Sector: business

For people, by people

Curated on Posted on by Stefaan Verhulst

Geeta Padmanabhan at the Hindu: “Ippodhu, a mobile app, is all about crowd-sourced civic participation for good governance…Last week, a passer-by noticed how the large hoardings outside Vivekanandar Illam, facing Marina Beach, blocked the view of the iconic building. Enraged, he whipped out his smartphone, logged on to Ippodhu and wrote: “How is this allowed? The banners are in the walking space and we can’t see the historic building!” Ippodhu.com carried the story with pictures.

“On Ippodhu, a community information mobile application, the person complaining has the option to do more,” says Peer Mohamed, the team leader of the app/website. “He could have registered a complaint with the police, the Corporation or a relevant NGO, using the ‘Act’ option. This facility makes Ippodhu a valuable tool for beleaguered citizens to complain and puts it above other social media avenues.”

Users can choose between Tamil and English, and read the latest posts just as they would in a Twitter feed. While posting, your location is geo-tagged automatically; if you find that intrusive, you can post anonymously. There is no word limit and one can enlarge the font, write an essay, a note or a rant and post it under one of 15 categories. I decided to check out the app and created an account. My post went live in less than a minute. Then I moved to Ippodhu’s USP. I clicked‘Act’, chose ‘civic issue’ as the category, and posted a note about flooding in my locality. “It’s on Apple and Android as just text now, but expect picture and video features soon when the circulation hits the target,” says Peer. “My team of 12 journalists curates the feeds 24/7, allowing no commercials, ads or abusive language. We want to keep it non-controversial and people-friendly.” It’s crowd-sourced citizen journalism and civic participation for good governance….(More)”

Decoding the Future for National Security

Curated on Posted on by Stefaan Verhulst

George I. Seffers at Signal: “U.S. intelligence agencies are in the business of predicting the future, but no one has systematically evaluated the accuracy of those predictions—until now. The intelligence community’s cutting-edge research and development agency uses a handful of predictive analytics programs to measure and improve the ability to forecast major events, including political upheavals, disease outbreaks, insider threats and cyber attacks.

The Office for Anticipating Surprise at the Intelligence Advanced Research Projects Activity (IARPA) is a place where crystal balls come in the form of software, tournaments and throngs of people. The office sponsors eight programs designed to improve predictive analytics, which uses a variety of data to forecast events. The programs all focus on incidents outside of the United States, and the information is anonymized to protect privacy. The programs are in different stages, some having recently ended as others are preparing to award contracts.

But they all have one more thing in common: They use tournaments to advance the state of the predictive analytic arts. “We decided to run a series of forecasting tournaments in which people from around the world generate forecasts about, now, thousands of real-world events,” says Jason Matheny, IARPA’s new director. “All of our programs on predictive analytics do use this tournament style of funding and evaluating research.” The Open Source Indicators program used a crowdsourcing technique in which people across the globe offered their predictions on such events as political uprisings, disease outbreaks and elections.

The data analyzed included social media trends, Web search queries and even cancelled dinner reservations—an indication that people are sick. “The methods applied to this were all automated. They used machine learning to comb through billions of pieces of data to look for that signal, that leading indicator, that an event was about to happen,” Matheny explains. “And they made amazing progress. They were able to predict disease outbreaks weeks earlier than traditional reporting.” The recently completed Aggregative Contingent Estimation (ACE) program also used a crowdsourcing competition in which people predicted events, including whether weapons would be tested, treaties would be signed or armed conflict would break out along certain borders. Volunteers were asked to provide information about their own background and what sources they used. IARPA also tested participants’ cognitive reasoning abilities. Volunteers provided their forecasts every day, and IARPA personnel kept score. Interestingly, they discovered the “deep domain” experts were not the best at predicting events. Instead, people with a certain style of thinking came out the winners. “They read a lot, not just from one source, but from multiple sources that come from different viewpoints. They have different sources of data, and they revise their judgments when presented with new information. They don’t stick to their guns,” Matheny reveals. …

The ACE research also contributed to a recently released book, Superforecasting: The Art and Science of Prediction, according to the IARPA director. The book was co-authored, along with Dan Gardner, by Philip Tetlock, the Annenberg University professor of psychology and management at the University of Pennsylvania who also served as a principal investigator for the ACE program. Like ACE, the Crowdsourcing Evidence, Argumentation, Thinking and Evaluation program uses the forecasting tournament format, but it also requires participants to explain and defend their reasoning. The initiative aims to improve analytic thinking by combining structured reasoning techniques with crowdsourcing.

Meanwhile, the Foresight and Understanding from Scientific Exposition (FUSE) program forecasts science and technology breakthroughs….(More)”

Meeting the Challenges of Big Data

Curated on Posted on by Stefaan Verhulst

Opinion by the European Data Protection Supervisor: “Big data, if done responsibly, can deliver significant benefits and efficiencies for society and individuals not only in health, scientific research, the environment and other specific areas. But there are serious concerns with the actual and potential impact of processing of huge amounts of data on the rights and freedoms of individuals, including their right to privacy. The challenges and risks of big data therefore call for more effective data protection.

Technology should not dictate our values and rights, but neither should promoting innovation and preserving fundamental rights be perceived as incompatible. New business models exploiting new capabilities for the massive collection, instantaneous transmission, combination and reuse of personal information for unforeseen purposes have placed the principles of data protection under new strains, which calls for thorough consideration on how they are applied.

European data protection law has been developed to protect our fundamental rights and values, including our right to privacy. The question is not whether to apply data protection law to big data, but rather how to apply it innovatively in new environments. Our current data protection principles, including transparency, proportionality and purpose limitation, provide the base line we will need to protect more dynamically our fundamental rights in the world of big data. They must, however, be complemented by ‘new’ principles which have developed over the years such as accountability and privacy by design and by default. The EU data protection reform package is expected to strengthen and modernise the regulatory framework .

The EU intends to maximise growth and competitiveness by exploiting big data. But the Digital Single Market cannot uncritically import the data-driven technologies and business models which have become economic mainstream in other areas of the world. Instead it needs to show leadership in developing accountable personal data processing. The internet has evolved in a way that surveillance – tracking people’s behaviour – is considered as the indispensable revenue model for some of the most successful companies. This development calls for critical assessment and search for other options.

In any event, and irrespective of the business models chosen, organisations that process large volumes of personal information must comply with applicable data protection law. The European Data Protection Supervisor (EDPS) believes that responsible and sustainable development of big data must rely on four essential elements:

  • organisations must be much more transparent about how they process personal data;
  • afford users a higher degree of control over how their data is used;
  • design user friendly data protection into their products and services; and;
  • become more accountable for what they do….(More)

E-Gov’s Untapped Potential for Cutting the Public Workforce

Curated on Posted on by Stefaan Verhulst

Robert D. Atkinson at Governing: “Since the flourishing of the Internet in the mid-1990s, e-government advocates have promised that information technology not only would make it easier to access public services but also would significantly increase government productivity and lower costs. Compared to the private sector, however, this promise has remained largely unfulfilled, in part because of a resistance to employing technology to replace government workers.

It’s not surprising, then, that state budget directors and budget committees usually look at IT as a cost rather than as a strategic investment that can produce a positive financial return for taxpayers. Until governments make a strong commitment to using IT to increase productivity — including as a means of workforce reduction — it will remain difficult to bring government into the 21st-century digital economy.

The benefits can be sizeable. My organization, the Information Technology and Innovation Foundation, estimates that if states focus on using IT to drive productivity, they stand to save more than $11 billion over the next five years. States can achieve these productivity gains in two primary ways:

First, they can use e-government to substitute for person-to-person interactions. For example, by moving just nine state services online — from one-stop business registration to online vehicle-license registration — Utah reduced the need for government employees to interact with citizens, saving an average of $13 per transaction.

And second, they can use IT to optimize performance and cut costs. In 2013, for example, Pennsylvania launched a mobile app to streamline the inspection process for roads and bridges, reducing the time it took for manual data entry. Inspectors saved about 15 minutes per survey, which added up to a savings of over $550,000 in 2013.

So if technology can cut costs, why has e-government not lived up to its original promise? One key reason is that most state governments have focused first and foremost on using IT to improve service quality and access rather than to increase productivity. In part, this is because boosting productivity involves reducing headcount, and state chief information officers and other policymakers often are unwilling to openly advocate for using technology in this way for fear that it will generate opposition from government workers and their unions. This is why replacing labor with modern IT tools has long been the third rail for the public-sector IT community.

This is not necessarily the case in some other nations that have moved to aggressively deploy IT to reduce headcount. The first goal of the Danish Agency for Digitisation’s strategic plan is “a productive and efficient public sector.” To get there, the agency plans to focus on automation of public administrative procedures. Denmark even introduced a rule in which all communications with government need to be done electronically, eliminating telephone receptionists at municipal offices. Likewise, the United Kingdom’s e-government strategy set a goal of increasing productivity by 2.5 percent, including through headcount cuts.

Another reason e-government has not lived up to its full promise is that many state IT systems are woefully out of date, especially compared to the systems the corporate sector uses. But if CIOs and other advocates of modern digital government are going to be able to make their case effectively for resources to bring their technology into the 21st century, they will need to make a more convincing bottom-line case to appropriators. This argument should be about saving money, including through workforce reduction.

Policymakers should base this case not just on savings for government but also for the state’s businesses and citizens….(More)”

Questioning Smart Urbanism: Is Data-Driven Governance a Panacea?

Curated on Posted on by Stefaan Verhulst

Alice Tang at the Chicago Policy Review: “In the era of data explosion, urban planners are increasingly relying on real-time, streaming data generated by “smart” devices to assist with city management. “Smart cities,” referring to cities that implement pervasive and ubiquitous computing in urban planning, are widely discussed in academia, business, and government. These cities are characterized not only by their use of technology but also by their innovation-driven economies and collaborative, data-driven city governance. Smart urbanism can seem like an effective strategy to create more efficient, sustainable, productive, and open cities. However, there are emerging concerns about the potential risks in the long-term development of smart cities, including political neutrality of big data, technocratic governance, technological lock-ins, data and network security, and privacy risks.

In a study entitled, “The Real-Time City? Big Data and Smart Urbanism,” Rob Kitchin provides a critical reflection on the potential negative effects of data-driven city governance on social development—a topic he claims deserves greater governmental, academic, and social attention.

In contrast to traditional datasets that rely on samples or are aggregated to a coarse scale, “big data” is huge in volume, high in velocity, and diverse in variety. Since the early 2000s, there has been explosive growth in data volume due to the rapid development and implementation of technology infrastructure, including networks, information management, and data storage. Big data can be generated from directed, automated, and volunteered sources. Automated data generation is of particular interest to urban planners. One example Kitchin cites is urban sensor networks, which allow city governments to monitor the movements and statuses of individuals, materials, and structures throughout the urban environment by analyzing real-time data.

With the huge amount of streaming data collected by smart infrastructure, many city governments use real-time analysis to manage different aspects of city operations. There has been a recent trend in centralizing data streams into a single hub, integrating all kinds of surveillance and analytics. These one-stop data centers make it easier for analysts to cross-reference data, spot patterns, identify problems, and allocate resources. The data are also often accessible by field workers via operations platforms. In London and some other cities, real-time data are visualized on “city dashboards” and communicated to citizens, providing convenient access to city information.

However, the real-time city is not a flawless solution to all the problems faced by city managers. The primary concern is the politics of big, urban data. Although raw data are often perceived as neutral and objective, no data are free of bias; the collection of data is a subjective process that can be shaped by various confounding factors. The presentation of data can also be manipulated to answer a specific question or enact a particular political vision….(More)”

Build digital democracy

Curated on Posted on by Stefaan Verhulst

Dirk Helbing & Evangelos Pournaras in Nature: “Fridges, coffee machines, toothbrushes, phones and smart devices are all now equipped with communicating sensors. In ten years, 150 billion ‘things’ will connect with each other and with billions of people. The ‘Internet of Things’ will generate data volumes that double every 12 hours rather than every 12 months, as is the case now.

Blinded by information, we need ‘digital sunglasses’. Whoever builds the filters to monetize this information determines what we see — Google and Facebook, for example. Many choices that people consider their own are already determined by algorithms. Such remote control weakens responsible, self-determined decision-making and thus society too.

The European Court of Justice’s ruling on 6 October that countries and companies must comply with European data-protection laws when transferring data outside the European Union demonstrates that a new digital paradigm is overdue. To ensure that no government, company or person with sole control of digital filters can manipulate our decisions, we need information systems that are transparent, trustworthy and user-controlled. Each of us must be able to choose, modify and build our own tools for winnowing information.

With this in mind, our research team at the Swiss Federal Institute of Technology in Zurich (ETH Zurich), alongside international partners, has started to create a distributed, privacy-preserving ‘digital nervous system’ called Nervousnet. Nervousnet uses the sensor networks that make up the Internet of Things, including those in smartphones, to measure the world around us and to build a collective ‘data commons’. The many challenges ahead will be best solved using an open, participatory platform, an approach that has proved successful for projects such as Wikipedia and the open-source operating system Linux.

A wise king?

The science of human decision-making is far from understood. Yet our habits, routines and social interactions are surprisingly predictable. Our behaviour is increasingly steered by personalized advertisements and search results, recommendation systems and emotion-tracking technologies. Thousands of pieces of metadata have been collected about every one of us (seego.nature.com/stoqsu). Companies and governments can increasingly manipulate our decisions, behaviour and feelings1.

Many policymakers believe that personal data may be used to ‘nudge’ people to make healthier and environmentally friendly decisions. Yet the same technology may also promote nationalism, fuel hate against minorities or skew election outcomes2 if ethical scrutiny, transparency and democratic control are lacking — as they are in most private companies and institutions that use ‘big data’. The combination of nudging with big data about everyone’s behaviour, feelings and interests (‘big nudging’, if you will) could eventually create close to totalitarian power.

Countries have long experimented with using data to run their societies. In the 1970s, Chilean President Salvador Allende created computer networks to optimize industrial productivity3. Today, Singapore considers itself a data-driven ‘social laboratory’4 and other countries seem keen to copy this model.

The Chinese government has begun rating the behaviour of its citizens5. Loans, jobs and travel visas will depend on an individual’s ‘citizen score’, their web history and political opinion. Meanwhile, Baidu — the Chinese equivalent of Google — is joining forces with the military for the ‘China brain project’, using ‘deep learning’ artificial-intelligence algorithms to predict the behaviour of people on the basis of their Internet activity6.

The intentions may be good: it is hoped that big data can improve governance by overcoming irrationality and partisan interests. But the situation also evokes the warning of the eighteenth-century philosopher Immanuel Kant, that the “sovereign acting … to make the people happy according to his notions … becomes a despot”. It is for this reason that the US Declaration of Independence emphasizes the pursuit of happiness of individuals.

Ruling like a ‘benevolent dictator’ or ‘wise king’ cannot work because there is no way to determine a single metric or goal that a leader should maximize. Should it be gross domestic product per capita or sustainability, power or peace, average life span or happiness, or something else?

Better is pluralism. It hedges risks, promotes innovation, collective intelligence and well-being. Approaching complex problems from varied perspectives also helps people to cope with rare and extreme events that are costly for society — such as natural disasters, blackouts or financial meltdowns.

Centralized, top-down control of data has various flaws. First, it will inevitably become corrupted or hacked by extremists or criminals. Second, owing to limitations in data-transmission rates and processing power, top-down solutions often fail to address local needs. Third, manipulating the search for information and intervening in individual choices undermines ‘collective intelligence’7. Fourth, personalized information creates ‘filter bubbles’8. People are exposed less to other opinions, which can increase polarization and conflict9.

Fifth, reducing pluralism is as bad as losing biodiversity, because our economies and societies are like ecosystems with millions of interdependencies. Historically, a reduction in diversity has often led to political instability, collapse or war. Finally, by altering the cultural cues that guide peoples’ decisions, everyday decision-making is disrupted, which undermines rather than bolsters social stability and order.

Big data should be used to solve the world’s problems, not for illegitimate manipulation. But the assumption that ‘more data equals more knowledge, power and success’ does not hold. Although we have never had so much information, we face ever more global threats, including climate change, unstable peace and socio-economic fragility, and political satisfaction is low worldwide. About 50% of today’s jobs will be lost in the next two decades as computers and robots take over tasks. But will we see the macroeconomic benefits that would justify such large-scale ‘creative destruction’? And how can we reinvent half of our economy?

The digital revolution will mainly benefit countries that achieve a ‘win–win–win’ situation for business, politics and citizens alike10. To mobilize the ideas, skills and resources of all, we must build information systems capable of bringing diverse knowledge and ideas together. Online deliberation platforms and reconfigurable networks of smart human minds and artificially intelligent systems can now be used to produce collective intelligence that can cope with the diverse and complex challenges surrounding us….(More)” See Nervousnet project

The Power of Nudges, for Good and Bad

Curated on Posted on by Stefaan Verhulst

Richard H. Thaler in the New York Times: “Nudges, small design changes that can markedly affect individual behavior, have been catching on. These techniques rely on insights from behavioral science, and when used ethically, they can be very helpful. But we need to be sure that they aren’t being employed to sway people to make bad decisions that they will later regret.

Whenever I’m asked to autograph a copy of “Nudge,” the book I wrote with Cass Sunstein, the Harvard law professor, I sign it, “Nudge for good.” Unfortunately, that is meant as a plea, not an expectation.

Three principles should guide the use of nudges:

■ All nudging should be transparent and never misleading.

■ It should be as easy as possible to opt out of the nudge, preferably with as little as one mouse click.

■ There should be good reason to believe that the behavior being encouraged will improve the welfare of those being nudged.
As far as I know, the government teams in Britain and the United States that have focused on nudging have followed these guidelines scrupulously. But the private sector is another matter. In this domain, I see much more troubling behavior.

For example, last spring I received an email telling me that the first prominent review of a new book of mine had appeared: It was in The Times of London. Eager to read the review, I clicked on a hyperlink, only to run into a pay wall. Still, I was tempted by an offer to take out a one-month trial subscription for the price of just £1. As both a consumer and producer of newspaper articles, I have no beef with pay walls. But before signing up, I read the fine print. As expected, I would have to provide credit card information and would be automatically enrolled as a subscriber when the trial period expired. The subscription rate would then be £26 (about $40) a month. That wasn’t a concern because I did not intend to become a paying subscriber. I just wanted to read that one article.

But the details turned me off. To cancel, I had to give 15 days’ notice, so the one-month trial offer actually was good for just two weeks. What’s more, I would have to call London, during British business hours, and not on a toll-free number. That was both annoying and worrying. As an absent-minded American professor, I figured there was a good chance I would end up subscribing for several months, and that reading the article would end up costing me at least £100….

These examples are not unusual. Many companies are nudging purely for their own profit and not in customers’ best interests. In a recent column in The New York Times, Robert Shiller called such behavior “phishing.” Mr. Shiller and George Akerlof, both Nobel-winning economists, have written a book on the subject, “Phishing for Phools.”

Some argue that phishing — or evil nudging — is more dangerous in government than in the private sector. The argument is that government is a monopoly with coercive power, while we have more choice in the private sector over which newspapers we read and which airlines we fly.

I think this distinction is overstated. In a democracy, if a government creates bad policies, it can be voted out of office. Competition in the private sector, however, can easily work to encourage phishing rather than stifle it.

One example is the mortgage industry in the early 2000s. Borrowers were encouraged to take out loans that they could not repay when real estate prices fell. Competition did not eliminate this practice, because it was hard for anyone to make money selling the advice “Don’t take that loan.”

As customers, we can help one another by resisting these come-ons. The more we turn down questionable offers like trip insurance and scrutinize “one month” trials, the less incentive companies will have to use such schemes. Conversely, if customers reward firms that act in our best interests, more such outfits will survive and flourish, and the options available to us will improve….(More)

2015 Digital Cities: Winners Experiment with Forward-Thinking Tech Projects

Curated on Posted on by Stefaan Verhulst

List of winners at Govtech:

1st place // City of Philadelphia, Pa.

A savvy mix of data-driven citizen engagement, tech modernization and outside-the-box thinking powered Philadelphia to its first-place ranking. A new city websitelaunched last year is designed to provide new levels of user convenience. For instance, three navigation options are squeezed into the top of the site — a search bar, a list of common actions like “report a problem” or “pay a bill,” and a menu of city functions arranged topically — giving citizens multiple ways to find what they need. The site was created using agile principles, launching as a work in progress in December and shaped by user feedback. The city also is broadening its use of open data as a citizen-engagement tool. A new generation of civic apps relies on open data sets to give residents easy access to property tax calculations, property ownership information anddetailed maps of various city resources. These improvements in customer-facing services have been facilitated by upgrades to back-end systems that are improving reliability and reducing staff support requirements. The city estimates that half of its IT systems now are procured as a service. Finally, an interesting pilot involving the city IT department and a local middle school is aimed at drawing more kids into STEM-related careers. Students met weekly in the city Innovation Lab for a series of hands-on experiences led by members of the Philadelphia Office of Information Technology.

2nd place // City of Los Angeles, Calif.

Second-ranked Los Angeles is developing a new model for funding innovative ideas, leveraging private-sector platforms to improve services, streamlining internal processes and closing the broadband gap. The city established a $1 million innovation fund late last year to seed pilot projects generated by city employees’ suggestions. More than a dozen projects have been launched so far. Through open APIs, the city trades traffic information with Google’s Waze traffic app. The app consumes city traffic data to warn drivers about closed roads, hazards and dangerous intersections, while the city transportation department uses information submitted by Waze users to identify potholes, illegal road construction and traffic patterns. MyPayLA, launched by the LA Controller’s Office and the city Information Technology Agency, is a mobile app that lets city employees view their payroll information on a mobile device. And theCityLinkLA broadband initiative is designed to attract broadband providers to the city with expedited permitting and access to existing assets like streetlights, real estate and fiber.

2nd place // City of Louisville, Ky.

Louisville’s mobile-friendly Web portal garnered the city a second-place finish in the Center for Digital Government’s Best of the Web awards earlier this year. Now, Louisville has a No. 2 ranking in the 2015 Digital Cities Survey to add to its trophy case. Besides running an excellent website — built on the open source Drupal platform and hosted in the cloud — Louisville is equipping its entire police force with body-worn cameras and expects to be finished by the end of 2015. Video from 1,000 officers, as well as footage from Metro Watch cameras placed around the city, will be stored in the cloud. Louisville’s Metro Police Department, one of 21 cities involved in the White House Police Data Initiative, also became one of the first in the nation to release data sets on assaulted officers, arrests and citations, and hate crimes on the city’s open data portal. In addition, a public-private partnership called Code Louisville offers free technology training to local residents. More than 500 people have taken 12-week classes to learn Web or mobile development skills.

3rd place // City of Kansas City, Mo.

Kansas City’s Art of Data initiative may be one of the nation’s most creative attempts to engage citizens through open data. The city selected 10 local artists earlier this year to turn information from its open data site into visual art. The artists pulled information from 10 different data sets, ranging from life expectancy by ZIP code to citizen satisfaction with the safety of their neighborhoods. The exhibit drew a large crowd when it opened in June, according to the city, and more than 3,000 residents eventually viewed the works of art. Kansas City also was chosen to participate in a new HUD digital inclusion program called ConnectHome, which will offer broadband access, training, digital literacy programs and devices for residents in assisted housing units. And the city is working with a local startup business, RFP365, to simplify its RFP process. Through a pilot partnership, Kansas City will use the RFP365 platform — which lets buyers track and receive bids from vendors and suppliers — to make the government purchasing process easier and more transparent.

3rd place // City of Phoenix, Ariz.

The development of a new citywide transportation plan in Phoenix offers a great example of how to use digital engagement tools. Using the MindMixer platform, the city developed a website to let citizens suggest ideas for new transit services and street infrastructure, as well as discuss a range of transportation-related issues. Using polling, mapping, open-ended questions and discussion prompts, residents directly helped to develop the plan. The engagement process reached more 3,700 residents and generated hundreds of comments online. In addition, a city-led technology summit held late last year brought together big companies, small businesses and citizens to discuss how technology could improve city operations and boost economic development. And new court technology lets attorneys receive hearing notifications on a mobile device and enables Web and interactive voice response (IVR) payments for a variety of cases.

…(More)”

Politics and the New Machine

Curated on Posted on by Stefaan Verhulst

Jill Lepore in the NewYorker on “What the turn from polls to data science means for democracy”: “…The modern public-opinion poll has been around since the Great Depression, when the response rate—the number of people who take a survey as a percentage of those who were asked—was more than ninety. The participation rate—the number of people who take a survey as a percentage of the population—is far lower. Election pollsters sample only a minuscule portion of the electorate, not uncommonly something on the order of a couple of thousand people out of the more than two hundred million Americans who are eligible to vote. The promise of this work is that the sample is exquisitely representative. But the lower the response rate the harder and more expensive it becomes to realize that promise, which requires both calling many more people and trying to correct for “non-response bias” by giving greater weight to the answers of people from demographic groups that are less likely to respond. Pollster.com’s Mark Blumenthal has recalled how, in the nineteen-eighties, when the response rate at the firm where he was working had fallen to about sixty per cent, people in his office said, “What will happen when it’s only twenty? We won’t be able to be in business!” A typical response rate is now in the single digits.

Meanwhile, polls are wielding greater influence over American elections than ever….

Still, data science can’t solve the biggest problem with polling, because that problem is neither methodological nor technological. It’s political. Pollsters rose to prominence by claiming that measuring public opinion is good for democracy. But what if it’s bad?

A “poll” used to mean the top of your head. Ophelia says of Polonius, “His beard as white as snow: All flaxen was his poll.” When voting involved assembling (all in favor of Smith stand here, all in favor of Jones over there), counting votes required counting heads; that is, counting polls. Eventually, a “poll” came to mean the count itself. By the nineteenth century, to vote was to go “to the polls,” where, more and more, voting was done on paper. Ballots were often printed in newspapers: you’d cut one out and bring it with you. With the turn to the secret ballot, beginning in the eighteen-eighties, the government began supplying the ballots, but newspapers kept printing them; they’d use them to conduct their own polls, called “straw polls.” Before the election, you’d cut out your ballot and mail it to the newspaper, which would make a prediction. Political parties conducted straw polls, too. That’s one of the ways the political machine worked….

Ever since Gallup, two things have been called polls: surveys of opinions and forecasts of election results. (Plenty of other surveys, of course, don’t measure opinions but instead concern status and behavior: Do you own a house? Have you seen a doctor in the past month?) It’s not a bad idea to reserve the term “polls” for the kind meant to produce election forecasts. When Gallup started out, he was skeptical about using a survey to forecast an election: “Such a test is by no means perfect, because a preelection survey must not only measure public opinion in respect to candidates but must also predict just what groups of people will actually take the trouble to cast their ballots.” Also, he didn’t think that predicting elections constituted a public good: “While such forecasts provide an interesting and legitimate activity, they probably serve no great social purpose.” Then why do it? Gallup conducted polls only to prove the accuracy of his surveys, there being no other way to demonstrate it. The polls themselves, he thought, were pointless…

If public-opinion polling is the child of a strained marriage between the press and the academy, data science is the child of a rocky marriage between the academy and Silicon Valley. The term “data science” was coined in 1960, one year after the Democratic National Committee hired Simulmatics Corporation, a company founded by Ithiel de Sola Pool, a political scientist from M.I.T., to provide strategic analysis in advance of the upcoming Presidential election. Pool and his team collected punch cards from pollsters who had archived more than sixty polls from the elections of 1952, 1954, 1956, 1958, and 1960, representing more than a hundred thousand interviews, and fed them into a UNIVAC. They then sorted voters into four hundred and eighty possible types (for example, “Eastern, metropolitan, lower-income, white, Catholic, female Democrat”) and sorted issues into fifty-two clusters (for example, foreign aid). Simulmatics’ first task, completed just before the Democratic National Convention, was a study of “the Negro vote in the North.” Its report, which is thought to have influenced the civil-rights paragraphs added to the Party’s platform, concluded that between 1954 and 1956 “a small but significant shift to the Republicans occurred among Northern Negroes, which cost the Democrats about 1 per cent of the total votes in 8 key states.” After the nominating convention, the D.N.C. commissioned Simulmatics to prepare three more reports, including one that involved running simulations about different ways in which Kennedy might discuss his Catholicism….

Data science may well turn out to be as flawed as public-opinion polling. But a stage in the development of any new tool is to imagine that you’ve perfected it, in order to ponder its consequences. I asked Hilton to suppose that there existed a flawless tool for measuring public opinion, accurately and instantly, a tool available to voters and politicians alike. Imagine that you’re a member of Congress, I said, and you’re about to head into the House to vote on an act—let’s call it the Smeadwell-Nutley Act. As you do, you use an app called iThePublic to learn the opinions of your constituents. You oppose Smeadwell-Nutley; your constituents are seventy-nine per cent in favor of it. Your constituents will instantly know how you’ve voted, and many have set up an account with Crowdpac to make automatic campaign donations. If you vote against the proposed legislation, your constituents will stop giving money to your reëlection campaign. If, contrary to your convictions but in line with your iThePublic, you vote for Smeadwell-Nutley, would that be democracy? …(More)”

 

How Satellite Data and Artificial Intelligence could help us understand poverty better

Curated on Posted on by Stefaan Verhulst

Maya Craig at Fast Company: “Governments and development organizations currently measure poverty levels by conducting door-to-door surveys. The new partnership will test the use of AI to supplement these surveys and increase the accuracy of poverty data. Orbital said its AI software will analyze satellite images to see if characteristics such as building height and rooftop material can effectively indicate wealth.

The pilot study will be conducted in Sri Lanka. If successful, the World Bank hopes to scale it worldwide. A recent study conducted by the organization found that more than 50 countries lack legitimate poverty estimates, which limits the ability of the development community to support the world’s poorest populations.

“Data depravation is a serious issue, especially in many of the countries where we need it most,” says David Newhouse, senior economist at the World Bank. “This technology has the potential to help us get that data more frequently and at a finer level of detail than is currently possible.”

The announcement is the latest in an emerging industry of AI analysis of satellite photos. A growing number of investors and entrepreneurs are betting that the convergence of these fields will have far-reaching impacts on business, policy, resource management and disaster response.

Wall Street’s biggest hedge-fund businesses have begun using the technology to improve investment strategies. The Pew Charitable Trust employs the method to monitor oceans for illegal fishing activities. And startups like San Francisco-based Mavrx use similar analytics to optimize crop harvest.

The commercial earth-imaging satellite market, valued at $2.7 billion in 2014, is predicted to grow by 14% each year through the decade, according to a recent report.

As recently as two years ago, there were just four commercial earth imaging satellites operated in the U.S., and government contracts accounted for about 70% of imagery sales. By 2020, there will be hundreds of private-sector “smallsats” in orbit capturing imagery that will be easily accessible online. Companies like Skybox Imaging and Planet Labs have the first of these smallsats already active, with plans for more.

The images generated by these companies will be among the world’s largest data sets. And recent breakthroughs in AI research have made it possible to analyze these images to inform decision-making…(More)”

Proudly powered by WordPress