recommend expediting the scope for individuals to contribute health and care data to sharing initiatives led by the public sector through promotion, education and pilot activities – so that data is deployed to transform public health and support the ‘pivot to prevention’.
In Chapter Two, we explore the rationale and scope for the UK to build upon emergent practice from around the world and become a global leader in ‘data philanthropy’ – to push at the boundaries of existing plans and programmes, and support the development of and access to unrivalled health and care data sets. We look at member-controlled ‘data cooperatives’ and what we’ve termed ‘data communities’ operated by trusted intermediaries. We also explore ‘data collaboratives’ which involve the private sector engaging in data philanthropy for public benefit. Here, we make recommendations about promoting a culture of data philanthropy through the demonstration of tangible benefits to participants and the wider public, and we call upon Government to assess the appetite and feasibility of establishing the world’s first National Health and Care Data Donor Bank….(More)”