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)”.

Mobile Data Collection Toolkit

Curated on Posted on by Stefaan Verhulst

Guide for the use of MDC in the humanitarian and development field: “This webpage aims at sharing documentation produced jointly by Terre des hommes (Tdh) and CartONG to help humanitarians and development actors use Mobile Data Collection (MDC)more efficiently in the field.

You will find tutorials and training material concerning all the phases of MDC, from thinking through the prerequisites of using MDC to the preparation of your forms and tools and the analysis of your data.

In addition to the MDC documentation you can also find a “Starter Kit” for data protection in humanitarian and development operations, as well as “Data Visualization” material, in the Analysis page,  produced to help organizations to better visualize the results of their data analyses.

These were made for Terre des hommes staff but are shared “as-is” as they could be useful for other NGOs. …(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)”.

Citicafe: conversation-based intelligent platform for citizen engagement

Curated on Posted on by Stefaan Verhulst

Paper by Amol Dumrewal et al in the Proceedings of the ACM India Joint International Conference on Data Science and Management of Data: “Community civic engagement is a new and emerging trend in urban cities driven by the mission of developing responsible citizenship. The recognition of civic potential in every citizen goes a long way in creating sustainable societies. Technology is playing a vital role in helping this mission and over the last couple of years, there have been a plethora of social media avenues to report civic issues. Sites like Twitter, Facebook, and other online portals help citizens to report issues and register complaints. These complaints are analyzed by the public services to help understand and in-turn address these issues. However, once the complaint is registered, often no formal or informal feedback is given back from these sites to the citizens. This de-motivates citizens and may deter them from registering further complaints. In addition, these sites offer no holistic information about a neighborhood to the citizens. It is useful for people to know whether there are similar complaints posted by other people in the same area, the profile of all complaints and a know-how of how and when these complaints will be addressed.

In this paper, we create a conversation-based platform CitiCafe for enhancing citizen engagement front-ended by a virtual agent with a Twitter interface. This platform back-end stores and processes information pertaining to civic complaints in a city. A Twitter based conversation service allows citizens to have a direct correspondence with CitiCafe via “tweets” and direct messages. The platform also helps citizens to (a) report problems and (b) gather information related to civic issues in different neighborhoods. This can also help, in the long run, to develop civic conversations among citizens and also between citizens and public services….(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)”.

Liquid democracy uses blockchain to fix politics, and now you can vote for it

Curated on Posted on by Stefaan Verhulst

Danny Crichton at TechCrunch: “…Confidence in Congress remains pitifully low, driven by perceived low ethical standards and an increasing awareness that politics is bought by the highest bidder.

Now, a group of technologists and blockchain enthusiasts are asking whether a new approach could reform the system, bringing citizens closer to their representatives and holding congressmen accountable to their voters in a public, verifiable way. And if you live in western San Francisco, you can actually vote to put this system into office.

The concept is known as liquid democracy, and it’s a solid choice for fixing a broken system. The idea is that every person should have the right to give feedback on a policy issue or a piece of new legislation, but often people don’t have the time to do so. Using a liquid democracy platform, however, that voter can select a personal representative who has the authority to be a proxy for their vote. That proxy can be changed at will as a voter’s interests change.

Here is where the magic happens. Those proxies can themselves proxy their votes to other people, creating a directed network graph, ideally connecting every voter to politicians and all publicly verified on a blockchain. While there may be 700,000 people in a congressional district, potentially only a few hundred of a few thousand “super proxies” would need to be deeply engaged in the system for better representation to take place.

David Ernst is a leader of the liquid democracy movement and now a candidate for California Assembly District 19, which centers on the western half of San Francisco. He is ardently committed to the concept, and despite its novelty, believes that this is the path forward for improving governance….

Following college (which he began at age 16) and a few startup jobs, Ernst began working as CTO of a startup called Numerai, a crypto-backed decentralized hedge fund that allows data scientists to earn money when they solve data challenges. “The idea was that we can include many more people to participate in the system who weren’t able to before,” Ernst explained. That’s when it hit him that the decentralized nature of blockchain could allow for more participation in politics, fusing his two passions.

Ernst followed the campaign of the Flux Party in Australia in 2016, which is trying to implement what it calls “issue-based direct democracy” in that country’s legislature. “That was when something clicked,” he said. A congressman for example could commit to voting the calculated liquid democracy position, and “We could elect these sort of remote-controlled politicians as a way to graft this new system onto the old system.”

He built a platform called United.vote to handle the logistics of selecting personal representatives and voting on issues. More importantly, the app then tracks how those votes compare to the votes of congressmen and provides a scorecard….(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)”.

Data-Driven Regulation and Governance in Smart Cities

Curated on Posted on by Stefaan Verhulst

Chapter by Sofia Ranchordas and Abram Klop in Berlee, V. Mak, E. Tjong Tjin Tai (Eds), Research Handbook on Data Science and Law (Edward Elgar, 2018): “This paper discusses the concept of data-driven regulation and governance in the context of smart cities by describing how these urban centres harness these technologies to collect and process information about citizens, traffic, urban planning or waste production. It describes how several smart cities throughout the world currently employ data science, big data, AI, Internet of Things (‘IoT’), and predictive analytics to improve the efficiency of their services and decision-making.

Furthermore, this paper analyses the legal challenges of employing these technologies to influence or determine the content of local regulation and governance. It explores in particular three specific challenges: the disconnect between traditional administrative law frameworks and data-driven regulation and governance, the effects of the privatization of public services and citizen needs due to the growing outsourcing of smart cities technologies to private companies; and the limited transparency and accountability that characterizes data-driven administrative processes. This paper draws on a review of interdisciplinary literature on smart cities and offers illustrations of data-driven regulation and governance practices from different jurisdictions….(More)”.

No One Owns Data

Curated on Posted on by Stefaan Verhulst

Paper by Lothar Determann: “Businesses, policy makers, and scholars are calling for property rights in data. They currently focus particularly on the vast amounts of data generated by connected cars, industrial machines, artificial intelligence, toys and other devices on the Internet of Things (IoT). This data is personal to numerous parties who are associated with a connected device, for example, the driver of a connected car, its owner and passengers, as well as other traffic participants. Manufacturers, dealers, independent providers of auto parts and services, insurance companies, law enforcement agencies and many others are also interested in this data. Various parties are actively staking their claims to data on the Internet of Things, as they are mining data, the fuel of the digital economy.

Stakeholders in digital markets often frame claims, negotiations and controversies regarding data access as one of ownership. Businesses regularly assert and demand that they own data. Individual data subjects also assume that they own data about themselves. Policy makers and scholars focus on how to redistribute ownership rights to data. Yet, upon closer review, it is very questionable whether data is—or should be—subject to any property rights. This article unambiguously answers the question in the negative, both with respect to existing law and future lawmaking, in the United States as in the European Union, jurisdictions with notably divergent attitudes to privacy, property and individual freedoms….

The article begins with a brief review of the current landscape of the Internet of Things notes explosive growth of data pools generated by connected devices, artificial intelligence, big data analytics tools and other information technologies. Part 1 lays the foundation for examining concrete current legal and policy challenges in the remainder of the article. Part 2 supplies conceptual differentiation and definitions with respect to “data” and “information” as the subject of rights and interests. Distinctions and definitional clarity serve as the basis for examining the purposes and reach of existing property laws in Part 3, including real property, personal property and intellectual property laws. Part 4 analyzes the effect of data-related laws that do not grant property rights. Part 5 examines how the interests of the various stakeholders are protected or impaired by the current framework of data-related laws to identify potential gaps that could warrant additional property rights. Part 6 examines policy considerations for and against property rights in data. Part 7 concludes that no one owns data and no one should own data….(More)”.