A Silicon Valley scheme to “disrupt” America’s education system would hurt the people who need it the most

The plot to destroy education: Why technology could ruin American classrooms — by trying to fix them

The plot to destroy education: Why technology could ruin American classrooms — by trying to fix them
(Credit: Warner Bros. Entertainment Inc./Pgiam via iStock/Salon)

How does Silicon Valley feel about college? Here’s a taste: Seven words in a tweet provoked by a conversation about education started by Silicon Valley venture capitalist Marc Andreeseen.

Arrogance? Check. Supreme confidence? Check. Oblivious to the value actually provided by a college education? Check.

The $400 billion a year that Americans pay for education after high school is being wasted on an archaic brick-and-mortar irrelevance. We can do better! 

But how? The question becomes more pertinent every day — and it’s one that Silicon Valley would dearly like to answer.

The robots are coming for our jobs, relentlessly working their way up the value chain. Anything that can be automated will be automated. The obvious — and perhaps the only — answer to this threat is a vastly improved educational system. We’ve got to leverage our human intelligence to stay ahead of robotic A.I.! And right now, everyone agrees, the system is not meeting the challenge. The cost of a traditional four-year college education has far outpaced inflation. Student loan debt is a national tragedy. Actually achieving a college degree still bequeaths better job prospects than the alternative, but for many students, the cost-benefit ratio is completely out of whack.

No problem, says the tech industry. Like a snake eating its own tail, Silicon Valley has the perfect solution for the social inequities caused by technologically induced “disruption.” More disruption!

Universities are a hopelessly obsolete way to go about getting an education when we’ve got the Internet, the argument goes. Just as Airbnb is disemboweling the hotel industry and Uber is annihilating the taxi industry, companies such as Coursera and Udacity will leverage technology and access to venture capital in order to crush the incumbent education industry, supposedly offering high-quality educational opportunities for a fraction of the cost of a four-year college.



There is an elegant logic to this argument. We’ll use the Internet to stay ahead of the Internet. Awesome tools are at our disposal. In MOOCs — “Massive Open Online Courses” — hundreds of thousands of students will imbibe the wisdom of Ivy League “superprofessors” via pre-recorded lectures piped down to your smartphone. No need even for overworked graduate student teaching assistants. Intelligent software will take care of the grading. (That’s right — we’ll use robots to meet the robot threat!) The market, in other words, will provide the solution to the problem that the market has caused. It’s a wonderful libertarian dream.

But there’s a flaw in the logic. Early returns on MOOCs have confirmed what just about any teacher could have told you before Silicon Valley started believing it could “fix” education. Real human interaction and engagement are hugely important to delivering a quality education. Most crucially, hands-on interaction with teachers is vital for the students who are in most desperate need for an education — those with the least financial resources and the most challenging backgrounds.

Of course, it costs money to provide greater human interaction. You need bodies — ideally, bodies with some mastery of the subject material. But when you raise costs, you destroy the primary attraction of Silicon Valley’s “disruptive” model. The big tech success stories are all about avoiding the costs faced by the incumbents. Airbnb owns no hotels. Uber owns no taxis. The selling point of Coursera and Udacity is that they need own no universities.

But education is different than running a hotel. There’s a reason why governments have historically considered providing education a public good. When you start throwing bodies into the fray to teach people who can’t afford a traditional private education you end up disastrously chipping away at the profits that the venture capitalists backing Coursera and Udacity demand.

And that’s a tail that the snake can’t swallow.

* * *

The New York Times famously dubbed 2012 “The Year of the MOOC.” Coursera and Udacity (both started by Stanford professors) and an MIT-Harvard collaboration called EdX exploded into the popular imagination. But the hype ebbed almost as quickly as it had flowed. In 2013, after a disastrous pilot experiment in which Udacity and San Jose State collaborated to deliver three courses, MOOCs were promptly declared dead — with the harshest schadenfreude coming from academics who saw the rush to MOOCs as an educational travesty.

At the end of 2013, the New York Times had changed its tune: “After Setbacks, Online Courses are Rethought.”

But MOOC supporters have never wavered. In May, Clayton Christensen, the high priest of “disruption” theory, scoffed at the unbelievers: ”[T]heir potential to disrupt — on price, technology, even pedagogy — in a long-stagnant industry,” wrote Christensen, ” is only just beginning to be seen.”

At the end of June, the Economist followed suit with a package of stories touting the inevitable “creative destruction” threatened by MOOCs: “[A] revolution has begun thanks to three forces: rising costs, changing demand and disruptive technology. The result will be the reinvention of the university …” It’s 2012 all over again!

Sure, there have been speed bumps along the way. But as Christensen explained, the same is true for any would-be disruptive start-up. Failures are bound to happen. What makes Silicon Valley so special is its ability to learn from mistakes, tweak its biz model and try something new. It’s called “iteration.”

There is, of course, great merit to the iterative process. And it would be foolish to claim that new technology won’t have an impact on the educational process. If there’s one thing that the Internet and smartphones are insanely good at, it is providing access to information. A teenager with a phone in Uganda has opportunities for learning that most of the world never had through the entire course of human history. That’s great.

But there’s a crucial difference between “access to information” and “education” that explains why the university isn’t about to become obsolete, and why we can’t depend — as Marc Andreessen tells us — on the magic elixir of innovation plus the free market to solve our education quandary.

Nothing better illustrates this point than a closer look at the Udacity-San Jose State collaboration.

* * *

When Gov. Jerry Brown announced the collaboration between Udacity, founded by the Stanford computer science Sebastian Thrun and San Jose State, a publicly funded university in the heart of Silicon Valley, in January 2013, the match seemed perfect. Where else would you want to test out the future of education? The plan was to focus on three courses: elementary statistics, remedial math and college algebra. The target student demographic was notoriously ill-served by the university system: “Students were drawn from a lower-income high school and the underperforming ranks of SJSU’s student body,” reported Fast Company.

The results of the pilot, conducted in the spring of 2013, were a disaster, reported Fast Company:

Among those pupils who took remedial math during the pilot program, just 25 percent passed. And when the online class was compared with the in-person variety, the numbers were even more discouraging. A student taking college algebra in person was 52 percent more likely to pass than one taking a Udacity class, making the $150 price tag–roughly one-third the normal in-state tuition–seem like something less than a bargain.

A second attempt during the summer achieved better results, but with a much less disadvantaged student body; and, even more crucially, with considerably greater resources put into human interaction and oversight. For example, San Jose State reported that the summer courses were improved by “checking in with students more often.”

But the prime takeaway was stark. Inside Higher Education reported that a research report conducted by San Jose State on the experiment concluded that “it may be difficult for the university to deliver online education in this format to the students who need it most.”

In an iterative world, San Jose State and Udacity would have learned from their mistakes. The next version of their collaboration would have incorporated the increased human resources necessary to make it work, to be sure that students didn’t fall through the cracks. But the lesson that Udacity learned from the collaboration turned out be something different: There isn’t going to be much profit to be made attempting to apply the principles of MOOCs to students from a disadvantaged background.

Thrun set off a firestorm of commentary when he told Fast Company’s Max Chafkin this:

“These were students from difficult neighborhoods, without good access to computers, and with all kinds of challenges in their lives,” he says. “It’s a group for which this medium is not a good fit….”

“I’d aspired to give people a profound education–to teach them something substantial… But the data was at odds with this idea.”

Henceforth, Udacity would “pivot” to focusing on vocational training funded by direct corporate support.

Thrun later claimed that his comments were misinterpreted by Fast Company. And in his May Op-Ed Christensen argued that Udacity’s pivot was a boon!

Udacity, for its part, should be applauded for not burning through all of its money in pursuit of the wrong strategy. The company realized — and publicly acknowledged — that its future lay on a different path than it had originally anticipated. Indeed, Udacity’s pivot may have even prevented a MOOC bubble from bursting.

Educating the disadvantaged via MOOCs is the wrong strategy? That’s not a pivot — it’s an abject surrender.

The Economist, meanwhile, brushed off the San Jose State episode by noting that “online learning has its pitfalls.” But the Economist also published a revealing observation: “In some ways MOOCs will reinforce inequality … among students (the talented will be much more comfortable than the weaker outside the structured university environment) …”

But isn’t that exactly the the problem? No one can deny that the access to information facilitated by the Internet is a fantastic thing for talented students — and particularly so for those with secure economic backgrounds and fast Internet connections. But such people are most likely to succeed in a world full of smart robots anyway. The challenge posed by technological transformation and disruption is that the jobs that are being automated away first are the ones that are most suited to the less talented or advantaged. In other words, the population that MOOCs are least suited to serving is the population that technology is putting in the most vulnerable position.

Innovation and the free market aren’t going to fix this problem, for the very simple reason that there is no money in it. There’s no profit to be mined in educating people who not only can’t pay for an education, but also require greater human resources to be educated.

This is why we have public education in the first place.

“College is a public good,” says Jonathan Rees, a professor at Colorado State University who has been critical of MOOCs. “It’s what industrialized democratic society should be providing for students.”

Andrew Leonard Andrew Leonard is a staff writer at Salon. On Twitter, @koxinga21.

Former State Department employee reveals spying on Americans by executive order

http://usofarn.com/wp-content/uploads/2013/08/nsa-prism1.jpg

By Ed Hightower
25 July 2014

In the latest revelation of unconstitutional spying on US citizens by the National Security Agency (NSA), former State Department employee John Napier Tye has given his account of ongoing violations of privacy under cover of a legal fig leaf known as Executive Order 12333.

Last week the Washington Post published Tye’s lengthy criticism of the Obama administration under the title “Meet Executive Order 12333: The Reagan rule that lets the NSA spy on Americans.” The editorial underscores both the immense scope of illegal spying by an unaccountable military-intelligence apparatus and the sham character of the official “reform.”

President Ronald Reagan enacted Executive Order 12333 in 1981. The order was aimed at providing a lax legal standard for the collection of communication content —not just metadata such as call logs—of US citizens, as long as the communication was not obtained within the United States.

While 12333 was legally dubious even in 1981, it was not until the widespread transfer of data over the internet that it could be exploited for the mass collection of communications. Enormous amounts of data and communications generated by Americans in the form of emails, for example, are now routinely routed to servers all over the world, bringing the data within the now much broader reach of 12333.

Tye’s editorial calls attention to 12333, saying that the order is now used to justify possibly more illegal surveillance than Section 215 of the Patriot Act, which sanctions bulk collection of telecommunications records. While Section 215 has garnered more public attention, Tye argues that it “is a small part of the picture and does not include the universe of collection and storage of communications by US persons authorized under Executive Order 12333.”

Referring to “classified facts that I am prohibited by law from publishing,” Tye writes, “I believe that Americans should be even more concerned about the collection and storage of their communications under Executive Order 12333 than under Section 215 [of the Patriot Act].”

Because it is an executive order as opposed to a statute, 12333 is subject to virtually zero oversight. The attorney general, who is part of the executive branch and serves at the pleasure of the president, determines what restraints, if any, apply. Currently, intelligence agencies are permitted to keep data obtained pursuant to 12333 for up to five years.

Nor does 12333 typically require a warrant. Tye explains that the NSA keeps data obtained through 12333 even if it is not directly related to a surveillance target who was subject to a warrant. This so-called “incidental” collection represents the exception that swallows the rule.

As Tye describes it, incidental collection is “a legal loophole that can be stretched very wide. Remember that the NSA is building a data center in Utah five times the size of the U.S. Capitol building, with its own power plant that will reportedly burn $40 million a year in electricity. ‘Incidental collection’ might need its own power plant.”

Tye worked for the State Department from 2011 until this past April. He currently serves as legal director for the nonprofit advocacy group Avaaz. His Post article was reviewed and cleared by the State Department and NSA prior to publication. Before he left his State Department job, Tye filed a complaint about 12333-related spying with the department’s inspector general, and he eventually brought this complaint to the House and Senate intelligence committees, as well as to the inspector general of the NSA.

While Tye did not leak any documents or data to the press, it is clear that what he saw and heard at the State Department deeply troubled him.

He begins his Washington Post piece with this disturbing anecdote:

“In March I received a call from the White House counsel’s office regarding a speech I had prepared for my boss at the State Department… The draft stated that ‘if U.S. citizens disagree with congressional and executive branch determinations about the proper scope of signals intelligence activities, they have the opportunity to change the policy through our democratic process.’”

“But the White House counsel’s office told me that no, that wasn’t true. I was instructed to amend the line, making a general reference to ‘our laws and policies,’ rather than our intelligence practices. I did.”

In other words, Tye was directed to remove from his speech something that might give the misleading impression that the US population has any meaningful oversight where the military-intelligence apparatus is concerned.

In his op-ed comment, Tye also points out the Obama administration’s “reforms” are bogus. Obama’s Review Group on Intelligence and Communications Technologies recommended that data obtained by incidental collection should be purged. Tye writes that an unclassified document he saw while working with the State Department made the White House’s position clear: there were no plans to change the practices around Executive Order 12333.

The terrifying uncertainty of our high-tech future

Our new robot overlords:

Are computers taking our jobs?

Our new robot overlords: The terrifying uncertainty of our high-tech future
(Credit: Ociacia, MaraZe via Shutterstock/Salon)
This article was originally published by Scientific American.

Scientific American Last fall economist Carl Benedikt Frey and information engineer Michael A. Osborne, both at the University of Oxford, published a study estimating the probability that 702 occupations would soon be computerized out of existence. Their findings were startling. Advances in data mining, machine vision, artificial intelligence and other technologies could, they argued, put 47 percent of American jobs at high risk of being automated in the years ahead. Loan officers, tax preparers, cashiers, locomotive engineers, paralegals, roofers, taxi drivers and even animal breeders are all in danger of going the way of the switchboard operator.

Whether or not you buy Frey and Osborne’s analysis, it is undeniable that something strange is happening in the U.S. labor market. Since the end of the Great Recession, job creation has not kept up with population growth. Corporate profits have doubled since 2000, yet median household income (adjusted for inflation) dropped from $55,986 to $51,017. At the same time, after-tax corporate profits as a share of gross domestic product increased from around 5 to 11 percent, while compensation of employees as a share of GDP dropped from around 47 to 43 percent. Somehow businesses are making more profit with fewer workers.

Erik Brynjolfsson and Andrew McAfee, both business researchers at the Massachusetts Institute of Technology, call this divergence the “great decoupling.” In their view, presented in their recent book “The Second Machine Age,” it is a historic shift.

The conventional economic wisdom has long been that as long as productivity is increasing, all is well. Technological innovations foster higher productivity, which leads to higher incomes and greater well-being for all. And for most of the 20th century productivity and incomes did rise in parallel. But in recent decades the two began to diverge. Productivity kept increasing while incomes—which is to say, the welfare of individual workers—stagnated or dropped.

Brynjolfsson and McAfee argue that technological advances are destroying jobs, particularly low-skill jobs, faster than they are creating them. They cite research showing that so-called routine jobs (bank teller, machine operator, dressmaker) began to fade in the 1980s, when computers first made their presence known, but that the rate has accelerated: between 2001 and 2011, 11 percent of routine jobs disappeared.



Plenty of economists disagree, but it is hard to referee this debate, in part because of a lack of data. Our understanding of the relation between technological advances and employment is limited by outdated metrics. At a roundtable discussion on technology and work convened this year by the European Union, the IRL School at Cornell University and the Conference Board (a business research association), a roomful of economists and financiers repeatedly emphasized how many basic economic variables are measured either poorly or not at all. Is productivity declining? Or are we simply measuring it wrong? Experts differ. What kinds of workers are being sidelined, and why? Could they get new jobs with the right retraining? Again, we do not know.

In 2013 Brynjolfsson told Scientific American that the first step in reckoning with the impact of automation on employment is to diagnose it correctly—“to understand why the economy is changing and why people aren’t doing as well as they used to.” If productivity is no longer a good proxy for a vigorous economy, then we need a new way to measure economic health. In a 2009 report economists Joseph Stiglitz of Columbia University, Amartya Sen of Harvard University and Jean-Paul Fitoussi of the Paris Institute of Political Studies made a similar case, writing that “the time is ripe for our measurement system to shift emphasis from measuring economic production to measuring people’s well-being.” An IRL School report last year called for statistical agencies to capture more and better data on job market churn—data that could help us learn which job losses stem from automation.

Without such data, we will never properly understand how technology is changing the nature of work in the 21st century—and what, if anything, should be done about it. As one participant in this year’s roundtable put it, “Even if this is just another industrial revolution, people underestimate how wrenching that is. If it is, what are the changes to the rules of labor markets and businesses that should be made this time? We made a lot last time. What is the elimination of child labor this time? What is the eight-hour workday this time?”

 

The rise of data and the death of politics

Tech pioneers in the US are advocating a new data-based approach to governance – ‘algorithmic regulation’. But if technology provides the answers to society’s problems, what happens to governments?

US president Barack Obama with Facebook founder Mark Zuckerberg

Government by social network? US president Barack Obama with Facebook founder Mark Zuckerberg. Photograph: Mandel Ngan/AFP/Getty Images

On 24 August 1965 Gloria Placente, a 34-year-old resident of Queens, New York, was driving to Orchard Beach in the Bronx. Clad in shorts and sunglasses, the housewife was looking forward to quiet time at the beach. But the moment she crossed the Willis Avenue bridge in her Chevrolet Corvair, Placente was surrounded by a dozen patrolmen. There were also 125 reporters, eager to witness the launch of New York police department’s Operation Corral – an acronym for Computer Oriented Retrieval of Auto Larcenists.

Fifteen months earlier, Placente had driven through a red light and neglected to answer the summons, an offence that Corral was going to punish with a heavy dose of techno-Kafkaesque. It worked as follows: a police car stationed at one end of the bridge radioed the licence plates of oncoming cars to a teletypist miles away, who fed them to a Univac 490 computer, an expensive $500,000 toy ($3.5m in today’s dollars) on loan from the Sperry Rand Corporation. The computer checked the numbers against a database of 110,000 cars that were either stolen or belonged to known offenders. In case of a match the teletypist would alert a second patrol car at the bridge’s other exit. It took, on average, just seven seconds.

Compared with the impressive police gear of today – automatic number plate recognition, CCTV cameras, GPS trackers – Operation Corral looks quaint. And the possibilities for control will only expand. European officials have considered requiring all cars entering the European market to feature a built-in mechanism that allows the police to stop vehicles remotely. Speaking earlier this year, Jim Farley, a senior Ford executive, acknowledged that “we know everyone who breaks the law, we know when you’re doing it. We have GPS in your car, so we know what you’re doing. By the way, we don’t supply that data to anyone.” That last bit didn’t sound very reassuring and Farley retracted his remarks.

As both cars and roads get “smart,” they promise nearly perfect, real-time law enforcement. Instead of waiting for drivers to break the law, authorities can simply prevent the crime. Thus, a 50-mile stretch of the A14 between Felixstowe and Rugby is to be equipped with numerous sensors that would monitor traffic by sending signals to and from mobile phones in moving vehicles. The telecoms watchdog Ofcom envisions that such smart roads connected to a centrally controlled traffic system could automatically impose variable speed limits to smooth the flow of traffic but also direct the cars “along diverted routes to avoid the congestion and even [manage] their speed”.

Other gadgets – from smartphones to smart glasses – promise even more security and safety. In April, Apple patented technology that deploys sensors inside the smartphone to analyse if the car is moving and if the person using the phone is driving; if both conditions are met, it simply blocks the phone’s texting feature. Intel and Ford are working on Project Mobil – a face recognition system that, should it fail to recognise the face of the driver, would not only prevent the car being started but also send the picture to the car’s owner (bad news for teenagers).

The car is emblematic of transformations in many other domains, from smart environments for “ambient assisted living” where carpets and walls detect that someone has fallen, to various masterplans for the smart city, where municipal services dispatch resources only to those areas that need them. Thanks to sensors and internet connectivity, the most banal everyday objects have acquired tremendous power to regulate behaviour. Even public toilets are ripe for sensor-based optimisation: the Safeguard Germ Alarm, a smart soap dispenser developed by Procter & Gamble and used in some public WCs in the Philippines, has sensors monitoring the doors of each stall. Once you leave the stall, the alarm starts ringing – and can only be stopped by a push of the soap-dispensing button.

In this context, Google’s latest plan to push its Android operating system on to smart watches, smart cars, smart thermostats and, one suspects, smart everything, looks rather ominous. In the near future, Google will be the middleman standing between you and your fridge, you and your car, you and your rubbish bin, allowing the National Security Agency to satisfy its data addiction in bulk and via a single window.

This “smartification” of everyday life follows a familiar pattern: there’s primary data – a list of what’s in your smart fridge and your bin – and metadata – a log of how often you open either of these things or when they communicate with one another. Both produce interesting insights: cue smart mattresses – one recent model promises to track respiration and heart rates and how much you move during the night – and smart utensils that provide nutritional advice.

In addition to making our lives more efficient, this smart world also presents us with an exciting political choice. If so much of our everyday behaviour is already captured, analysed and nudged, why stick with unempirical approaches to regulation? Why rely on laws when one has sensors and feedback mechanisms? If policy interventions are to be – to use the buzzwords of the day – “evidence-based” and “results-oriented,” technology is here to help.

This new type of governance has a name: algorithmic regulation. In as much as Silicon Valley has a political programme, this is it. Tim O’Reilly, an influential technology publisher, venture capitalist and ideas man (he is to blame for popularising the term “web 2.0″) has been its most enthusiastic promoter. In a recent essay that lays out his reasoning, O’Reilly makes an intriguing case for the virtues of algorithmic regulation – a case that deserves close scrutiny both for what it promises policymakers and the simplistic assumptions it makes about politics, democracy and power.

To see algorithmic regulation at work, look no further than the spam filter in your email. Instead of confining itself to a narrow definition of spam, the email filter has its users teach it. Even Google can’t write rules to cover all the ingenious innovations of professional spammers. What it can do, though, is teach the system what makes a good rule and spot when it’s time to find another rule for finding a good rule – and so on. An algorithm can do this, but it’s the constant real-time feedback from its users that allows the system to counter threats never envisioned by its designers. And it’s not just spam: your bank uses similar methods to spot credit-card fraud.

In his essay, O’Reilly draws broader philosophical lessons from such technologies, arguing that they work because they rely on “a deep understanding of the desired outcome” (spam is bad!) and periodically check if the algorithms are actually working as expected (are too many legitimate emails ending up marked as spam?).

O’Reilly presents such technologies as novel and unique – we are living through a digital revolution after all – but the principle behind “algorithmic regulation” would be familiar to the founders of cybernetics – a discipline that, even in its name (it means “the science of governance”) hints at its great regulatory ambitions. This principle, which allows the system to maintain its stability by constantly learning and adapting itself to the changing circumstances, is what the British psychiatrist Ross Ashby, one of the founding fathers of cybernetics, called “ultrastability”.

To illustrate it, Ashby designed the homeostat. This clever device consisted of four interconnected RAF bomb control units – mysterious looking black boxes with lots of knobs and switches – that were sensitive to voltage fluctuations. If one unit stopped working properly – say, because of an unexpected external disturbance – the other three would rewire and regroup themselves, compensating for its malfunction and keeping the system’s overall output stable.

Ashby’s homeostat achieved “ultrastability” by always monitoring its internal state and cleverly redeploying its spare resources.

Like the spam filter, it didn’t have to specify all the possible disturbances – only the conditions for how and when it must be updated and redesigned. This is no trivial departure from how the usual technical systems, with their rigid, if-then rules, operate: suddenly, there’s no need to develop procedures for governing every contingency, for – or so one hopes – algorithms and real-time, immediate feedback can do a better job than inflexible rules out of touch with reality.

Algorithmic regulation could certainly make the administration of existing laws more efficient. If it can fight credit-card fraud, why not tax fraud? Italian bureaucrats have experimented with the redditometro, or income meter, a tool for comparing people’s spending patterns – recorded thanks to an arcane Italian law – with their declared income, so that authorities know when you spend more than you earn. Spain has expressed interest in a similar tool.

Such systems, however, are toothless against the real culprits of tax evasion – the super-rich families who profit from various offshoring schemes or simply write outrageous tax exemptions into the law. Algorithmic regulation is perfect for enforcing the austerity agenda while leaving those responsible for the fiscal crisis off the hook. To understand whether such systems are working as expected, we need to modify O’Reilly’s question: for whom are they working? If it’s just the tax-evading plutocrats, the global financial institutions interested in balanced national budgets and the companies developing income-tracking software, then it’s hardly a democratic success.

With his belief that algorithmic regulation is based on “a deep understanding of the desired outcome”, O’Reilly cunningly disconnects the means of doing politics from its ends. But the how of politics is as important as the what of politics – in fact, the former often shapes the latter. Everybody agrees that education, health, and security are all “desired outcomes”, but how do we achieve them? In the past, when we faced the stark political choice of delivering them through the market or the state, the lines of the ideological debate were clear. Today, when the presumed choice is between the digital and the analog or between the dynamic feedback and the static law, that ideological clarity is gone – as if the very choice of how to achieve those “desired outcomes” was apolitical and didn’t force us to choose between different and often incompatible visions of communal living.

By assuming that the utopian world of infinite feedback loops is so efficient that it transcends politics, the proponents of algorithmic regulation fall into the same trap as the technocrats of the past. Yes, these systems are terrifyingly efficient – in the same way that Singapore is terrifyingly efficient (O’Reilly, unsurprisingly, praises Singapore for its embrace of algorithmic regulation). And while Singapore’s leaders might believe that they, too, have transcended politics, it doesn’t mean that their regime cannot be assessed outside the linguistic swamp of efficiency and innovation – by using political, not economic benchmarks.

As Silicon Valley keeps corrupting our language with its endless glorification of disruption and efficiency – concepts at odds with the vocabulary of democracy – our ability to question the “how” of politics is weakened. Silicon Valley’s default answer to the how of politics is what I call solutionism: problems are to be dealt with via apps, sensors, and feedback loops – all provided by startups. Earlier this year Google’s Eric Schmidt even promised that startups would provide the solution to the problem of economic inequality: the latter, it seems, can also be “disrupted”. And where the innovators and the disruptors lead, the bureaucrats follow.

The intelligence services embraced solutionism before other government agencies. Thus, they reduced the topic of terrorism from a subject that had some connection to history and foreign policy to an informational problem of identifying emerging terrorist threats via constant surveillance. They urged citizens to accept that instability is part of the game, that its root causes are neither traceable nor reparable, that the threat can only be pre-empted by out-innovating and out-surveilling the enemy with better communications.

Speaking in Athens last November, the Italian philosopher Giorgio Agamben discussed an epochal transformation in the idea of government, “whereby the traditional hierarchical relation between causes and effects is inverted, so that, instead of governing the causes – a difficult and expensive undertaking – governments simply try to govern the effects”.

Nobel laureate Daniel Kahneman

Governments’ current favourite pyschologist, Daniel Kahneman. Photograph: Richard Saker for the Observer
For Agamben, this shift is emblematic of modernity. It also explains why the liberalisation of the economy can co-exist with the growing proliferation of control – by means of soap dispensers and remotely managed cars – into everyday life. “If government aims for the effects and not the causes, it will be obliged to extend and multiply control. Causes demand to be known, while effects can only be checked and controlled.” Algorithmic regulation is an enactment of this political programme in technological form.The true politics of algorithmic regulation become visible once its logic is applied to the social nets of the welfare state. There are no calls to dismantle them, but citizens are nonetheless encouraged to take responsibility for their own health. Consider how Fred Wilson, an influential US venture capitalist, frames the subject. “Health… is the opposite side of healthcare,” he said at a conference in Paris last December. “It’s what keeps you out of the healthcare system in the first place.” Thus, we are invited to start using self-tracking apps and data-sharing platforms and monitor our vital indicators, symptoms and discrepancies on our own.This goes nicely with recent policy proposals to save troubled public services by encouraging healthier lifestyles. Consider a 2013 report by Westminster council and the Local Government Information Unit, a thinktank, calling for the linking of housing and council benefits to claimants’ visits to the gym – with the help of smartcards. They might not be needed: many smartphones are already tracking how many steps we take every day (Google Now, the company’s virtual assistant, keeps score of such data automatically and periodically presents it to users, nudging them to walk more).

The numerous possibilities that tracking devices offer to health and insurance industries are not lost on O’Reilly. “You know the way that advertising turned out to be the native business model for the internet?” he wondered at a recent conference. “I think that insurance is going to be the native business model for the internet of things.” Things do seem to be heading that way: in June, Microsoft struck a deal with American Family Insurance, the eighth-largest home insurer in the US, in which both companies will fund startups that want to put sensors into smart homes and smart cars for the purposes of “proactive protection”.

An insurance company would gladly subsidise the costs of installing yet another sensor in your house – as long as it can automatically alert the fire department or make front porch lights flash in case your smoke detector goes off. For now, accepting such tracking systems is framed as an extra benefit that can save us some money. But when do we reach a point where not using them is seen as a deviation – or, worse, an act of concealment – that ought to be punished with higher premiums?

Or consider a May 2014 report from 2020health, another thinktank, proposing to extend tax rebates to Britons who give up smoking, stay slim or drink less. “We propose ‘payment by results’, a financial reward for people who become active partners in their health, whereby if you, for example, keep your blood sugar levels down, quit smoking, keep weight off, [or] take on more self-care, there will be a tax rebate or an end-of-year bonus,” they state. Smart gadgets are the natural allies of such schemes: they document the results and can even help achieve them – by constantly nagging us to do what’s expected.

The unstated assumption of most such reports is that the unhealthy are not only a burden to society but that they deserve to be punished (fiscally for now) for failing to be responsible. For what else could possibly explain their health problems but their personal failings? It’s certainly not the power of food companies or class-based differences or various political and economic injustices. One can wear a dozen powerful sensors, own a smart mattress and even do a close daily reading of one’s poop – as some self-tracking aficionados are wont to do – but those injustices would still be nowhere to be seen, for they are not the kind of stuff that can be measured with a sensor. The devil doesn’t wear data. Social injustices are much harder to track than the everyday lives of the individuals whose lives they affect.

In shifting the focus of regulation from reining in institutional and corporate malfeasance to perpetual electronic guidance of individuals, algorithmic regulation offers us a good-old technocratic utopia of politics without politics. Disagreement and conflict, under this model, are seen as unfortunate byproducts of the analog era – to be solved through data collection – and not as inevitable results of economic or ideological conflicts.

However, a politics without politics does not mean a politics without control or administration. As O’Reilly writes in his essay: “New technologies make it possible to reduce the amount of regulation while actually increasing the amount of oversight and production of desirable outcomes.” Thus, it’s a mistake to think that Silicon Valley wants to rid us of government institutions. Its dream state is not the small government of libertarians – a small state, after all, needs neither fancy gadgets nor massive servers to process the data – but the data-obsessed and data-obese state of behavioural economists.

The nudging state is enamoured of feedback technology, for its key founding principle is that while we behave irrationally, our irrationality can be corrected – if only the environment acts upon us, nudging us towards the right option. Unsurprisingly, one of the three lonely references at the end of O’Reilly’s essay is to a 2012 speech entitled “Regulation: Looking Backward, Looking Forward” by Cass Sunstein, the prominent American legal scholar who is the chief theorist of the nudging state.

And while the nudgers have already captured the state by making behavioural psychology the favourite idiom of government bureaucracy –Daniel Kahneman is in, Machiavelli is out – the algorithmic regulation lobby advances in more clandestine ways. They create innocuous non-profit organisations like Code for America which then co-opt the state – under the guise of encouraging talented hackers to tackle civic problems.

Airbnb's homepage.

Airbnb: part of the reputation-driven economy.
Such initiatives aim to reprogramme the state and make it feedback-friendly, crowding out other means of doing politics. For all those tracking apps, algorithms and sensors to work, databases need interoperability – which is what such pseudo-humanitarian organisations, with their ardent belief in open data, demand. And when the government is too slow to move at Silicon Valley’s speed, they simply move inside the government. Thus, Jennifer Pahlka, the founder of Code for America and a protege of O’Reilly, became the deputy chief technology officer of the US government – while pursuing a one-year “innovation fellowship” from the White House.Cash-strapped governments welcome such colonisation by technologists – especially if it helps to identify and clean up datasets that can be profitably sold to companies who need such data for advertising purposes. Recent clashes over the sale of student and health data in the UK are just a precursor of battles to come: after all state assets have been privatised, data is the next target. For O’Reilly, open data is “a key enabler of the measurement revolution”.This “measurement revolution” seeks to quantify the efficiency of various social programmes, as if the rationale behind the social nets that some of them provide was to achieve perfection of delivery. The actual rationale, of course, was to enable a fulfilling life by suppressing certain anxieties, so that citizens can pursue their life projects relatively undisturbed. This vision did spawn a vast bureaucratic apparatus and the critics of the welfare state from the left – most prominently Michel Foucault – were right to question its disciplining inclinations. Nonetheless, neither perfection nor efficiency were the “desired outcome” of this system. Thus, to compare the welfare state with the algorithmic state on those grounds is misleading.

But we can compare their respective visions for human fulfilment – and the role they assign to markets and the state. Silicon Valley’s offer is clear: thanks to ubiquitous feedback loops, we can all become entrepreneurs and take care of our own affairs! As Brian Chesky, the chief executive of Airbnb, told the Atlantic last year, “What happens when everybody is a brand? When everybody has a reputation? Every person can become an entrepreneur.”

Under this vision, we will all code (for America!) in the morning, drive Uber cars in the afternoon, and rent out our kitchens as restaurants – courtesy of Airbnb – in the evening. As O’Reilly writes of Uber and similar companies, “these services ask every passenger to rate their driver (and drivers to rate their passenger). Drivers who provide poor service are eliminated. Reputation does a better job of ensuring a superb customer experience than any amount of government regulation.”

The state behind the “sharing economy” does not wither away; it might be needed to ensure that the reputation accumulated on Uber, Airbnb and other platforms of the “sharing economy” is fully liquid and transferable, creating a world where our every social interaction is recorded and assessed, erasing whatever differences exist between social domains. Someone, somewhere will eventually rate you as a passenger, a house guest, a student, a patient, a customer. Whether this ranking infrastructure will be decentralised, provided by a giant like Google or rest with the state is not yet clear but the overarching objective is: to make reputation into a feedback-friendly social net that could protect the truly responsible citizens from the vicissitudes of deregulation.

Admiring the reputation models of Uber and Airbnb, O’Reilly wants governments to be “adopting them where there are no demonstrable ill effects”. But what counts as an “ill effect” and how to demonstrate it is a key question that belongs to the how of politics that algorithmic regulation wants to suppress. It’s easy to demonstrate “ill effects” if the goal of regulation is efficiency but what if it is something else? Surely, there are some benefits – fewer visits to the psychoanalyst, perhaps – in not having your every social interaction ranked?

The imperative to evaluate and demonstrate “results” and “effects” already presupposes that the goal of policy is the optimisation of efficiency. However, as long as democracy is irreducible to a formula, its composite values will always lose this battle: they are much harder to quantify.

For Silicon Valley, though, the reputation-obsessed algorithmic state of the sharing economy is the new welfare state. If you are honest and hardworking, your online reputation would reflect this, producing a highly personalised social net. It is “ultrastable” in Ashby’s sense: while the welfare state assumes the existence of specific social evils it tries to fight, the algorithmic state makes no such assumptions. The future threats can remain fully unknowable and fully addressable – on the individual level.

Silicon Valley, of course, is not alone in touting such ultrastable individual solutions. Nassim Taleb, in his best-selling 2012 book Antifragile, makes a similar, if more philosophical, plea for maximising our individual resourcefulness and resilience: don’t get one job but many, don’t take on debt, count on your own expertise. It’s all about resilience, risk-taking and, as Taleb puts it, “having skin in the game”. As Julian Reid and Brad Evans write in their new book, Resilient Life: The Art of Living Dangerously, this growing cult of resilience masks a tacit acknowledgement that no collective project could even aspire to tame the proliferating threats to human existence – we can only hope to equip ourselves to tackle them individually. “When policy-makers engage in the discourse of resilience,” write Reid and Evans, “they do so in terms which aim explicitly at preventing humans from conceiving of danger as a phenomenon from which they might seek freedom and even, in contrast, as that to which they must now expose themselves.”

What, then, is the progressive alternative? “The enemy of my enemy is my friend” doesn’t work here: just because Silicon Valley is attacking the welfare state doesn’t mean that progressives should defend it to the very last bullet (or tweet). First, even leftist governments have limited space for fiscal manoeuvres, as the kind of discretionary spending required to modernise the welfare state would never be approved by the global financial markets. And it’s the ratings agencies and bond markets – not the voters – who are in charge today.

Second, the leftist critique of the welfare state has become only more relevant today when the exact borderlines between welfare and security are so blurry. When Google’s Android powers so much of our everyday life, the government’s temptation to govern us through remotely controlled cars and alarm-operated soap dispensers will be all too great. This will expand government’s hold over areas of life previously free from regulation.

With so much data, the government’s favourite argument in fighting terror – if only the citizens knew as much as we do, they too would impose all these legal exceptions – easily extends to other domains, from health to climate change. Consider a recent academic paper that used Google search data to study obesity patterns in the US, finding significant correlation between search keywords and body mass index levels. “Results suggest great promise of the idea of obesity monitoring through real-time Google Trends data”, note the authors, which would be “particularly attractive for government health institutions and private businesses such as insurance companies.”

If Google senses a flu epidemic somewhere, it’s hard to challenge its hunch – we simply lack the infrastructure to process so much data at this scale. Google can be proven wrong after the fact – as has recently been the case with its flu trends data, which was shown to overestimate the number of infections, possibly because of its failure to account for the intense media coverage of flu – but so is the case with most terrorist alerts. It’s the immediate, real-time nature of computer systems that makes them perfect allies of an infinitely expanding and pre-emption‑obsessed state.

Perhaps, the case of Gloria Placente and her failed trip to the beach was not just a historical oddity but an early omen of how real-time computing, combined with ubiquitous communication technologies, would transform the state. One of the few people to have heeded that omen was a little-known American advertising executive called Robert MacBride, who pushed the logic behind Operation Corral to its ultimate conclusions in his unjustly neglected 1967 book, The Automated State.

At the time, America was debating the merits of establishing a national data centre to aggregate various national statistics and make it available to government agencies. MacBride attacked his contemporaries’ inability to see how the state would exploit the metadata accrued as everything was being computerised. Instead of “a large scale, up-to-date Austro-Hungarian empire”, modern computer systems would produce “a bureaucracy of almost celestial capacity” that can “discern and define relationships in a manner which no human bureaucracy could ever hope to do”.

“Whether one bowls on a Sunday or visits a library instead is [of] no consequence since no one checks those things,” he wrote. Not so when computer systems can aggregate data from different domains and spot correlations. “Our individual behaviour in buying and selling an automobile, a house, or a security, in paying our debts and acquiring new ones, and in earning money and being paid, will be noted meticulously and studied exhaustively,” warned MacBride. Thus, a citizen will soon discover that “his choice of magazine subscriptions… can be found to indicate accurately the probability of his maintaining his property or his interest in the education of his children.” This sounds eerily similar to the recent case of a hapless father who found that his daughter was pregnant from a coupon that Target, a retailer, sent to their house. Target’s hunch was based on its analysis of products – for example, unscented lotion – usually bought by other pregnant women.

For MacBride the conclusion was obvious. “Political rights won’t be violated but will resemble those of a small stockholder in a giant enterprise,” he wrote. “The mark of sophistication and savoir-faire in this future will be the grace and flexibility with which one accepts one’s role and makes the most of what it offers.” In other words, since we are all entrepreneurs first – and citizens second, we might as well make the most of it.

What, then, is to be done? Technophobia is no solution. Progressives need technologies that would stick with the spirit, if not the institutional form, of the welfare state, preserving its commitment to creating ideal conditions for human flourishing. Even some ultrastability is welcome. Stability was a laudable goal of the welfare state before it had encountered a trap: in specifying the exact protections that the state was to offer against the excesses of capitalism, it could not easily deflect new, previously unspecified forms of exploitation.

How do we build welfarism that is both decentralised and ultrastable? A form of guaranteed basic income – whereby some welfare services are replaced by direct cash transfers to citizens – fits the two criteria.

Creating the right conditions for the emergence of political communities around causes and issues they deem relevant would be another good step. Full compliance with the principle of ultrastability dictates that such issues cannot be anticipated or dictated from above – by political parties or trade unions – and must be left unspecified.

What can be specified is the kind of communications infrastructure needed to abet this cause: it should be free to use, hard to track, and open to new, subversive uses. Silicon Valley’s existing infrastructure is great for fulfilling the needs of the state, not of self-organising citizens. It can, of course, be redeployed for activist causes – and it often is – but there’s no reason to accept the status quo as either ideal or inevitable.

Why, after all, appropriate what should belong to the people in the first place? While many of the creators of the internet bemoan how low their creature has fallen, their anger is misdirected. The fault is not with that amorphous entity but, first of all, with the absence of robust technology policy on the left – a policy that can counter the pro-innovation, pro-disruption, pro-privatisation agenda of Silicon Valley. In its absence, all these emerging political communities will operate with their wings clipped. Whether the next Occupy Wall Street would be able to occupy anything in a truly smart city remains to be seen: most likely, they would be out-censored and out-droned.

To his credit, MacBride understood all of this in 1967. “Given the resources of modern technology and planning techniques,” he warned, “it is really no great trick to transform even a country like ours into a smoothly running corporation where every detail of life is a mechanical function to be taken care of.” MacBride’s fear is O’Reilly’s master plan: the government, he writes, ought to be modelled on the “lean startup” approach of Silicon Valley, which is “using data to constantly revise and tune its approach to the market”. It’s this very approach that Facebook has recently deployed to maximise user engagement on the site: if showing users more happy stories does the trick, so be it.

Algorithmic regulation, whatever its immediate benefits, will give us a political regime where technology corporations and government bureaucrats call all the shots. The Polish science fiction writer Stanislaw Lem, in a pointed critique of cybernetics published, as it happens, roughly at the same time as The Automated State, put it best: “Society cannot give up the burden of having to decide about its own fate by sacrificing this freedom for the sake of the cybernetic regulator.”

 

Meet the Online Tracking Device That is Virtually Impossible to Block

A new kind of tracking tool, canvas fingerprinting, is being used to follow visitors to thousands of top websites, from WhiteHouse.gov to YouPorn.

(David Sleight/ProPublica)

Update: A YouPorn.com spokesperson said that the website was “completely unaware that AddThis contained a tracking software that had the potential to jeopardize the privacy of our users.” After this article was published, YouPorn removed AddThis technology from its website.

This story was co-published with Mashable.

A new, extremely persistent type of online tracking is shadowing visitors to thousands of top websites, from WhiteHouse.gov to YouPorn.com.

First documented in a forthcoming paper by researchers at Princeton University and KU Leuven University in Belgium, this type of tracking, called canvas fingerprinting, works by instructing the visitor’s Web browser to draw a hidden image. Because each computer draws the image slightly differently, the images can be used to assign each user’s device a number that uniquely identifies it.

 

Canvas Fingerprinting in Action

Watch your browser generate a unique fingerprint image. This is for informational purposes only and no fingerprint information is sent to ProPublica. (Mike Tigas, ProPublica)

See your browser’s fingerprintClick the button above and your computer and web browser will draw a ProPublica-designed canvas fingerprint.

 

Like other tracking tools, canvas fingerprints are used to build profiles of users based on the websites they visit — profiles that shape which ads, news articles, or other types of content are displayed to them.

But fingerprints are unusually hard to block: They can’t be prevented by using standard Web browser privacy settings or using anti-tracking tools such as AdBlock Plus.

The researchers found canvas fingerprinting computer code, primarily written by a company called AddThis, on 5 percent of the top 100,000 websites. Most of the code was on websites that use AddThis’ social media sharing tools. Other fingerprinters include the German digital marketer Ligatus and the Canadian dating site Plentyoffish. (A list of all the websites on which researchers found the code is here).

Rich Harris, chief executive of AddThis, said that the company began testing canvas fingerprinting earlier this year as a possible way to replace “cookies,” the traditional way that users are tracked, via text files installed on their computers.

“We’re looking for a cookie alternative,” Harris said in an interview.

Harris said the company considered the privacy implications of canvas fingerprinting before launching the test, but decided “this is well within the rules and regulations and laws and policies that we have.”

He added that the company has only used the data collected from canvas fingerprints for internal research and development. The company won’t use the data for ad targeting or personalization if users install the AddThis opt-out cookie on their computers, he said.

Arvind Narayanan, the computer science professor who led the Princeton research team, countered that forcing users to take AddThis at its word about how their data will be used, is “not the best privacy assurance.”

Device fingerprints rely on the fact that every computer is slightly different: Each contains different fonts, different software, different clock settings and other distinctive features. Computers automatically broadcast some of their attributes when they connect to another computer over the Internet.

Tracking companies have long sought to use those differences to uniquely identify devices for online advertising purposes, particularly as Web users are increasingly using ad-blocking software and deleting cookies.

In May 2012, researchers at the University of California, San Diego, noticed that a Web programming feature called “canvas” could allow for a new type of fingerprint — by pulling in different attributes than a typical device fingerprint.

How You Can Try to Thwart Canvas Fingerprinting

  • Use the Tor browser (Warning: can be slow)
  • Block JavaScript from loading in your browser (Warning: breaks a lot of web sites)
  • Use NoScript browser extension to block JavaScript from known fingerprinters such as AddThis (Warning: requires a lot of research and decision-making)
  • Try the experimental browser extension Chameleon that is designed to block fingerprinting (Warning: only recommended for tech-savvy users at this point)
  • Install opt-out cookies from known fingerprinters such as AddThis (Warning: fingerprint will likely still be collected, companies simply pledge not to use the data for ad targeting or personalization)

In June, the Tor Project added a feature to its privacy-protecting Web browser to notify users when a website attempts to use the canvas feature and sends a blank canvas image. But other Web browsers did not add notifications for canvas fingerprinting.

A year later, Russian programmer Valentin Vasilyev noticed the study and added a canvas feature to freely available fingerprint code that he had posted on the Internet. The code was immediately popular.

But Vasilyev said that the company he was working for at the time decided against using the fingerprint technology. “We collected several million fingerprints but we decided against using them because accuracy was 90 percent,” he said, “and many of our customers were on mobile and the fingerprinting doesn’t work well on mobile.”

Vasilyev added that he wasn’t worried about the privacy concerns of fingerprinting. “The fingerprint itself is a number which in no way is related to a personality,” he said.

AddThis improved upon Vasilyev’s code by adding new tests and using the canvas to draw a pangram “Cwm fjordbank glyphs vext quiz” — a sentence that uses every letter of the alphabet at least once. This allows the company to capture slight variations in how each letter is displayed.

AddThis said it rolled out the feature to a small portion of the 13 million websites on which its technology appears, but is considering ending its test soon. “It’s not uniquely identifying enough,” Harris said.

AddThis did not notify the websites on which the code was placed because “we conduct R&D projects in live environments to get the best results from testing,” according to a spokeswoman.

She added that the company does not use any of the data it collects — whether from canvas fingerprints or traditional cookie-based tracking — from government websites including WhiteHouse.gov for ad targeting or personalization.

The company offered no such assurances about data it routinely collects from visitors to other sites, such as YouPorn.com. YouPorn.com did not respond to inquiries from ProPublica about whether it was aware of AddThis’ test of canvas fingerprinting on its website.

Read our recent coverage about how online tracking is getting creepier, how Facebook has been tracking you, and what tools to use to protect yourself.

More musicians are taking aim at the rates paid by Spotify and Pandora, and warning whole genres are in danger

It’s not just David Byrne and Radiohead: Spotify, Pandora and how streaming music kills jazz and classical

It's not just David Byrne and Radiohead: Spotify, Pandora and how streaming music kills jazz and classical

David Byrne, Thom Yorke (Credit: Reuters/Hugo Correia/AP/Chris Pizzello/Photo collage by Salon)

After years in which tech-company hype has drowned out most other voices, the frustration of musicians with the digital music world has begun to get a hearing. We know now that many rockers don’t like it. Less discussed so far is the trouble jazz and classical musicians — and their fans — have with music streaming, which is being hailed as the “savior” of the music business.

But between low royalties, opaque payout rates, declining record sales and suspicion that the major labels have cut deals with the streamers that leave musicians out of the equation, anger from the music business’s artier edges is slowing growing. It’s further proof of the lie of the “long tail.” The shift to digital is also helping to isolate these already marginalized genres: It has a decisive effect on what listeners can find, and on whether or not an artist can earn a living from his work. (Music streaming, in all genres, is up 42 percent for the first half of this year, according to Nielsen SoundScan, against the first half of 2013. Over the same period, CD sales fell 19.6 percent, and downloads, the industry’s previous savior, were down 11.6 percent.)

Only a very few classical artists have been outspoken on the issue so far: San-Francisco-based Zoe Keating — a tech-savvy, DIY, Amanda Palmer of the cello — has blown the whistle on the tiny amounts the streaming services pay musicians. Though she’s exactly the kind of artist who should be cashing in on streaming, since she releases her own music, tours relentlessly, and has developed a strong following since her days with rock band Rasputina, only 8 percent of  her last year’s earnings from recorded music came from streaming. The iTunes store, which pays out in small amounts since most purchases are for 99 cent songs, paid her about six times what she earned from streaming. (More than 400,000 Spotify streams earned her $1,764; almost 2 million YouTube views generated $1,248.)

For jazz and classical players without Keating’s entrepreneurial energy or larger cult following, the numbers are even bleaker. “It feels awful,” says Christina Courtin, a Julliard-trained violinist who plays in classical groups and has put out albums on the Nonesuch and Hundred Pockets labels. “I don’t count on that as a way to make money — I don’t see how it makes sense for a musician. It’s pretty dark — no one’s selling as much as they were even five years ago.”



Some artists remember a very different world. “I used to sell CDs of my music,” says Richard Danielpour, a celebrated American composer who has written an opera with Toni Morrison and once had an exclusive recording contract with Sony Classical. “And now we get nothing.”

It’s not just streaming, but the larger digital era that’s burying record stores, radio and recordings – and it’s hitting jazz and classical musicians especially hard. For some young musicians launching their careers, the “exposure” they get on Pandora or YouTube brings them employment or a fan base somewhere down the line. But many wait in vain. And like their counterparts in the pop world, musicians typically cannot opt out of streaming and the rest of the new world.

“One of the big reasons musicians kept control of their publishing was for the possibility that at least we would be paid when those songs were played in media outlets,” says jazz pianist Jason Moran, currently the jazz advisor for the Kennedy Center. “Back in the day, Fats Waller, and tons of other artists were robbed of their publishing. This is the new version of it, but on a much more wider scale.”

*

In some ways, the trouble in these genres resembles the problems experienced by any non-superstar musicians. Royalties on steaming services, for instance, are notoriously low. “All of my colleagues — composers and arrangers — are seeing huge cuts in their earnings,” says Paul Chihara, a veteran composer who until recently headed UCLA’s film-music program. “In effect, we’re not getting royalties. It’s almost amusing some of the royalty checks I get.” One of the last checks he got was for $29. “And it bounced.”

The pain is especially acute for indie musicians. While some jazz and classical labels are owned by one of the three majors — Blue Note and Deutsche Grammophon, for example, are now part of the Universal Music Group — the vast majority of musicians record for independent labels. And the indies have been largely left out of the sweet deals struck with the streamers. Most of those deals are opaque; the informed speculation says that these arrangement are not good for musicians, especially those not on the few remaining majors.

“Musicians in niche categories need to be fearful of the agreements that labels are signing with streaming services,” says music historian Ted Gioia, who has also recorded as a jazz pianist. Some of these deals, he suspects, allow the steamers to pay nothing at all to some artists, including most who record jazz and classical music. “The record labels could make a case that they don’t need to share royalties with artists whose sales don’t cross a certain threshold. If you’re Lady Gaga or Justin Bieber, you have no problem. But otherwise, you would get no royalties. The nature of these deals are that the rich get richer and the poor get poorer.”

Labels that own substantial back catalog — old Pink Floyd and Eagles albums, and earlier music that no longer require royalty payments to musicians — have likely cut much better deals than labels that primarily put out new music, especially those in non-pop genres. Says Gioia: “I suspect we’d find agreements where the labels say, [to the streamers], ‘You can have our whole catalog for $5 million, plus you pay us a fraction of a penny for any song that streams more than a million times.’” You don’t have to be a conspiracy theorist to think this way: The major labels have a number of weaselly little tricks like this one, sometimes called a “digital breakage,” in which musicians get nothing.

Moran compares the appearance of Spotify on the scene to the arrival of Wal-Mart to an American small-town: The new model undercuts the existing ones, and helps put smaller, independent stores out of business.

Indie labels are equally vulnerable. Pi Recordings is a jazz label that puts out recordings by the cream of the avant-garde, including Henry Threadgill, Marc Ribot and Rudresh Mahanthappa. It’s been described as one of the rare success stories in a dark time. But Yulun Wang, who co-runs the label, is not sure how they can stand up against the streaming onslaught.

“You have the guy who buys 20 jazz records a year — $300 a year,” Wang says. “He might buy one or two of our albums. If I convert that guy to Spotify – he’s now getting all-you-can-eat for $120. And the proportion that comes to me is literally pennies. That’s when it over. That’s will force labels like ours to either change the way we do things significantly.”

The digital enthusiasts say that labels need to “adjust” to the new world – by taking a piece of musicians’ touring, or cutting “360 deals” in which they get part of every strand of an artist’s revenue stream. But for jazz artists, touring outside New York and a few other cities does not yield much. “If I take 15 percent of someone making $30,000, it’s just less money in their pocket.” At a certain point, the artist can no longer pay the rent. “That’s when it’s game over.”

*

But it’s not just a problem of scale. There are distinctive qualities to jazz and classical music that make it a difficult fit to the digital world as it now exists, and that punish musicians and curious fans alike. To Jean Cook, a new-music violinist, onetime Mekon, and director of programs for the Future Musical Coalition, it further marginalizes these already peripheral styles, creating what she calls “invisible genres.”

It doesn’t matter if it’s Spotify, Pandora, iTunes, or Beats Music, she says. “Any music service that’s serving pop and classical music will not serve classical music well.” The problem is the nature of classical music, and jazz as well, and the way they differ from pop music. They all make different use of metadata – a term most people associate with Edward Snowden’s NSA revelations, but which have a profound importance to streaming services. Put most simply: Classical music and jazz are such a mismatch for existing streaming services, it’s almost impossible to find stuff. Cook realized this when she got a recommendation from a music lover, and found herself falling down an online labyrinth trying to find it.

Here’s a good place to start: Say you’re looking for a bedrock recording, the Beethoven Piano Concertos, with titan Maurizio Pollini on piano. Who is the “artist” for this one? Is it the Berlin Philharmonic, or Claudio Abbado, who conducts them? Is it Pollini? Or is it Beethoven himself? If you can see the entire record jacket, you can see who the recording includes. Otherwise, you could find yourself guessing.

Or, if you want music written the Russian late romantic, do you want Rachmaninoff, or Rachmaninoff? Chances are, your service will have one but not the other. And what do you call the movements of a symphony or chamber piece? By their Roman numeral? Or by names like andante or scherzo?

“These services are built to serve the largest segments of the marketplace — pop, country and hip hop,” says Cook. None of these have this kind of complicated structure.

Jazz offers similar difficulties, she says. Say you want to find recordings by pianist Bill Evans. You can find a bunch of them — but nothing linking him to “Kind of Blue,” perhaps the most important (and, in vinyl and CD form, certainly the bestselling) recording he was ever a part of. Evans shaped that album profoundly. You won’t find John Coltrane — another key voice on that session — there either, since it’s a Miles Davis record.

“Listing sidemen is something that is just not built into the architecture,” says Cook. It’s not a small problem. “I can’t think of a single example of a jazz musician who was not a sideman at one point in their career. We’re talking about a significant portion of jazz history that can’t get out.” It also makes you wonder — what are the chances that sidemen, or their heirs, get paid when things are streamed? And what do potential music consumers do when they can’t find what they’re looking for?

There used to be a solution to this. “Go back to the days of record stores,” says Gioia, “and customers could learn a lot from browsing the racks, or asking the serious music fans who worked there.” (Classical record stores, then and now, tended to have their recordings organized by composer rather than group.) The algorithms for specialized genres — classical, reggae, acoustic blues, Brazilian music —are hopeless, he says.

“These days, you have to know exactly what you’re looking for. If you want something by Beyonce or Miley Cyrus, it’s not hard. If you’re interested in niche music, you can be in the position of not knowing what’s out there. I still find myself missing important releases by musicians I care about. Streaming provides access to millions of hours of music, but it’s easy to get lost in it.”

If dedicated fans like Cook and Gioia have these problems, what will happen to the casual or new fans that every genre needs in order to stay alive? They’ll simply drift away to the stuff that’s being beamed at them by advertisers around the clock.

*

Even some of those frightened and demoralized by the digital transition think things can be improved for jazz and classical music.

So far, Wang’s solution has been to drop out. It’s nearly impossible for artists to withdraw, but as a label head, he can pull all of Pi’s music off Spotify. After three or four months on the service, two years back, he received a royalty statement of about $25 for all of it, and decided it just wasn’t worth it.

“What we found when we got out of Spotify — after these dire warnings — was that our sales went up; they absolutely jumped.”

He’s very familiar with the pressure to give art away. “We were always told you need to get as many audiences as possible … With the exposure argument, you’re told, ‘You could become the next Lady Gaga!’ It’s like playing Lotto — buy dollar tickets, and you could hit it big. In jazz, keep buying dollar tickets so you can win a dollar fifty.”

Cook sees the poor fit of these genres to streaming services as part of a larger phenomenon: Their radio playlists don’t show up in Billboard, their ticket receipts and album sales are often not reported to SoundScan and PollStar, and their awards on the Grammys are rarely televised. “This affects the visibility of jazz and classical music, and the way they are viewed by the rest of the industry.”

Part of a solution involves getting the data straight. “There is no database that tells you who played on what recording, and who wrote each song. ASCAP has one piece of the puzzle; iTunes has another. If you’ve got a music service, you need this, because you need to know who to pay. You need to tell listeners who they’re listening too. And if it’s not consistent, it’s not searchable.”

She wonders how it happens, though, even with open-source software that makes it easier. “The classical community needs to say, ‘This is a good index, instead of the crap the record labels are sending you. It requires a coordinated effort by a lot of different parties.”

Composer Danielpour says that classical people should not give up on recording work and trying to get on the radio. “Even though radio is a mid-20th century medium, for classical music it’s still a powerful source of revenue,” especially in Europe, where royalties are typically better. He recently returned from a trip to St. Petersburg, Russia. “For European and Russian audiences, classical music is religion. For us in America, it’s entertainment.”

Gioia, a former businessman, is pragmatic and forward looking. “My view is that the only solution for this, that is equitable for everyone, is for the music labels, in partnership with the artists, to control their own streaming,” says Gioia. “They need to bypass Silicon Valley.

“They need to work together with a new model, to control distribution and not rely on Apple, Amazon and everyone else. The music industry has always hated technology — they hated radio when it came out — and have always dragged their feet. They need to embrace technology and do it better.”

 

Scott Timberg, a longtime arts reporter in Los Angeles who has contributed to the New York Times, runs the blog Culture Crash. His book, “Culture Crash: The Killing of the Creative Class” comes out in January. Follow him on Twitter at @TheMisreadCity

http://www.salon.com/2014/07/20/its_not_just_david_byrne_and_radiohead_spotify_pandora_and_how_streaming_music_kills_jazz_and_classical/?source=newsletter

THE BULLSHIT MACHINE

Here’s a tiny confession. I’m bored.

Yes; I know. I’m a sinner. Go ahead. Burn me at the stake of your puritanical Calvinism; the righteously, thoroughly, well, boring idea that boredom itself is a moral defect; that a restless mind is the Devil’s sweatshop.

There’s nothing more boring than that; and I’ll return to that very idea at the end of this essay; which I hope is the beginning.

What am I bored of? Everything. Blogs books music art business ideas politics tweets movies science math technology…but more than that: the spirit of the age; the atmosphere of the time; the tendency of the now; the disposition of the here.

Sorry; but it’s true. It’s boring me numb and dumb.

A culture that prizes narcissism above individualism. A politics that places “tolerance” above acceptance. A spirit that encourages cynicism over reverence. A public sphere that places irony over sincerity. A technosophy that elevates “data” over understanding. A society that puts “opportunity” before decency. An economy that…you know. Works us harder to make us poorer at “jobs” we hate where we make stuff that sucks every last bit of passion from our souls to sell to everyone else who’s working harder to get poorer at “jobs” they hate where they make stuff that sucks every last bit of passion from their souls.

To be bored isn’t to be indifferent. It is to be fatigued. Because one is exhausted. And that is precisely where—and only where—the values above lead us. To exhaustion; with the ceaseless, endless, meaningless work of maintaining the fiction. Of pretending that who we truly want to be is what everyone believes everyone else wants to be. Liked, not loved; “attractive”, not beautiful; clever, not wise; snarky, not happy; advantaged, not prosperous.

It exhausts us; literally; this game of parasitically craving everyone’s cravings. It makes us adversaries not of one another; but of ourselves. Until there is nothing left. Not of us as we are; but of the people we might have been. The values above shrink and reduce and diminish our potential; as individuals, as people, societies. And so I have grown fatigued by them.

Ah, you say. But when hasn’t humanity always suffered all the above? Please. Let’s not mince ideas. Unless you think the middle class didn’t actually thrive once; unless you think that the gentleman that’s made forty seven Saw flicks (so far) is this generation’s Alfred Hitchcock; unless you believe that this era has a John Lennon; unless you think that Jeff Koons is Picasso…perhaps you see my point.

I’m bored, in short, of what I’d call a cycle of perpetual bullshit. A bullshit machine. The bullshit machine turns life into waste.

The bullshit machine looks something like this. Narcissism about who you are leads to cynicism about who you could be leads to mediocrity in what you do…leads to narcissism about who you are. Narcissism leads to cynicism leads to mediocrity…leads to narcissism.

Let me simplify that tiny model of the stalemate the human heart can reach with life.

The bullshit machine is the work we do only to live lives we don’t want, need, love, or deserve.

Everything’s work now. Relationships; hobbies; exercise. Even love. Gruelling; tedious; unrelenting; formulaic; passionless; calculated; repetitive; predictable; analysed; mined; timed; performed.

Work is bullshit. You know it, I know it; mankind has always known it. Sure; you have to work at what you want to accomplish. But that’s not the point. It is the flash of genius; the glimmer of intuition; the afterglow of achievement; the savoring of experience; the incandescence of meaning; all these make life worthwhile, pregnant, impossible, aching with purpose. These are the ends. Work is merely the means.

Our lives are confused like that. They are means without ends; model homes; acts which we perform, but do not fully experience.

Remember when I mentioned puritanical Calvinism? The idea that being bored is itself a sign of a lack of virtue—and that is, itself, the most boring idea in the world?

That’s the battery that powers the bullshit machine. We’re not allowed to admit it: that we’re bored. We’ve always got to be doing something. Always always always. Tapping, clicking, meeting, partying, exercising, networking, “friending”. Work hard, play hard, live hard. Improve. Gain. Benefit. Realize.

Hold on. Let me turn on crotchety Grandpa mode. Click.

Remember when cafes used to be full of people…thinking? Now I defy you to find one not full of people Tinder—Twitter—Facebook—App-of-the-nanosecond-ing; furiously. Like true believers hunched over the glow of a spiritualized Eden they can never truly enter; which is precisely why they’re mesmerized by it. The chance at a perfect life; full of pleasure; the perfect partner, relationship, audience, job, secret, home, career; it’s a tap away. It’s something like a slot-machine of the human soul, this culture we’re building. The jackpot’s just another coin away…forever. Who wouldn’t be seduced by that?

Winners of a million followers, fans, friends, lovers, dollars…after all, a billion people tweeting, updating, flicking, swiping, tapping into the void a thousand times a minute can’t be wrong. Can they?

And therein is the paradox of the bullshit machine. We do more than humans have ever done before. But we are not accomplishing much; and we are, it seems to me, becoming even less than that.

The more we do, the more passive we seem to become. Compliant. Complaisant. As if we are merely going through the motions.

Why? We are something like apparitions today; juggling a multiplicity of selves through the noise; the “you” you are on Facebook, Twitter, Tumblr, Tinder…wherever…at your day job, your night job, your hobby, your primary relationship, your friend-with-benefits, your incredibly astonishing range of extracurricular activities. But this hyperfragmentation of self gives rise to a kind of schizophrenia; conflicts, dissocations, tensions, dislocations, anxieties, paranoias, delusions. Our social wombs do not give birth to our true selves; the selves explosive with capability, possibility, wonder.

Tap tap tap. And yet. We are barely there, at all; in our own lives; in the moments which we will one day look back on and ask ourselves…what were we thinking wasting our lives on things that didn’t matter at all?

The answer, of course, is that we weren’t thinking. Or feeling. We don’t have time to think anymore. Thinking is a superluxury. Feeling is an even bigger superluxury. In an era where decent food, water, education, and healthcare are luxuries; thinking and feeling are activities to costly for society to allow. They are a drag on “growth”; a burden on “productivity”; they slow down the furious acceleration of the bullshit machine.

And so. Here we are. Going through the motions. The bullshit machine says the small is the great; the absence is the presence; the vicious is the noble; the lie is the truth. We believe it; and, greedily, it feeds on our belief. The more we feed it, the more insatiable it becomes. Until, at last, we are exhausted. By pretending to want the lives we think we should; instead of daring to live the lives we know we could.

Fuck it. Just admit it. You’re probably just as bored as I am.

Good for you.

Welcome to the world beyond the Bullshit Machine.

Thinking of Trying to Make Money Off Airbnb or Uber? Read This First


The so-called ‘sharing economy’ is becoming a booming industry for middlemen, but for you, it’s complicated.

Photo Credit: Shutterstock.com

Joining the sharing economy as a provider of services – accommodation, transportation or whatever else the market calls for – gives you a chance to make money while being part of a “movement”. It sounds tremendously appealing, doesn’t it?

The companies being built around this new zeitgeist have different enough business models for it to be worth discussing them as if they do, indeed, fall into a different category from more traditional bastions of capitalism. To some, the appeal is the ability to feel like part of a community by pooling their resources: helping a neighbor or network member to cut the cost of everything from a pricey textbook to a baby stroller, or a ride from San Francisco to LA and an overnight stay in someone’s spare room. It’s a far cry from shopping on Amazon, and checking for plane fares on JetBlue and shopping around for hotel bargains on Priceline – somehow morepersonal.

But make no mistake: it’s a business. And you forget that at your peril, regardless of how you’re participating in the sharing economy.

Here’s the bottom line: none of the businesses that have sprung up to serve the sharing economy are 501c3 non-profit entities. Rather, they are corporations whose goal is to make a profit out of a much less formal sharing economy that already existed. Long before Airbnb was launched in 2008, a friend of mind traveled across Europe using a couch-surfing style network called Servus. I’ve formed some lasting friendships with people with a free Airbnb-style network, Hospitality Club, that offers hosts and guests the chance to review each other, Airbnb style. Airbnb has just formalized those arrangements, while ride-sharing companies like BlaBla Car have done the same with those old-fashioned ride share boards on walls or online – and build in a profit for the middleman.

But you don’t get to become one of the most valuable venture capital-based businesses in the world, as Airbnb has done, and to be worth an estimated $10bn (more than some hotel chains) if all you are is part of a “movement”. Nope, you have to have found a way to make being the middleman pay off very handsomely indeed – and that’s capitalism 101, not a movement.

All of which means that if you’re doing business with Airbnb – or Uber, or Parking Panda, or Liquid, or any of the other sharing economy enterprises springing up – you need to think of it in those terms, too.

First of all, while you may think of this as just generating a bit of extra income on the side – a way to pay off your student loans, to make your summer vacation pay for itself, to fund your weekends out with friends or to help save up to pay for a wedding or a downpayment for your house or car – the IRS won’t see it that way.

And if you think the IRS won’t ever know, well, let me disabuse you of that right now. You’ll fill out tax forms – and come January, you’ll get a 1099 form. Depending on the figure on it, you may end up kissing your expected refund goodbye, or facing an unexpected tax liability. If that 1099 form doesn’t show up? Don’t heave a sigh of relief and fail to report that income. If you think an unexpected tax liability is bad, getting on the wrong side of the IRS is exponentially worse.

The best idea of all is to talk to your accountant and ask for their input. At what point does sharing economy income change your tax picture by putting you in a higher tax bracket? Are there any additional writeoffs you should be aware of? Sure, this might cost you an hour of her time – but it could save you a lot of money down the road. And remember, you’re thinking of this as a business – just like the Airbnbs, Ubers and others who are quite happy to scoop up a percentage of what you collect.

Before you delve into the sharing economy, consider the regulations governing the micro-business that you’re choosing to enter and how they might affect you. In New York, for instance, it’s illegal to rent out a room in your apartment unless you’re there during the guest’s stay; generally, apartment rentals of under 30 days are illegal. (Depending on who you ask, this is an attempt either to make sure housing stock remains available to people who want to live in it, or a result of fierce lobbying by the hotel industry.) That doesn’t stop people from publicly violating both the law and the terms of their own leases – but Airbnb has made it crystal clear that they are on their own when it comes to sorting out those problems. So if you’ve got a landlord – or neighbors – who you know are just itching to bid you farewell for whatever reason, handing them an ironclad reason to do so might be foolhardy.

(Meanwhile, Airbnb is confronting some of these issues itself: this past week Barcelona slapped a fine on the company for violating laws that require rooms rented to tourists be registered with government authorities.)

What does set this new breed of business apart from its peers and predecessors is the emphasis on collaboration: hence, the alternative moniker of “collaborative consumption”. That’s a reason to assume that it’s less businesslike in nature (just as the Internet startups of yore were no less focused on making millions just because their founders wore khakis instead of suits). What it means for those of us hoping to make a much smaller amount of money alongside the capitalist creators of these businesses is that marketing may matter much more than before. Expectations are pretty low for customer “service” from traditional businesses; they’re higher from your peers in the sharing economy community who will be rating things like the cleanliness of your home and the promptness with which you respond to queries.

The “sharing economy” isn’t going anywhere, and the temptation to become a micro-entrepreneur is only going to grow. But if you’re on the verge of succumbing to temptation, ask yourself whether you’re ready to view this as a business. If not, you’re probably not ready to deal with the risks you’ll be taking onboard along with the much more widely touted rewards.

http://www.alternet.org/economy/thinking-trying-make-money-airbnb-or-uber-read-first?akid=12016.265072.GVVEly&rd=1&src=newsletter1011288&t=13&paging=off&current_page=1#bookmark

Here are the states where you are most likely to be wiretapped

According to the Administrative Office of the U.S. Court’s Wiretap Report, here’s where wiretapping occurs the most

 

Here are the states where you are most likely to be wiretapped

In terms of wiretapping — with a warrant — it turns out some states use the tactic far more than others.

The Administrative Office of the U.S. Court released its “Wiretap Report” for the year 2013, and it turns out that Nevada, California, Colorado and New York account for nearly half of all wiretap applications on portable devices in the United States. Add in New Jersey, Georgia and Florida and you have 80 percent of the country’s applications for wiretaps. A chart from Pew Research can be viewed here.

Overall, according to the report, wiretaps were up in 2013:

“The number of federal and state wiretaps reported in 2013 increased 5 percent from 2012. A total of 3,576 wiretaps were reported as authorized in 2013, with 1,476 authorized by federal judges and 2,100 authorized by state judges.”

The report also found that in terms of federal applications The Southern District of California was responsible for 8 percent of the applications, approved by federal judges — the most by a single district in the country.

In terms of the nation, Pew Research reports:

“When we factor in population, Nevada leads the nation with 38 mobile wiretaps for every 500,000 people. Most Nevada wiretaps (187) were sought by officials in Clark County, home to Las Vegas; federal prosecutors in the state obtained authorization for 26 more, though only one was actually installed.”

The overwhelming majority of the wiretaps, nationwide — 90 percent, according to Pew Research — were requested to monitor drug-related criminal activity. Pew also reported that the wiretaps resulted in 3,744 arrests and 709 convictions.

Most of the wiretaps were for “portable devices” which included mobile phones and digital pagers, according to the report.



The states where no wiretaps were requested include Hawaii, Montana, North Dakota, South Dakota and Vermont.

Of course, the report only highlights wiretaps that require a warrant, and not those done without.

h/t Gizmodo, Pew Research, U.S. Courts

 

http://www.salon.com/2014/07/14/here_are_the_sates_where_you_are_most_likely_to_be_wiretapped/?source=newsletter