Your Data Is Crucial to a Robotic Age. Shouldn’t You Be Paid for It?

Curated on Posted on by Stefaan Verhulst

Eduardo Porter at The New York Times: “The idea has been around for a bit. Jaron Lanier, the tech philosopher and virtual-reality pioneer who now works for Microsoft Research, proposed it in his 2013 book, “Who Owns the Future?,” as a needed corrective to an online economy mostly financed by advertisers’ covert manipulation of users’ consumer choices.

It is being picked up in “Radical Markets,” a book due out shortly from Eric A. Posner of the University of Chicago Law School and E. Glen Weyl, principal researcher at Microsoft. And it is playing into European efforts to collect tax revenue from American internet giants.

In a report obtained last month by Politico, the European Commission proposes to impose a tax on the revenue of digital companies based on their users’ location, on the grounds that “a significant part of the value of a business is created where the users are based and data is collected and processed.”

Users’ data is a valuable commodity. Facebook offers advertisers precisely targeted audiences based on user profiles. YouTube, too, uses users’ preferences to tailor its feed. Still, this pales in comparison with how valuable data is about to become, as the footprint of artificial intelligence extends across the economy.

Data is the crucial ingredient of the A.I. revolution. Training systems to perform even relatively straightforward tasks like voice translation, voice transcription or image recognition requires vast amounts of data — like tagged photos, to identify their content, or recordings with transcriptions.

“Among leading A.I. teams, many can likely replicate others’ software in, at most, one to two years,” notes the technologist Andrew Ng. “But it is exceedingly difficult to get access to someone else’s data. Thus data, rather than software, is the defensible barrier for many businesses.”

We may think we get a fair deal, offering our data as the price of sharing puppy pictures. By other metrics, we are being victimized: In the largest technology companies, the share of income going to labor is only about 5 to 15 percent, Mr. Posner and Mr. Weyl write. That’s way below Walmart’s 80 percent. Consumer data amounts to work they get free….

The big question, of course, is how we get there from here. My guess is that it would be naïve to expect Google and Facebook to start paying for user data of their own accord, even if that improved the quality of the information. Could policymakers step in, somewhat the way the European Commission did, demanding that technology companies compute the value of consumer data?…(More)”.

Trustworthy data will transform the world

Curated on Posted on by Stefaan Verhulst

John Thornhill at the Financial Times: “The internet’s original sin was identified as early as 1993 in a New Yorker cartoon. “On the internet, nobody knows you’re a dog,” the caption ran beneath an illustration of a pooch at a keyboard. That anonymity has brought some benefits. But it has also created myriad problems, injecting distrust into the digital world. If you do not know the provenance and integrity of information and data, how can you trust their veracity?

That has led to many of the scourges of our times, such as cyber crime, identity theft and fake news. In his Alan Turing Institute lecture in London last week, the American computer scientist Sandy Pentland outlined the massive gains that could result from trusted data.

The MIT professor argued that the explosion of such information would give us the capability to understand our world in far more detail than ever before. Most of what we know in the fields of sociology, psychology, political science and medicine is derived from tiny experiments in controlled environments. But the data revolution enables us to observe behaviour as it happens at mass scale in the real world. That feedback could provide invaluable evidence about which theories are most valid and which policies and products work best.

The promise is that we make soft social science harder and more predictive. That, in turn, could lead to better organisations, fairer government, and more effective monitoring of our progress towards achieving collective ambitions, such as the UN’s sustainable development goals. To take one small example, Mr Pentland illustrated the strong correlation between connectivity and wealth. By studying the telephone records of 100,000 users in south-east Asia, researchers have plotted social connectivity against income. The conclusion: “The more diverse your connections, the more money you have.” This is not necessarily a causal relationship but it does have a strong causal element, he suggested.

Similar studies of European cities have shown an almost total segregation between groups of different socio-economic status. That lack of connectivity has to be addressed if our politics is not to descend further into a meaningless dialogue.

Data give us a new way to measure progress.

For years, the Open Data movement has been working to create public data sets that can better inform decision making. This worldwide movement is prising open anonymised public data sets, such as transport records, so that they can be used by academics, entrepreneurs and civil society groups. However, much of the most valuable data is held by private entities, notably the consumer tech companies, telecoms operators, retailers and banks. “The big win would be to include private data as a public good,” Mr Pentland said….(More)”.

Using Open Data for Public Services

Curated on Posted on by Stefaan Verhulst

New report by the Open Data Institute:  “…Today we’re publishing our initial findings based on examining 8 examples where open data supports the delivery of a public service. We have defined 3 high-level ‘patterns’ for how open data is used in public services. We think these could be helpful for others looking to redesign and deliver better services.

The patterns are summarised in the table below:

The first pattern is perhaps the model which everyone is most familiar with as it’s used by the likes of Citymapper, who use open transport data from Transport for London to inform passengers about routes and timings, and other citizen-focused apps. Data is released by a public sector organisation about a public service and a third organisation uses this data to provide a complementary service, online or face-face, to help citizens use the public service.

The second pattern involves the release of open data in the service delivery chain. Open data is used to plan public service delivery and make service delivery chains more efficient. Examples provided in the report include local authorities’ release of open spending, contract and tender data, which is used by Spend Network to support better value for money in public expenditure.

In the third pattern, public sector organisations commissioning services and external organisations involved in service delivery make strategic decisions based on insights and patterns revealed by open data. Visualisations of open data can inform policies on job seeker allowance, as shown in the example from the Department for Work and Pensions in the report.

As well as identifying these patterns, we have created ecosystem maps of the public services we have examined to help understand the relationships and the mechanisms by which open data supports each of them….

Having compared the ecosystems of the examples we have considered so far, the report sets out practical recommendations for those involved in the delivery of public services and for Central Government for the better use of open data in the delivery of public services.

The recommendations are focused on organisational collaboration; technology infrastructure, digital skills and literacy; open standards for data; senior level championing; peer networks; intermediaries; and problem focus….(More)”.

Informed Diet Selection: Increasing Food Literacy through Crowdsourcing

Curated on Posted on by Stefaan Verhulst

Paper by Niels van Berkel et al: “The obesity epidemic is one of the greatest threats to health and wellbeing throughout much of the world. Despite information on healthy lifestyles and eating habits being more accessible than ever before, the situation seems to be growing worse  And for a person who wants to lose weight there are practically unlimited options and temptations to choose from. Food, or dieting, is a booming business, and thousands of companies and vendors want their cut by pitching their solutions, particularly online (Google) where people first turn to find weight loss information. In our work, we have set to harness the wisdom of crowds in making sense of available diets, and to offer a direct way for users to increase their food literacy during diet selection.  The Diet Explorer is a crowd-powered online knowledge base that contains an arbitrary number of weight loss diets that are all assessed in terms of an arbitrary set of criteria…(More)”.

Global Fishing Watch And The Power Of Data To Understand Our Natural World

Curated on Posted on by Stefaan Verhulst

Kalev Leetaru at Forbes: “A year and a half ago I wrote about the public debut of the Global Fishing Watch project as a showcase of what becomes possible when massive datasets are made accessible to the general public through easy-to-use interfaces that allow them to explore the planet they inhabit. At the time I noted how the project drove home the divide between the “glittering technological innovation of Silicon Valley and the technological dark ages of the development community” and what becomes possible when technologists and development organizations come together to apply incredible technology not for commercial gain, but rather to save the world itself. Continuing those efforts, last week Global Fishing Watch launched what it describes as the “the first ever dataset of global industrial fishing activities (all countries, all gears),” making the entire dataset freely accessible to seed new scientific, activist, governmental, journalistic and citizen understanding of the state of global fishing.

The Global Fishing Watch project stands as a powerful model for data-driven development work done right and hopefully, the rise of notable efforts like it will eventually catalyze the broader development community to emerge from the stone age of technology and more openly embrace the technological revolution. While it has a very long way to go, there are signs of hope for the development community as pockets of innovation begin to infuse the power of data-driven decision making and situational awareness into everything from disaster response to proactive planning to shaping legislative action.

Bringing technologists and development organizations together is not always that easy and the most creative solutions aren’t always to be found among the “usual suspects.” Open data and open challenges built upon them offer the potential for organizations to reach beyond the usual communities they interact with and identify innovative new approaches to the grand challenges of their fields. Just last month a collaboration of the World Bank, WeRobotics and OpenAerialMap launched a data challenge to apply deep learning to assess aerial imagery in the immediate aftermath of disasters to determine the impact to food producing trees and to road networks. By launching the effort as an open AI challenge, the goal is to reach the broader AI and open development communities at the forefront of creative and novel algorithmic approaches….(More)”.

The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation

Curated on Posted on by Stefaan Verhulst

Report by Miles Brundage et al: “Artificial intelligence and machine learning capabilities are growing at an unprecedented rate. These technologies have many widely beneficial applications, ranging from machine translation to medical image analysis. Countless more such applications are being developed and can be expected over the long term. Less attention has historically been paid to the ways in which artificial intelligence can be used maliciously. This report surveys the landscape of potential security threats from malicious uses of artificial intelligence technologies, and proposes ways to better forecast, prevent, and mitigate these threats. We analyze, but do not conclusively resolve, the question of what the long-term equilibrium between attackers and defenders will be. We focus instead on what sorts of attacks we are likely to see soon if adequate defenses are not developed.

In response to the changing threat landscape we make four high-level recommendations:

1. Policymakers should collaborate closely with technical researchers to investigate, prevent, and mitigate potential malicious uses of AI.

2. Researchers and engineers in artificial intelligence should take the dual-use nature of their work seriously, allowing misuserelated considerations to influence research priorities and norms, and proactively reaching out to relevant actors when harmful applications are foreseeable.

3. Best practices should be identified in research areas with more mature methods for addressing dual-use concerns, such as computer security, and imported where applicable to the case of AI.

4. Actively seek to expand the range of stakeholders and domain experts involved in discussions of these challenges….(More)”.

Six creative ways to engage the public in innovation policy

Curated on Posted on by Stefaan Verhulst

Tom Saunders at Nesta: “When someone decides to engage the public in a discussion about science or innovation, it usually involves booking a room, bringing a group of people together and giving them some information about a topical issue then listening to their thoughts about it. After this, the organisers ususally produce a report which they email to everyone they want to influence, or if it was commissioned directly by a research funder or a public body, there is usually a response detailing how they are going to act on the views of the public.

What’s wrong with this standard format of public dialogue? Through our research into public engagement in innovation policy, we noticed a number of issues:

  • Almost all public engagement work is offline, with very little money spent on digital methods

  • Most dialogues are top down, e.g a research council decides that they need to engage the public on a particular issue. They rarely come from citizens themselves

  • Most public dialogues are only open to a small number of hand-picked participants. No one else can take part, even if they want to

  • Few public engagement activities focus specifically on engaging with underrepresented groups…(More)”.

How AI-Driven Insurance Could Reduce Gun Violence

Curated on Posted on by Stefaan Verhulst

Jason Pontin at WIRED: “As a political issue, guns have become part of America’s endless, arid culture wars, where Red and Blue tribes skirmish for political and cultural advantage. But what if there were a compromise? Economics and machine learning suggest an answer, potentially acceptable to Americans in both camps.

Economists sometimes talk about “negative externalities,” market failures where the full costs of transactions are borne by third parties. Pollution is an externality, because society bears the costs of environmental degradation. The 20th-century British economist Arthur Pigou, who formally described externalities, also proposed their solution: so-called “Pigovian taxes,” where governments charge producers or customers, reducing the quantity of the offending products and sometimes paying for ameliorative measures. Pigovian taxes have been used to fight cigarette smoking or improve air quality, and are the favorite prescription of economists for reducing greenhouse gases. But they don’t work perfectly, because it’s hard for governments to estimate the costs of externalities.

Gun violence is a negative externality too. The choices of millions of Americans to buy guns overflow into uncaptured costs for society in the form of crimes, suicides, murders, and mass shootings. A flat gun tax would be a blunt instrument: It could only reduce gun violence by raising the costs of gun ownership so high that almost no one could legally own a gun, which would swell the black market for guns and probably increase crime. But insurers are very good at estimating the risks and liabilities of individual choices; insurance could capture the externalities of gun violence in a smarter, more responsive fashion.

Here’s the proposed compromise: States should require gun owners to be licensed and pay insurance, just as car owners must be licensed and insured today….

The actuaries who research risk have always considered a wide variety of factors when helping insurers price the cost of a policy. Car, home, and life insurance can vary according to a policy holder’s age, health, criminal record, employment, residence, and many other variables. But in recent years, machine learning and data analytics have provided actuaries with new predictive powers. According to Yann LeCun, the director of artificial intelligence at Facebook and the primary inventor of an important technique in deep learning called convolution, “Deep learning systems provide better statistical models with enough data. They can be advantageously applied to risk evaluation, and convolutional neural nets can be very good at prediction, because they can take into account a long window of past values.”

State Farm, Liberty Mutual, Allstate, and Progressive Insurance have all used algorithms to improve their predictive analysis and to more accurately distribute risk among their policy holders. For instance, in late 2015, Progressive created a telematics app called Snapshot that individual drivers used to collect information on their driving. In the subsequent two years, 14 billion miles of driving data were collected all over the country and analyzed on Progressive’s machine learning platform, H20.ai, resulting in discounts of $600 million for their policy holders. On average, machine learning produced a $130 discount for Progressive customers.

When the financial writer John Wasik popularized gun insurance in a series of posts in Forbes in 2012 and 2013, the NRA’s argument about prior constraints was a reasonable objection. Wasik proposed charging different rates to different types of gun owners, but there were too many factors that would have to be tracked over too long a period to drive down costs for low-risk policy holders. Today, using deep learning, the idea is more practical: Insurers could measure the interaction of dozens or hundreds of factors, predicting the risks of gun ownership and controlling costs for low-risk gun owners. Other, more risky bets might pay more. Some very risky would-be gun owners might be unable to find insurance at all. Gun insurance could even be dynamically priced, changing as the conditions of the policy holders’ lives altered, and the gun owners proved themselves better or worse risks.

Requiring gun owners to buy insurance wouldn’t eliminate gun violence in America. But a political solution to the problem of gun violence is chimerical….(More)”.

Strategies for Governing: The Foundation of Public Administration

Curated on Posted on by Stefaan Verhulst

Book by Alasdair S. Roberts: “The leaders of modern-day states face an extraordinary challenge. They must devise a strategy for leading their countries toward security, order, prosperity, well-being and justice. They must design and build institutions that will put their strategy into practice. And they must deal with the vicissitudes of time and chance, adapting strategies and institutions in response to altered circumstances and unexpected events. To do this well, leaders need advice about the machinery of government — how it should be designed and built, how it ought to be run, and how it can be disassembled and reconstructed. Researchers who work in the academic discipline of public administration should be expert in providing this sort of advice. And at one time, they did aspire to provide that sort of expertise. But the field of public administration took a wrong turn forty years ago, and slowly moved away from large and important questions about the governance of modern-day states. The purpose of this book is to map a way back to the main road….(More)”.

Do Academic Journals Favor Researchers from Their Own Institutions?

Curated on Posted on by Stefaan Verhulst

Yaniv Reingewertz and Carmela Lutmar at Harvard Business Review: “Are academic journals impartial? While many would suggest that academic journals work for the advancement of knowledge and science, we show this is not always the case. In a recent study, we find that two international relations (IR) journals favor articles written by authors who share the journal’s institutional affiliation. We term this phenomenon “academic in-group bias.”

In-group bias is a well-known phenomenon that is widely documented in the psychological literature. People tend to favor their group, whether it is their close family, their hometown, their ethnic group, or any other group affiliation. Before our study, the evidence regarding academic in-group bias was scarce, with only one studyfinding academic in-group bias in law journals. Studies from economics found mixedresults. Our paper provides evidence of academic in-group bias in IR journals, showing that this phenomenon is not specific to law. We also provide tentative evidence which could potentially resolve the conflict in economics, suggesting that these journals might also exhibit in-group bias. In short, we show that academic in-group bias is general in nature, even if not necessarily large in scope….(More)”.