DATA – Page 419 – The Living Library

UK’s Digital Strategy

Curated on March 6, 2017August 3, 2018 by Stefaan Verhulst

Executive Summary: “This government’s Plan for Britain is a plan to build a stronger, fairer country that works for everyone, not just the privileged few. …Our digital strategy now develops this further, applying the principles outlined in the Industrial Strategy green paper to the digital economy. The UK has a proud history of digital innovation: from the earliest days of computing to the development of the World Wide Web, the UK has been a cradle for inventions which have changed the world. And from Ada Lovelace – widely recognised as the first computer programmer – to the pioneers of today’s revolution in artificial intelligence, the UK has always been at the forefront of invention. …

Maintaining the UK government as a world leader in serving its citizens online

From personalised services in health, to safer care for the elderly at home, to tailored learning in education and access to culture – digital tools, techniques and technologies give us more opportunities than ever before to improve the vital public services on which we all rely.

The UK is already a world leader in digital government,⁷ but we want to go further and faster. The new Government Transformation Strategy published on 9 February 2017 sets out our intention to serve the citizens and businesses of the UK with a better, more coherent experience when using government services online – one that meets the raised expectations set by the many other digital services and tools they use every day. So, we will continue to develop single cross-government platform services, including by working towards 25 million GOV.UK Verify users by 2020 and adopting new services onto the government’s GOV.UK Pay and GOV.UK Notify platforms.

We will build on the ‘Government as a Platform’ concept, ensuring we make greater reuse of platforms and components across government. We will also continue to move towards common technology, ensuring that where it is right we are consuming commodity hardware or cloud-based software instead of building something that is needlessly government specific.

We will also continue to work, across government and the public sector, to harness the potential of digital to radically improve the efficiency of our public services – enabling us to provide a better service to citizens and service users at a lower cost. In education, for example, we will address the barriers faced by schools in regions not connected to appropriate digital infrastructure and we will invest in the Network of Teaching Excellence in Computer Science to help teachers and school leaders build their knowledge and understanding of technology. In transport, we will make our infrastructure smarter, more accessible and more convenient for passengers. At Autumn Statement 2016 we announced that the National Productivity Investment Fund would allocate £450 million from 2018-19 to 2020-21 to trial digital signalling technology on the rail network. And in policing, we will enable police officers to use biometric applications to match fingerprint and DNA from scenes of crime and return results including records and alerts to officers over mobile devices at the crime scene.

Unlocking the power of data in the UK economy and improving public confidence in its use

As part of creating the conditions for sustainable growth, we will take the actions needed to make the UK a world-leading data-driven economy, where data fuels economic and social opportunities for everyone, and where people can trust that their data is being used appropriately.

Data is a global commodity and we need to ensure that our businesses can continue to compete and communicate effectively around the world. To maintain our position at the forefront of the data revolution, we will implement the General Data Protection Regulation by May 2018. This will ensure a shared and higher standard of protection for consumers and their data.

Watchdog to launch inquiry into misuse of data in politics

Curated on March 6, 2017August 3, 2018 by Stefaan Verhulst

Jamie Doward, Carole Cadwalladr and Alice Gibbs in The Guardian: “The UK’s privacy watchdog is launching an inquiry into how voters’ personal data is being captured and exploited in political campaigns, cited as a key factor in both the Brexit and Trump victories last year.

The intervention by the Information Commissioner’s Office (ICO) follows revelations in last week’s Observer that a technology company part-owned by a US billionaire played a key role in the campaign to persuade Britons to vote to leave the European Union.

It comes as privacy campaigners, lawyers, politicians and technology experts express fears that electoral laws are not keeping up with the pace of technological change.

“We are conducting a wide assessment of the data-protection risks arising from the use of data analytics, including for political purposes, and will be contacting a range of organisations,” an ICO spokeswoman confirmed. “We intend to publicise our findings later this year.”

The ICO spokeswoman confirmed that it had approached Cambridge Analytica over its apparent use of data following the story in the Observer. “We have concerns about Cambridge Analytica’s reported use of personal data and we are in contact with the organisation,” she said….

In the US, companies are free to use third-party data without seeking consent. But Gavin Millar QC, of Matrix Chambers, said this was not the case in Europe. “The position in law is exactly the same as when people would go canvassing from door to door,” Millar said. “They have to say who they are, and if you don’t want to talk to them you can shut the door in their face.That’s the same principle behind the data protection act. It’s why if telephone canvassers ring you, they have to say that whole long speech. You have to identify yourself explicitly.”…

Dr Simon Moores, visiting lecturer in the applied sciences and computing department at Canterbury Christ Church University and a technology ambassador under the Blair government, said the ICO’s decision to shine a light on the use of big data in politics was timely.

“A rapid convergence in the data mining, algorithmic and granular analytics capabilities of companies like Cambridge Analytica and Facebook is creating powerful, unregulated and opaque ‘intelligence platforms’. In turn, these can have enormous influence to affect what we learn, how we feel, and how we vote. The algorithms they may produce are frequently hidden from scrutiny and we see only the results of any insights they might choose to publish.” …(More)”

The Whatsapp-inspired, Facebook-investor funded app tackling India’s doctor shortage

Curated on March 4, 2017October 9, 2018 by Stefaan Verhulst

Kylee McIntyre at TechInAsia: “A problem beyond India’s low doctor-to-patient ratio is the distribution of those doctors. Most, particularly specialists, congregate in bigger cities and get seen by patients in the surrounding areas. Only 19 percent of specialists are available in community health centers across India, and most fall well below the country’s requirement for specialists. Community health centers are located in smaller towns and help patients in the area decide if they need to visit a larger, better-equipped city facility….

The IIT-Madras grad’s company, DocsApp, co-founded with fellow IIT-Madras alum Enbasekar D (CTO), joins startups like Practo, DocDoc, and Medinfi in helping patients find physicians. However, the app’s main focus is specialists, and it lets patients chat with doctors and get consultations.

DocsApp’s name is directly inspired by WhatsApp. As long as you have a chat screen on your phone, you can input your problems and location, find a doctor, and ask questions. A user can pay for his or her own appointment over mobile. If treatment requires a physical visit, the user’s money is refunded….

Doctor profiles include the physician’s experience, medical counsel ID, patient reviews, specialty, and languages – DocsApp covers 17 different languages. DocsApp has 1,200 doctors in 15 specialties. All doctors on the platform are verified by looking up certification, an interview, and a facilities review.

If a consultation reveals that a patient needs a prescription, the doctor can provide a digitally-signed e-prescription. DocsApp can deliver medicines within two days to any location in India, says Satish.

Once a user has access to one of the doctors, he or she can message the doctor 24/7 and get a response in 30 minutes – Satish says that the company’s average is now 18 minutes. The team of 55 is aiming for a minute or less….

Telemedicine is one of the ways tech is combatting India’s doctor shortage. Other startups in the industry in the country include Visit, which focuses on both physical and mental health, and SeeDoc, a physician video consultation app.

A chat is a little less personal than a physical visit, which can open the door for patients who want to discuss more taboo topics in India, like mental health and fertility questions. Satish adds that women who live in locations where it’s best to be accompanied by a man when going out also find convenience, as they don’t necessarily need to wait for a husband to come back from work before addressing a medical question she has about her child…(More)”.

Restoring Trust in Expertise

Curated on March 4, 2017July 19, 2019 by Stefaan Verhulst

Minouche Shafik at Project Syndicate: “…public confidence in experts is at a crossroads. With news becoming more narrowly targeted to individual interests and preferences, and with people increasingly choosing whom to trust and follow, the traditional channels for sharing expertise are being disrupted. Who needs experts when you have Facebook, Google, Mumsnet, and Twitter?

Actually, we all do. Over the course of human history, the application of expertise has helped tackle disease, reduce poverty, and improve human welfare. If we are to build on this progress, we need reliable experts to whom the public can confidently turn.

Restoring confidence requires, first, that those describing themselves as “experts” embrace uncertainty. Rather than pretending to be certain and risk frequently getting it wrong, commentators should be candid about uncertainty. Over the long term, such an approach will rebuild credibility. A good example of this is the use of “fan charts” in forecasts produced by the Bank of England’s Monetary Policy Committee (MPC), which show the wide range of possible outcomes for issues such as inflation, growth, and unemployment.

Yet conveying uncertainty increases the complexity of a message. This is a major challenge. It is easy to tweet “BoE forecasts 2% growth.” The fan chart’s true meaning – “If economic circumstances identical to today were to prevail on 100 occasions, the MPC’s best collective judgment is that the mature estimate of GDP growth would lie above 2% on 50 occasions and below 2% on 50 occasions” – doesn’t even fit within Twitter’s 140-character limit.

This underscores the need for sound principles and trustworthy practices to become more widespread as technology changes the way we consume information. Should journalists and bloggers be exposed for reporting or recirculating falsehoods or rumors? Perhaps principles and practices widely used in academia – such as peer review, competitive processes for funding research, transparency about conflicts of interests and financing sources, and requirements to publish underlying data – should be adapted and applied more widely to the world of think tanks, websites, and the media….

Schools and universities will have to do more to educate students to be better consumers of information. Striking research by the Stanford History Education Group, based on tests of thousands of students across the US, described as “bleak” their findings about young people’s ability to evaluate information they encounter online. Fact-checking websites appraising the veracity of claims made by public figures are a step in the right direction, and have some similarities to peer review in academia.

Listening to the other side is crucial. Social media exacerbates the human tendency of groupthink by filtering out opposing views. We must therefore make an effort to engage with opinions that are different from our own and resist algorithmic channeling to avoid difference. Perhaps technology “experts” could code algorithms that burst such bubbles.

Finally, the boundary between technocracy and democracy needs to be managed more carefully. Not surprisingly, when unelected individuals steer decisions that have huge social consequences, public resentment may not be far behind. Problems often arise when experts try to be politicians or politicians try to be experts. Clarity about roles – and accountability when boundaries are breached – is essential.

We need expertise more than ever to solve the world’s problems. The question is not how to manage without experts, but how to ensure that expertise is trustworthy. Getting this right is vital: if the future is not to be shaped by ignorance and narrow-mindedness, we need knowledge and informed debate more than ever before….(More)”.

AI, machine learning and personal data

Curated on March 4, 2017August 20, 2018 by Stefaan Verhulst

Jo Pedder at the Information Commissioner’s Office Blog: “Today sees the publication of the ICO’s updated paper on big data and data protection.

But why now? What’s changed in the two and a half years since we first visited this topic? Well, quite a lot actually:

big data is becoming the norm for many organisations, using it to profile people and inform their decision-making processes, whether that’s to determine your car insurance premium or to accept/reject your job application;

artificial intelligence (AI) is stepping out of the world of science-fiction and into real life, providing the ‘thinking’ power behind virtual personal assistants and smart cars; and

machine learning algorithms are discovering patterns in data that traditional data analysis couldn’t hope to find, helping to detect fraud and diagnose diseases.

The complexity and opacity of these types of processing operations mean that it’s often hard to know what’s going on behind the scenes. This can be problematic when personal data is involved, especially when decisions are made that have significant effects on people’s lives. The combination of these factors has led some to call for new regulation of big data, AI and machine learning, to increase transparency and ensure accountability.

In our view though, whilst the means by which the processing of personal data are changing, the underlying issues remain the same. Are people being treated fairly? Are decisions accurate and free from bias? Is there a legal basis for the processing? These are issues that the ICO has been addressing for many years, through oversight of existing European data protection legislation….(More)”

When the Big Lie Meets Big Data

Curated on March 4, 2017August 3, 2018 by Stefaan Verhulst

Peter Bruce in Scientific America: “…The science of predictive modeling has come a long way since 2004. Statisticians now build “personality” models and tie them into other predictor variables. … One such model bears the acronym “OCEAN,” standing for the personality characteristics (and their opposites) of openness, conscientiousness, extroversion, agreeableness, and neuroticism. Using Big Data at the individual level, machine learning methods might classify a person as, for example, “closed, introverted, neurotic, not agreeable, and conscientious.”

Alexander Nix, CEO of Cambridge Analytica (owned by Trump’s chief donor, Rebekah Mercer), says he has thousands of data points on you, and every other voter: what you buy or borrow, where you live, what you subscribe to, what you post on social media, etc. At a recent Concordia Summit, using the example of gun rights, Nix described how messages will be crafted to appeal specifically to you, based on your personality profile. Are you highly neurotic and conscientious? Nix suggests the image of a sinister gloved hand reaching through a broken window.

In his presentation, Nix noted that the goal is to induce behavior, not communicate ideas. So where does truth fit in? Johan Ugander, Assistant Professor of Management Science at Stanford, suggests that, for Nix and Cambridge Analytica, it doesn’t. In counseling the hypothetical owner of a private beach how to keep people off his property, Nix eschews the merely factual “Private Beach” sign, advocating instead a lie: “Sharks sighted.” Ugander, in his critique, cautions all data scientists against “building tools for unscrupulous targeting.”

The warning is needed, but may be too late. What Nix described in his presentation involved carefully crafted messages aimed at his target personalities. His messages pulled subtly on various psychological strings to manipulate us, and they obeyed no boundary of truth, but they required humans to create them. The next phase will be the gradual replacement of human “craftsmanship” with machine learning algorithms that can supply targeted voters with a steady stream of content (from whatever source, true or false) designed to elicit desired behavior. Cognizant of the Pandora’s box that data scientists have opened, the scholarly journal Big Data has issued a call for papers for a future issue devoted to “Computational Propaganda.”…(More)”

Handbook of Big Data Technologies

Curated on March 4, 2017August 3, 2018 by Stefaan Verhulst

Handbook by Albert Y. Zomaya and Sherif Sakr: “…offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms. Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems. Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques. Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks. Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems. All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains.
Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field….(More)”

Facebook artificial intelligence spots suicidal users

Curated on March 2, 2017August 3, 2018 by Stefaan Verhulst

Leo Kelion at BBC News: “Facebook has begun using artificial intelligence to identify members that may be at risk of killing themselves.

The social network has developed algorithms that spot warning signs in users’ posts and the comments their friends leave in response.

After confirmation by Facebook’s human review team, the company contacts those thought to be at risk of self-harm to suggest ways they can seek help.

A suicide helpline chief said the move was “not just helpful but critical”.

The tool is being tested only in the US at present.

It marks the first use of AI technology to review messages on the network since founder Mark Zuckerberg announced last month that he also hoped to use algorithms to identify posts by terrorists, among other concerning content.

Facebook also announced new ways to tackle suicidal behaviour on its Facebook Live broadcast tool and has partnered with several US mental health organisations to let vulnerable users contact them via its Messenger platform.

Pattern recognition

Facebook has offered advice to users thought to be at risk of suicide for years, but until now it had relied on other users to bring the matter to its attention by clicking on a post’s report button.

It has now developed pattern-recognition algorithms to recognise if someone is struggling, by training them with examples of the posts that have previously been flagged.

Talk of sadness and pain, for example, would be one signal.

Responses from friends with phrases such as “Are you OK?” or “I’m worried about you,” would be another.

Once a post has been identified, it is sent for rapid review to the network’s community operations team.

“We know that speed is critical when things are urgent,” Facebook product manager Vanessa Callison-Burch told the BBC.

The director of the US National Suicide Prevention Lifeline praised the effort, but said he hoped Facebook would eventually do more than give advice, by also contacting those that could help….

The latest effort to help Facebook Live users follows the death of a 14-year-old-girl in Miami, who livestreamed her suicide on the platform in January.

However, the company said it had already begun work on its new tools before the tragedy.

The goal is to help at-risk users while they are broadcasting, rather than wait until their completed video has been reviewed some time later….(More)”.

Fighting Illegal Fishing With Big Data

Curated on March 1, 2017August 3, 2018 by Stefaan Verhulst

Emily Matchar in Smithsonian: “In many ways, the ocean is the Wild West. The distances are vast, the law enforcement agents few and far between, and the legal jurisdiction often unclear. In this environment, illegal activity flourishes. Illegal fishing is so common that experts estimate as much as a third of fish sold in the U.S. was fished illegally. This illegal fishing decimates the ocean’s already dwindling fish populations and gives rise to modern slavery, where fishermen are tricked onto vessels and forced to work, sometimes for years.

A new use of data technology aims to help curb these abuses by shining a light on the high seas. The technology uses ships’ satellite signals to detect instances of transshipment, when two vessels meet at sea to exchange cargo. As transshipment is a major way illegally caught fish makes it into the legal supply chain, tracking it could potentially help stop the practice.

“[Transshipment] really allows people to do something out of sight,” says David Kroodsma, the research program director at Global Fishing Watch, an online data platform launched by Google in partnership with the nonprofits Oceana and SkyTruth. “It’s something that obscures supply chains. It’s basically being able to do things without any oversight. And that’s a problem when you’re using a shared resource like the oceans.”

Global Fishing Watch analyzed some 21 billion satellite signals broadcast by ships, which are required to carry transceivers for collision avoidance, from between 2012 and 2016. It then used an artificial intelligence system it created to identify which ships were refrigerated cargo vessels (known in the industry as “reefers”). They then verified this information with fishery registries and other sources, eventually identifying 794 reefers—90 percent of the world’s total number of such vessels. They tracked instances where a reefer and a fishing vessel were moving at similar speeds in close proximity, labeling these instances as “likely transshipments,” and also traced instances where reefers were traveling in a way that indicated a rendezvous with a fishing vessel, even if no fishing vessel was present—fishing vessels often turn off their satellite systems when they don’t want to be seen. All in all there were more than 90,000 likely or potential transshipments recorded.

Even if these encounters were in fact transshipments, they would not all have been for nefarious purposes. They may have taken place to refuel or load up on supplies. But looking at the patterns of where the potential transshipments happen is revealing. Very few are seen close to the coasts of the U.S., Canada and much of Europe, all places with tight fishery regulations. There are hotspots off the coast of Peru and Argentina, all over Africa, and off the coast of Russia. Some 40 percent of encounters happen in international waters, far enough off the coast that no country has jurisdiction.

The tracked reefers were flying flags from some 40 different countries. But that doesn’t necessarily tell us much about where they really come from. Nearly half of the reefers tracked were flying “flags of convenience,” meaning they’re registered in countries other than where the ship’s owners are from to take advantage of those countries’ lax regulations….(More)”

Read more: http://www.smithsonianmag.com/innovation/fighting-illegal-fishing-big-data-180962321/#7eCwGrGS5v5gWjFz.99
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