DATA – Page 429 – The Living Library

Artificial Intelligence Could Help Colleges Better Plan What Courses They Should Offer

Curated on December 16, 2016May 29, 2019 by Stefaan Verhulst

Jeffrey R. Young at EdSsurge: Big data could help community colleges better predict how industries are changing so they can tailor their IT courses and other programs. After all, if Amazon can forecast what consumers will buy and prestock items in their warehouses to meet the expected demand, why can’t colleges do the same thing when planning their curricula, using predictive analytics to make sure new degree or certificates programs are started just in time for expanding job opportunities?

That’s the argument made by Gordon Freedman, president of the nonprofit National Laboratory for Education Transformation. He’s part of a new center that will do just that, by building a data warehouse that brings together up-to-date information on what skills employers need and what colleges currently offer—and then applying artificial intelligence to attempt to predict when sectors or certain employment needs might be expanding.

He calls the approach “opportunity engineering,” and the center boasts some heavy-hitting players to assist in the efforts, including the University of Chicago, the San Diego Supercomputing Center and Argonne National Laboratory. It’s called the National Center for Opportunity Engineering & Analysis.

Ian Roark, vice president of workforce development at Pima Community College in Arizona, is among those eager for this kind of “opportunity engineering” to emerge.

He explains when colleges want to start new programs, they face a long haul—it takes time to develop a new curriculum, put it through an internal review, and then send it through an accreditor….

Other players are already trying to translate the job market into a giant data set to spot trends. LinkedIn sits on one of the biggest troves of data, with hundreds of millions of job profiles, and ambitions to create what it calls the “economic graph” of the economy. But not everyone is on LinkedIn, which attracts mainly those in white-collar jobs. And companies such as Burning Glass Technologies have scanned hundreds of thousands of job listings and attempt to provide real-time intelligence on what employers say they’re looking for. Those still don’t paint the full picture, Freedman argues, such as what jobs are forming at companies.

“We need better information from the employer, better information from the job seeker and better information from the college, and that’s what we’re going after,” Freedman says…(More)”.

Can you crowdsource water quality data?

Curated on December 16, 2016May 29, 2019 by Stefaan Verhulst

Pratibha Mistry at The Water Blog (Worldbank): “The recently released Contextual Framework for Crowdsourcing Water Quality Data lays out a strategy for citizen engagement in decentralized water quality monitoring, enabled by the “mobile revolution.”

According to the WHO, 1.8 billion people lack access to safe drinking water worldwide. Poor source water quality, non-existent or insufficient treatment, and defects in water distribution systems and storage mean these consumers use water that often doesn’t meet the WHO’s Guidelines for Drinking Water Quality.

The crowdsourcing framework develops a strategy to engage citizens in measuring and learning about the quality of their own drinking water. Through their participation, citizens provide utilities and water supply agencies with cost-effective water quality data in near-real time. Following a typical crowdsourcing model: consumers use their mobile phones to report water quality information to a central service. That service receives the information, then repackages and shares it via mobile phone messages, websites, dashboards, and social media. Individual citizens can thus be educated about their water quality, and water management agencies and other stakeholders can use the data to improve water management; it’s a win-win.

*A well-implemented crowdsourcing project both depends on and benefits end users.*Source: Figure modified from Hutchings, M., Dev, A., Palaniappan, M., Srinivasan, V., Ramanathan, N., Taylor, J. 2012. “mWASH: Mobile Phone Applications for the Water, Sanitation, and Hygiene Sector.” Pacific Institute, Oakland, California. 114 p. (*Link to full text*)

Several groups, from the private sector to academia to non-profits, have taken a recent interest in developing a variety of so-called mWASH apps (mobile phone applications for the water, sanitation, and hygiene WASH sector). A recent academic study analyzed how mobile phones might facilitate the flow of water quality data between water suppliers and public health agencies in Africa. USAID has invested in piloting a mobile application in Tanzania to help consumers test their water for E. coli….(More)”

Introducing the Agricultural Open Data Package: BETA Version

Curated on December 14, 2016August 3, 2018 by Stefaan Verhulst

PressRelease: “GODAN, Open Data for Development (OD4D) Network, Open Data Charter, and the Open Data Institute are pleased to announce the release of the Agricultural Open Data Package: BETA version. …The Agriculture Open Data Package (http://AgPack.info) has been designed to help governments get to impact with open data in the agriculture sector. This practical resource provides key policy areas, key data categories, examples datasets, relevant interoperability initiatives, and use cases that policymakers and other stakeholders in the agriculture sector or open data should focus on, in order to address food security challenges.

The Package is meant as a source of inspiration and an invitation to start a national open data for agriculture initiative.

In the Package we identify fourteen key categories of data and discuss the effort it will take for a government to make this data available in a meaningful way. …

The Package also highlights more than ten use cases (the number is growing) demonstrating how open data is being harnessed to address sustainable agriculture and food security around the world. Examples include:

mapping water points to optimise scarce resource allocation in Burkina Faso
surfacing daily price information on multiple food commodities across India
benchmarking agricultural productivity in the Netherlands

Where relevant we also highlight applicable interoperability initiatives, such as open contracting, international aid transparency initiative (IATI), and global product classification (GPC) standards.

We recognise that the agriculture sector is diverse, with many contextual differences affecting scope of activities, priorities and capacities. In the full version of the Agricultural Open Data Package we discuss important implementation considerations such as inter-agency coordination and resourcing to develop an appropriate data infrastructure and a healthy data ‘ecosystem’ for agriculture….(More)”

Four steps to precision public health

Curated on December 13, 2016August 3, 2018 by Stefaan Verhulst

Scott F. Dowell, David Blazes & Susan Desmond-Hellmann at Nature: “When domestic transmission of Zika virus was confirmed in the United States in July 2016, the entire country was not declared at risk — nor even the entire state of Florida. Instead, precise surveillance defined two at-risk areas of Miami-Dade County, neighbourhoods measuring just 2.6 and 3.9 square kilometres. Travel advisories and mosquito control focused on those regions. Six weeks later, ongoing surveillance convinced officials to lift restrictions in one area and expand the other.

By contrast, a campaign against yellow fever launched this year in sub-Saharan Africa defines risk at the level of entire nations, often hundreds of thousands of square kilometres. More granular assessments have been deemed too complex.

The use of data to guide interventions that benefit populations more efficiently is a strategy we call precision public health. It requires robust primary surveillance data, rapid application of sophisticated analytics to track the geographical distribution of disease, and the capacity to act on such information¹.

The availability and use of precise data is becoming the norm in wealthy countries. But large swathes of the developing world are not reaping its advantages. In Guinea, it took months to assemble enough data to clearly identify the start of the largest Ebola outbreak in history. This should take days. Sub-Saharan Africa has the highest rates of childhood mortality in the world; it is also where we know the least about causes of death…..

The value of precise disease tracking was baked into epidemiology from the start. In 1854, John Snow famously located cholera cases in London. His mapping of the spread of infection through contaminated water dealt a blow to the idea that the disease was caused by bad air. These days, people and pathogens move across the globe swiftly and in great numbers. In 2009, the H1N1 ‘swine flu’ influenza virus took just 35 days to spread from Mexico and the United States to China, South Korea and 12 other countries…

The public-health community is sharing more data faster; expectations are higher than ever that data will be available from clinical trials and from disease surveillance. In the past two years, the US National Institutes of Health, the Wellcome Trust in London and the Gates Foundation have all instituted open data policies for their grant recipients, and leading journals have declared that sharing data during disease emergencies will not impede later publication.

Meanwhile, improved analysis, data visualization and machine learning have expanded our ability to use disparate data sources to decide what to do. A study published last year⁴ used precise geospatial modelling to infer that insecticide-treated bed nets were the single most influential intervention in the rapid decline of malaria.

However, in many parts of the developing world, there are still hurdles to the collection, analysis and use of more precise public-health data. Work towards malaria elimination in South Africa, for example, has depended largely on paper reporting forms, which are collected and entered manually each week by dozens of subdistricts, and eventually analysed at the province level. This process would be much faster if field workers filed reports from mobile phones.

Sources: Ref. 8/Bill & Melinda Gates Foundation

…Frontline workers should not find themselves frustrated by global programmes that fail to take into account data on local circumstances. Wherever they live — in a village, city or country, in the global south or north — people have the right to public-health decisions that are based on the best data and science possible, that minimize risk and cost, and maximize health in their communities…(More)”

neveragain.tech

Curated on December 13, 2016August 3, 2018 by Stefaan Verhulst

neveragain.tech: “We, the undersigned, are employees of tech organizations and companies based in the United States. We are engineers, designers, business executives, and others whose jobs include managing or processing data about people. We are choosing to stand in solidarity with Muslim Americans, immigrants, and all people whose lives and livelihoods are threatened by the incoming administration’s proposed data collection policies. We refuse to build a database of people based on their Constitutionally-protected religious beliefs. We refuse to facilitate mass deportations of people the government believes to be undesirable…..

Today we stand together to say: not on our watch, and never again.

We commit to the following actions:

We refuse to participate in the creation of databases of identifying information for the United States government to target individuals based on race, religion, or national origin.
We will advocate within our organizations:
- to minimize the collection and retention of data that would facilitate ethnic or religious targeting.
- to scale back existing datasets with unnecessary racial, ethnic, and national origin data.
- to responsibly destroy high-risk datasets and backups.
- to implement security and privacy best practices, in particular, for end-to-end encryption to be the default wherever possible.
- to demand appropriate legal process should the government request that we turn over user data collected by our organization, even in small amounts.
If we discover misuse of data that we consider illegal or unethical in our organizations:
- We will work with our colleagues and leaders to correct it.
- If we cannot stop these practices, we will exercise our rights and responsibilities to speak out publicly and engage in responsible whistleblowing without endangering users.
- If we have the authority to do so, we will use all available legal defenses to stop these practices.
- If we do not have such authority, and our organizations force us to engage in such misuse, we will resign from our positions rather than comply.
We will raise awareness and ask critical questions about the responsible and fair use of data and algorithms beyond our organization and our industry….(More)“

21st Century Enlightenment Revisited

Curated on December 13, 2016August 3, 2018 by Stefaan Verhulst

Matthew Taylor at the RSA: “The French historian Tzvetan Todorov describes the three essential ideas of the Enlightenment as ‘autonomy’, ‘universalism’ and ‘humanism’. The ideal of autonomy speaks to every individual’s right to self-determination. Universalism asserts that all human beings equally deserve basic rights and dignity (although, of course, in the 18^th and 19^th century most thinkers restricted this ambition to educated white men). The idea of humanism is that it is up to the people – not Gods or monarchs – through the use of rational inquiry to determine the path to greater human fulfilment….

21^st Century Enlightenment

Take autonomy; too often today we think of freedom either as a shrill demand to be able to turn our backs on wider society or in the narrow possessive terms of consumerism. Yet, brain and behavioural science have confirmed the intuition of philosophers through the ages genuine autonomy is something we only attain when we become aware of our human frailties and understand our truly social nature. Of course, freedom from oppression is the base line, but true autonomy is not a right to be granted but a goal to be pursued through self-awareness and engagement in society.

What of universalism, or social justice as we now tend to think of it? In most parts of the world and certainly in the West there have been incredible advances in equal rights. Discrimination and injustice still exist, but through struggle and reform huge strides have been made in widening the Enlightenment brotherhood of rich white men to women, people of different ethnicity, homosexuals and people with disabilities. Indeed the progress in legal equality over recent decades stands in contrast to the stubborn persistence, and even worsening, of social inequality, particularly based on class.

But the rationalist universalism of human rights needs an emotional corollary. People may be careful not to use the wrong words, but they still harbour resentment and suspicion towards other groups. …

Finally, humanism or the call of progress. The utilitarian philosophy that arose from the Enlightenment spoke to the idea that, free from the religious or autocratic dogma, the best routes to human fulfilment could be identified and should be pursued. The great motors of human progress – markets, science and technology, the modern state – shifted into gear and started to accelerate. Aspects of all these phenomena, indeed of Enlightenment ideas themselves, could be found at earlier stages of human history – what was different was the way they fed off each other and became dominant. Yet, in the process, the idea that these forces could deliver progress often became elided with the assumption that their development was the same as human progress.

Today this danger of letting the engines of progress determine the direction of the human journey feels particularly acute in relation to markets and technology. There is, for example, more discussion of how humans should best adapt to AI and robots than about how technological inquiry might be aligned with human fulfilment. The hollowing out of democratic institutions has diminished the space for public debate about what progress should comprise at just the time when the pace and scale of change makes those debates particularly vital.

A twenty first century enlightenment reinstates true autonomy over narrow ideas of freedom, it asserts a universalism based not just on legal status but on empathy and social connection and reminds us that humanism should lie at the heart of progress.

Think like a system act like an entrepreneur

There is one new strand I want to add to the 2010 account. In the face of many defeats, we must care as much about how we achieve change as about the goals we pursue. At the RSA we talk about ‘thinking like a system and acting like an entrepreneur’, a method which seeks to avoid the narrowness and path dependency of so many unsuccessful models of change. To alter the course our society is now on we need more fully to understand the high barriers to change but then to act more creatively and adaptively when we spot opportunities to take a different path….(More)”

The Government Isn’t Doing Enough to Solve Big Problems with AI

Curated on December 12, 2016August 3, 2018 by Stefaan Verhulst

Mike Orcutt at MIT Technology Review: “The government should play a bigger role in developing new tools based on artificial intelligence, or we could miss out on revolutionary applications because they don’t have obvious commercial upside.

That was the message from prominent AI technologists and researchers at a Senate committee hearing last week. They agreed that AI is in a crucial developmental moment, and that government has a unique opportunity to shape its future. They also said that the government is in a better position than technology companies to invest in AI applications aimed at broad societal problems.

Today just a few companies, led by Google and Facebook, account for the lion’s share of AI R&D in the U.S. But Eric Horvitz, technical fellow and managing director of Microsoft Research, told the committee members that there are important areas that are rich and ripe for AI innovation, such as homelessness and addiction, where the industry isn’t making big investments. The government could help support those pursuits, Horvitz said.

For a more specific example, take the plight of a veteran seeking information online about medical options, says Andrew Moore, dean of the school of computer science at Carnegie Mellon University. If an application that could respond to freeform questions, search multiple government data sets at once, and provide helpful information about a veteran’s health care options were commercially attractive, it might be available already, he says.

There is a “real hunger for basic research” says Greg Brockman, cofounder and chief technology officer of the nonprofit research company OpenAI, because technologists understand that they haven’t made the most important advances yet. If we continue to leave the bulk of it to industry, not only could we miss out on useful applications, but also on the chance to adequately explore urgent scientific questions about ethics, safety, and security while the technology is still young, says Brockman. Since the field of AI is growing “exponentially,” it’s important to study these things now, he says, and the government could make that a “top line thing that they are trying to get done.”….(More)”.

The age of analytics: Competing in a data-driven world

Curated on December 11, 2016August 3, 2018 by Stefaan Verhulst

Updated report by the McKinsey Global Institute: “Back in 2011, the McKinsey Global Institute published a report highlighting the transformational potential of big data. Five years later, we remain convinced that this potential has not been overhyped. In fact, we now believe that our 2011 analyses gave only a partial view. The range of applications and opportunities has grown even larger today. The convergence of several technology trends is accelerating progress. The volume of data continues to double every three years as information pours in from digital platforms, wireless sensors, and billions of mobile phones. Data storage capacity has increased, while its cost has plummeted. Data scientists now have unprecedented computing power at their disposal, and they are devising ever more sophisticated algorithms….

There has been uneven progress in capturing value from data and analytics…

The EU public sector: Our 2011 report analyzed how the European Union’s public sector could use data and analytics to make government services more efficient, reduce fraud and errors in transfer payments, and improve tax collection, potentially achieving some €250 billion worth of annual savings. But only about 10 to 20 percent of this has materialized. Some agencies have moved more interactions online, and many (particularly tax agencies) have introduced pre-filled forms. But across Europe and other advanced economies, adoption and capabilities vary greatly. The complexity of existing systems and the difficulty of attracting scarce analytics talent with public-sector salaries have slowed progress. Despite this, we see even wider potential today for societies to use analytics to make more evidence-based decisions in many aspects of government.

US health care: To date, only 10 to 20 percent of the opportunities we outlined in 2011 have been realized by the US health-care sector. A range of barriers—including a lack of incentives, the difficulty of process and organizational changes, a shortage of technical talent, data-sharing challenges, and regulations—have combined to limit adoption. Within clinical operations, the biggest success has been the shift to electronic medical records, although the vast stores of data they contain have not yet been fully mined. While payers have been slow to capitalize on big data for accounting and pricing, a growing industry now aggregates and synthesizes clinical records, and analytics have taken on new importance in public health surveillance. Many pharmaceutical firms are using analytics in R&D, particularly in streamlining clinical trials. While the health-care sector continues to lag in adoption, there are enormous unrealized opportunities to transform clinical care and deliver personalized medicine… (More)”

Executive Summary (PDF–1MB)

Full Report (PDF–3MB)

Appendix (PDF–533KB)

What does Big Data mean to public affairs research?

Curated on December 10, 2016August 3, 2018 by Stefaan Verhulst

Ines Mergel, R. Karl Rethemeyer, and Kimberley R. Isett at LSE’s The Impact Blog: “…Big Data promises access to vast amounts of real-time information from public and private sources that should allow insights into behavioral preferences, policy options, and methods for public service improvement. In the private sector, marketing preferences can be aligned with customer insights gleaned from Big Data. In the public sector however, government agencies are less responsive and agile in their real-time interactions by design – instead using time for deliberation to respond to broader public goods. The responsiveness Big Data promises is a virtue in the private sector but could be a vice in the public.

Moreover, we raise several important concerns with respect to relying on Big Data as a decision and policymaking tool. While in the abstract Big Data is comprehensive and complete, in practice today’sversion of Big Data has several features that should give public sector practitioners and scholars pause. First, most of what we think of as Big Data is really ‘digital exhaust’ – that is, data collected for purposes other than public sector operations or research. Data sets that might be publicly available from social networking sites such as Facebook or Twitter were designed for purely technical reasons. The degree to which this data lines up conceptually and operationally with public sector questions is purely coincidental. Use of digital exhaust for purposes not previously envisioned can go awry. A good example is Google’s attempt to predict the flu based on search terms.

Second, we believe there are ethical issues that may arise when researchers use data that was created as a byproduct of citizens’ interactions with each other or with a government social media account. Citizens are not able to understand or control how their data is used and have not given consent for storage and re-use of their data. We believe that research institutions need to examine their institutional review board processes to help researchers and their subjects understand important privacy issues that may arise. Too often it is possible to infer individual-level insights about private citizens from a combination of data points and thus predict their behaviors or choices.

Lastly, Big Data can only represent those that spend some part of their life online. Yet we know that certain segments of society opt in to life online (by using social media or network-connected devices), opt out (either knowingly or passively), or lack the resources to participate at all. The demography of the internet matters. For instance, researchers tend to use Twitter data because its API allows data collection for research purposes, but many forget that Twitter users are not representative of the overall population. Instead, as a recent Pew Social Media 2016 update shows, only 24% of all online adults use Twitter. Internet participation generally is biased in terms of age, educational attainment, and income – all of which correlate with gender, race, and ethnicity. We believe therefore that predictive insights are potentially biased toward certain parts of the population, making generalisations highly problematic at this time….(More)”