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Shifting attitudes among Democrats have big implications for 2020

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eviatarbach
12 days ago
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Sixties Scoop compensation excludes Métis, non-status Indigenous Peoples - CBC News

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An agreement in principle to directly compensate Sixties Scoop survivors has been reached between the federal government and the plaintiffs in a class-action lawsuit, Minister of Crown-Indigenous Relations and Northern Affairs Carolyn Bennett announced Friday.

However, the claim will leave out groups like Métis and non-status Indigenous Peoples, said Jeffery Wilson, the lawyer who represented the Ontario survivors in the class action suit.

For those wondering if they will qualify for the settlement, lawyer Jeffery Wilson broke down some key points:

  • The agreement applies to those adopted or fostered between 1951 and 1991.
  • The agreement will apply to anyone in Canada.
  • It will apply to anyone adopted to another country.
  • Indigenous people who were wards of the Crown during the period covered by the settlement will qualify.
  • Those who were adopted or were in temporary care for a long period of time during the period covered by the settlement will qualify.
  • Claimants will not have to pay legal fees.

"[The claim] does not include Métis. It includes non-status [First Nations] … so long as they are eligible to be status," said Wilson. "But if they're non-status they are not, by that fact in and of itself, disqualified.

"The reason Métis are not included is because there is no records to identify Métis during the relevant period of time," he said.

Wilson has been working on the Ontario case for 13 years.

The agreement in principle will see $750 million go directly to people who were taken from their families during what is known as the Sixties Scoop, during which thousands of Indigenous children were taken from their homes and placed in non-Indigenous care over a period of decades.

Another $50 million has been earmarked for a foundation dedicated to reconciliation initiatives.

Depending on how many make a claim, survivors could each see between $25,000 to $50,000.

Exact details are still in the works for applying for compensation, but Wilson said the process will be simple.

"A person will file an application saying, 'I believe that I was removed, I didn't live with my parents or my nation,'" said Wilson.

Those eligible for compensation will have to get documents to prove they were Crown or permanent wards and were adopted out.

"If the provincial authorities don't have the records — which is very possible — then the process will enable them to declare for a statutory declaration," said Wilson.

Many Sixties Scoop adoptees were sent to different provinces, while others were shipped across North America and further abroad. Adoptees sent to other countries will be eligible to receive compensation as well, said Wilson.

"It doesn't matter where you were sent to as long as you were [adopted] between 1951 to 1991. That's a 40-year period that covers eligibility," said Wilson.

'The process is going to be kept very simple. Lawyers cannot charge for any work that is done.' - Jeffery Wilson

"The process is going to be kept very simple. Lawyers cannot charge for any work that is done."

The government is putting aside an additional $75 million to cover claimants' legal fees.

Friday's announcement was an agreement in principle, which means the finer details of the settlement still need to be arranged.

There will, however, be a fund dedicated to reconciliation initiatives that will be open to anyone — including non-status First Nations, Inuit, and Métis people — who were adopted out during the Sixties Scoop.

Wilson said there is a debate about how compensation will work for adoptees who have since died.

"The question that has to be considered is — let's assume there was an adopted child who was a Sixties Scoop survivor [who] dies. You're not telling me that their adopted parents could come forward and claim?" said Wilson.

Non-First Nations adoptee parents are unlikely to be compensated for children they adopted who have since died, he added.

"There's an alternative argument that maybe this money should go to the foundation and this should be for the living people," said Wilson.

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eviatarbach
12 days ago
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Can we open the black box of AI? : Nature News & Comment

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AI black box animation

Illustration by Simon Prades

Dean Pomerleau can still remember his first tussle with the black-box problem. The year was 1991, and he was making a pioneering attempt to do something that has now become commonplace in autonomous-vehicle research: teach a computer how to drive.

This meant taking the wheel of a specially equipped Humvee military vehicle and guiding it through city streets, says Pomerleau, who was then a robotics graduate student at Carnegie Mellon University in Pittsburgh, Pennsylvania. With him in the Humvee was a computer that he had programmed to peer through a camera, interpret what was happening out on the road and memorize every move that he made in response. Eventually, Pomerleau hoped, the machine would make enough associations to steer on its own.

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Shamini Bundell finds out about AI’s black-box problem

On each trip, Pomerleau would train the system for a few minutes, then turn it loose to drive itself. Everything seemed to go well — until one day the Humvee approached a bridge and suddenly swerved to one side. He avoided a crash only by quickly grabbing the wheel and retaking control.

Back in the lab, Pomerleau tried to understand where the computer had gone wrong. “Part of my thesis was to open up the black box and figure out what it was thinking,” he explains. But how? He had programmed the computer to act as a 'neural network' — a type of artificial intelligence (AI) that is modelled on the brain, and that promised to be better than standard algorithms at dealing with complex real-world situations. Unfortunately, such networks are also as opaque as the brain. Instead of storing what they have learned in a neat block of digital memory, they diffuse the information in a way that is exceedingly difficult to decipher. Only after extensively testing his software's responses to various visual stimuli did Pomerleau discover the problem: the network had been using grassy roadsides as a guide to the direction of the road, so the appearance of the bridge confused it.

Twenty-five years later, deciphering the black box has become exponentially harder and more urgent. The technology itself has exploded in complexity and application. Pomerleau, who now teaches robotics part-time at Carnegie Mellon, describes his little van-mounted system as “a poor man's version” of the huge neural networks being implemented on today's machines. And the technique of deep learning, in which the networks are trained on vast archives of big data, is finding commercial applications that range from self-driving cars to websites that recommend products on the basis of a user's browsing history.

It promises to become ubiquitous in science, too. Future radio-astronomy observatories will need deep learning to find worthwhile signals in their otherwise unmanageable amounts of data; gravitational-wave detectors will use it to understand and eliminate the tiniest sources of noise; and publishers will use it to scour and tag millions of research papers and books. Eventually, some researchers believe, computers equipped with deep learning may even display imagination and creativity. “You would just throw data at this machine, and it would come back with the laws of nature,” says Jean-Roch Vlimant, a physicist at the California Institute of Technology in Pasadena.

But such advances would make the black-box problem all the more acute. Exactly how is the machine finding those worthwhile signals, for example? And how can anyone be sure that it's right? How far should people be willing to trust deep learning? “I think we are definitely losing ground to these algorithms,” says roboticist Hod Lipson at Columbia University in New York City. He compares the situation to meeting an intelligent alien species whose eyes have receptors not just for the primary colours red, green and blue, but also for a fourth colour. It would be very difficult for humans to understand how the alien sees the world, and for the alien to explain it to us, he says. Computers will have similar difficulties explaining things to us, he says. “At some point, it's like explaining Shakespeare to a dog.”

Faced with such challenges, AI researchers are responding just as Pomerleau did — by opening up the black box and doing the equivalent of neuroscience to understand the networks inside. Answers are not insight, says Vincenzo Innocente, a physicist at CERN, the European particle-physics laboratory near Geneva, Switzerland who has pioneered the application of AI to the field. “As a scientist,” he says, “I am not satisfied with just distinguishing cats from dogs. A scientist wants to be able to say: 'the difference is such and such'.”

Good trip

The first artificial neural networks were created in the early 1950s, almost as soon as there were computers capable of executing the algorithms. The idea is to simulate small computational units — the 'neurons' — that are arranged in layers connected by a multitude of digital 'synapses' (see 'Do AIs dream of electric sheep?') Each unit in the bottom layer takes in external data, such as pixels in an image, then distributes that information up to some or all of the units in the next layer. Each unit in that second layer then integrates its inputs from the first layer, using a simple mathematical rule, and passes the result further up. Eventually, the top layer yields an answer — by, say, classifying the original picture as a 'cat' or a 'dog'.

Design: Nik Spencer/Nature; Photos: Keith McDuffee/flickr/CC BY; djhumster/flickr/CC BY-SA; Bernard Dupont; Linda Stanley; Phil Fiddyment/Flickr/CC BY

The power of such networks stems from their ability to learn. Given a training set of data accompanied by the right answers, they can progressively improve their performance by tweaking the strength of each connection until their top-level outputs are also correct. This process, which simulates how the brain learns by strengthening or weakening synapses, eventually produces a network that can successfully classify new data that were not part of its training set.

That ability to learn was a major attraction for CERN physicists back in the 1990s, when they were among the first to routinely use large-scale neural networks for science: the networks would prove to be an enormous help in reconstructing the trajectories of subatomic shrapnel coming out of particle collisions at CERN's Large Hadron Collider.

But this form of learning is also why information is so diffuse in the network: just as in the brain, memory is encoded in the strength of multiple connections, rather than stored at specific locations, as in a conventional database. “Where is the first digit of your phone number stored in your brain? Probably in a bunch of synapses, probably not too far from the other digits,” says Pierre Baldi, a machine-learning researcher at the University of California, Irvine. But there is no well-defined sequence of bits that encodes the number. As a result, says computer scientist Jeff Clune at the University of Wyoming in Laramie, “even though we make these networks, we are no closer to understanding them than we are a human brain”.

To scientists who have to deal with big data in their respective disciplines, this makes deep learning a tool to be used with caution. To see why, says Andrea Vedaldi, a computer scientist at the University of Oxford, UK, imagine that in the near future, a deep-learning neural network is trained using old mammograms that have been labelled according to which women went on to develop breast cancer. After this training, says Vedaldi, the tissue of an apparently healthy woman could already 'look' cancerous to the machine. “The neural network could have implicitly learned to recognize markers — features that we don't know about, but that are predictive of cancer,” he says.

But if the machine could not explain how it knew, says Vedaldi, it would present physicians and their patients with serious dilemmas. It's difficult enough for a woman to choose a preventive mastectomy because she has a genetic variant known to substantially up the risk of cancer. But it could be even harder to make that choice without even knowing what the risk factor is — even if the machine making the recommendation happened to be very accurate in its predictions.

“The problem is that the knowledge gets baked into the network, rather than into us,” says Michael Tyka, a biophysicist and programmer at Google in Seattle, Washington. “Have we really understood anything? Not really — the network has.”

Several groups began to look into this black-box problem in 2012. A team led by Geoffrey Hinton, a machine-learning specialist at the University of Toronto in Canada, entered a computer-vision competition and showed for the first time that deep learning's ability to classify photographs from a database of 1.2 million images far surpassed that of any other AI approach1.

Digging deeper into how this was possible, Vedaldi's group took algorithms that Hinton had developed to improve neural-network training, and essentially ran them in reverse. Rather than teaching a network to give the correct interpretation of an image, the team started with pretrained networks and tried to reconstruct the images that produced them2. This helped the researchers to identify how the machine was representing various features — as if they were asking a hypothetical cancer-spotting neural network, 'What part of this mammogram have you decided is a marker of cancer risk?”

“The problem is that the knowledge gets baked into the network, rather than into us.”

Last year, Tyka and fellow Google researchers followed a similar approach to its ultimate conclusion. Their algorithm, which they called Deep Dream, starts from an image — say a flower, or a beach — and modifies it to enhance the response of a particular top-level neuron. If the neuron likes to tag images as birds, for example, the modified picture will start showing birds everywhere. The resulting images evoke LSD trips, with birds emerging from faces, buildings and much more. “I think it's much more like a hallucination” than a dream, says Tyka, who is also an artist. When he and the team saw the potential for others to use the algorithm for creative purposes, they made it available to anyone to download. Within days, Deep Dream was a viral sensation online.

Using techniques that could maximize the response of any neuron, not just the top-level ones, Clune's team discovered in 2014 that the black-box problem might be worse than expected: neural networks are surprisingly easy to fool with images that to people look like random noise, or abstract geometric patterns. For instance, a network might see wiggly lines and classify them as a starfish, or mistake black-and-yellow stripes for a school bus. Moreover, the patterns elicited the same responses in networks that had been trained on different data sets3.

Researchers have proposed a number of approaches to solving this 'fooling' problem, but so far no general solution has emerged. And that could be dangerous in the real world. An especially frightening scenario, Clune says, is that ill-intentioned hackers could learn to exploit these weaknesses. They could then send a self-driving car veering into a billboard that it thinks is a road, or trick a retina scanner into giving an intruder access to the White House, thinking that the person is Barack Obama. “We have to roll our sleeves up and do hard science, to make machine learning more robust and more intelligent,” concludes Clune.

Issues such as these have led some computer scientists to think that deep learning with neural networks should not be the only game in town. Zoubin Ghahramani, a machine-learning researcher at the University of Cambridge, UK, says that if AI is to give answers that humans can easily interpret, “there's a world of problems for which deep learning is just not the answer”. One relatively transparent approach with an ability to do science was debuted in 2009 by Lipson and computational biologist Michael Schmidt, then at Cornell University in Ithaca, New York. Their algorithm, called Eureqa, demonstrated that it could rediscover the laws of Newtonian physics simply by watching a relatively simple mechanical object — a system of pendulums — in motion4.

Starting from a random combination of mathematical building blocks such as +, −, sine and cosine, Eureqa follows a trial-and-error method inspired by Darwinian evolution to modify the terms until it arrives at the formulae that best describe the data. It then proposes experiments to test its models. One of its advantages is simplicity, says Lipson. “A model produced by Eureqa usually has a dozen parameters. A neural network has millions.”

On autopilot

Last year, Ghahramani published an algorithm that automates the job of a data scientist, from looking at raw data all the way to writing a paper5. His software, called Automatic Statistician, spots trends and anomalies in data sets and presents its conclusion, including a detailed explanation of its reasoning. That transparency, Ghahramani says, is “absolutely critical” for applications in science, but it is also important for many commercial applications. For example, he says, in many countries, banks that deny a loan have a legal obligation to say why — something a deep-learning algorithm might not be able to do5.

Similar concerns apply to a wide range of institutions, points out Ellie Dobson, director of data science at the big-data firm Arundo Analytics in Oslo. If something were to go wrong as a result of setting the UK interest rates, she says, “the Bank of England can't say, 'the black box made me do it'”.

Despite these fears, computer scientists contend that efforts at creating transparent AI should be seen as complementary to deep learning, not as a replacement. Some of the transparent techniques may work well on problems that are already described as a set of abstract facts, they say, but are not as good at perception — the process of extracting facts from raw data.

Ultimately, these researchers argue, the complex answers given by machine learning have to be part of science's toolkit because the real world is complex: for phenomena such as the weather or the stock market, a reductionist, synthetic description might not even exist. “There are things we cannot verbalize,” says Stéphane Mallat, an applied mathematician at the École Polytechnique in Paris. “When you ask a medical doctor why he diagnosed this or this, he's going to give you some reasons,” he says. “But how come it takes 20 years to make a good doctor? Because the information is just not in books.”

To Baldi, scientists should embrace deep learning without being “too anal” about the black box. After all, they all carry a black box in their heads. “You use your brain all the time; you trust your brain all the time; and you have no idea how your brain works.”

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All Worked Up and Nowhere to Go

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I did not anticipate how the death of the radical British writer and theorist Mark Fisher would haunt me, but I am reminded of him more and more often, and I find myself returning to his work regularly. And it’s not just me. Recently, a magazine editor asked if I would cowrite an autopsy of contemporary radical activism; we both felt a postmortem was needed before a reanimated left could emerge to fight capital and seize power. “We could be like Mark Fisher!” he said excitedly. “We could tell the hard truths!” I had to remind him not only that neither of us is half as smart as Fisher, but that the “hard truths” essay the editor was referring to got Fisher crucified by his peers. And that Mark Fisher had recently committed suicide.

Although Fisher’s work demonstrates a sprawling awareness of life deranged by capitalism, he is best remembered for the prescient, infamous essay “Exiting the Vampire Castle,” which infuriated much of the self-identified left by arguing that a shallow and noxious liberal identity critique, delivered mostly on the internet, was being used to undermine class politics and paralyze left discourse. I remember not thinking too much of his diagnosis at the time, which was late 2013, agreeing with some points, but not buying in wholesale. Later I realized it was spot-on, a preview of the farcically doomed Clinton campaign; but by then Fisher had been written off as a “toxic” white brocialist, a man doing “violence” to the “most vulnerable” people in “the movement.” Even worse, after Fisher died at forty-eight in January of this year, he was still being denounced by po-faced critics for his frankly gracious critique of the left. And I’m talking right after his death—within hours of the information going public.

The Trump administration has rekindled the internal hysteria that Fisher warned against. And though it was heartening, the first wave of solidarity marches and general actions is now fading into memory; we’re left with a familiar hostility, a recurring bad faith that so recently has smeared greater minds and gentler hearts than my own. The economic ambitions of the so-called “Sanders Effect” appear to have waned, and the focus has predictably turned to the glittering, bilious spectacle of Trumpism. Just as Trump remade politics as television, we’ve allowed political action to mimic the spiteful, futile patterns of online bickering: our fellow anti-capitalists betray us all by enjoying or creating the wrong art, reading the wrong articles, championing the wrong theories, or even laughing at the wrong jokes. The left is at once flailing and sclerotic. Afflicted by a vague autoimmune disorder, we cannot even retain what little power we have, nor do we have any institutions capable of doing so; thus, we are able to smack only those within arm’s reach of us—ourselves. Meanwhile, the bigger and stronger the right gets, the more insular we become, single-mindedly obsessed with purifying our own ranks and weeding out the problematic among us. Of course, the left requires large portions of the problematic and disparate working class to sign on, but the range of acceptable comradely thinking is becoming ever-stricter, and “deviants are sacrificed to increase group solidarity,” as the artist Jenny Holzer warned.

The self-appointed Trump Resistance is stuck in a compulsive loop, perseverating on symptoms and self-help rather than tackling the disease.

Marxist writer David Harvey notes that even Warren Buffett acknowledges the neoliberal era is marked by a one-sided class war, waged only by the capitalists. (“Sure there is class war, and it is my class, the rich, who are making it and we are winning,” Buffett has said.) The left lies sputtering on the mat, unable to maintain its ground, much less make any material gains. It’s hard to disagree when our gestures lack bite and our political parties—and most of our unions—are feckless at best, and capitalist quislings at worst. Whether it takes the form of insular campus activism, reactionary internet sermonizing, or impotent calls for general action, what passes for “the left” today is both parochial and completely disconnected from power. To put it bluntly, we have lost; we are decimated and we are feeble. What’s worse, we refuse to admit our failures, repeating them over and over and over again, castigating anyone who might question this pattern. In “Exiting the Vampire Castle,” Fisher alerted us to a “witch-hunting moralism”—in this case, against anyone who might try to raise class consciousness—that inevitably devolves into guilt and ineffectuality. In the wake of the election, it’s a lesson that seems to have gone largely unlearned by a self-sabotaging left.

Scabs and Flirts

I was introduced to the idea of a Women’s Strike while speaking on a panel of leftist feminists shortly after Trump was elected. During the Q&A afterwards, a feminist from the audience took the microphone and delivered an impassioned speech. Among the things participants were to abstain from:

Paid jobs

Emotional Labor

Childcare

Diapers

Housework

Cooking

Sweeping

Laundry

Dishes

Errands

Groceries

Fake smiles

Flirting

Makeup

Laundry

Shaving

At the end of her speech, I jokingly asked if I was allowed to flirt with other women during the strike, or if that would be scabbing—I did not get a laugh. Of course, tensions were high and good humor was in short supply, but there was also something genuinely irksome about the perceived usefulness of such a “strike,” and my glibness betrayed my skepticism.

For one, general strikes require a massive amount of organizing, and the proposed date for the strike was a few short months away. Also, the National Planning Committee was much heavier on academics and writers than on labor organizers. And if the turnout was low, would anyone even notice? (If a tree strikes in the woods, where no boss is there to feel it, can the tree really get the goods?) These questions were frustratingly overshadowed by criticisms from liberals insisting that only the “privileged” women would be striking. This framing, of course, misses the point; the success of a strike is not dependent on the relative “privilege” of the workers participating, but in the chaos those workers can inflict by withholding their labor.

Capitalism doesn’t actually give a shit about your unpaid emotional labor. It’s kind of a bro like that.

Striking works because it fucks up someone’s day, but whose day would the participants of the Women’s Strike affect? Would the event, billed as “A Day Without Women,” amount to anything more than a day without adjuncts and freelance graphic designers? As an adjunct myself, I believe my job is important, but if I’m being perfectly honest, no one notices when I don’t show up for one day of work. It costs no money, and it doesn’t plunge the university into chaos, and without cost or chaos, a strike is an impotent performance.

In my little lefty circles, these concerns were not received graciously. Men who questioned the strike’s utility were branded sexist; women who did the same were simply ignored. It was reminiscent of the Hillary campaign’s rhetoric: every feminist who didn’t fall in line was suddenly invisible; every man with a criticism of a woman was suddenly manifesting a deep-seated and pathological misogyny. When I asked my more enthusiastic comrades why I should be striking, or what I would even be striking for, the best answer I got was “Why not? We’re just trying to see what sticks.” The worst I got was silence. There were a lot of passive-aggressive Facebook manifestos about how lefties who questioned the action were just scared, or closet liberals, or worse, “scabs.”

As early as January, many leftists expressed skepticism about calls for a general strike, but by March there was a self-justifying urgency to defend the tactic against all doubts. Maybe it was due to the reorienting of the action as a “Women’s Strike”—no one wants to be called a brocialist or a mansplainer—but I think the bigger culprit was in our general panic. We are living in an era of Post-Trump Hysteria. It’s scary out there, and so we cling to the delusion that what we are doing is working. The naysayers, the thinking goes, must be politically backward or reactionary; we should be quick to root them out. Meanwhile, the world goes on.

In the end, I called off my classes. I told myself I was setting an example for my students, but I still put “Women’s Strike” in quotation marks when I explained why class was cancelled. I told myself the students were critical thinkers, and that it would do them good to see a politically active teacher; but really, I cancelled class for the same reason I do so many fruitless and potentially self-destructive things—so that no one can call me a coward. In the meantime, I peeked at the rally; it was small by New York standards. Weeks later, I still saw colleagues and comrades defending the action as “radical.” Some were denouncing those who considered the strike a failure—even those who went on strike themselves—as insufficiently supportive of this promising new vanguard of women college professors.

The pervasive mood reminded me of church, and specifically the churches of my grandparents, who cycled through about a hundred tiny Protestant evangelical sects, each one seething with mistrust of its own parishioners. Belief, in those denominations, was fervent, and turnover was high. I grew up with a certain envy of Catholics and Jews, who are allowed to attend services regardless of their connection to God. For these evangelical Protestants, however, a loss of faith was considered a personal failing, and any hint of creeping atheism could get you purged, lest you infect the brethren with your demonic skepticism. The arbitrary piety was there, too. During the strike, I remembered when my Papaw tried to sell a car to my mother, but then refused to accept her check on a Sunday, since he couldn’t do business on the Sabbath. That event—like the Women’s Strike—was a strangely un-materialist initiative, one underwritten by the idea that we should abstain from work merely out of observance and reverence, and not to “get the goods.”

I still flirted that day. I have never understood this tactic of chastity, but then again, I’ve always viewed sex and romance as properly proletarian pursuits. (It never felt like work to me, but maybe I’ve been doing it wrong.) I also did my dishes. God might not want you to be prurient or fastidious on the day of rest, but capitalism doesn’t actually give a shit about your unpaid emotional labor. It’s kind of a bro like that.

What the Women’s Strike did reveal is that the self-appointed Trump Resistance is stuck in a compulsive loop, perseverating on symptoms and self-help rather than tackling the disease. The “battles” you see making headlines in our claustrophobic community have become microscopically petty: Who speaks at what campus? Who made what problematic joke? Which left magazine has a bad take and who will “take responsibility”? None of these squabbles are politics; none of them build power. I’m sorry to say, even punching the odd Nazi doesn’t build power. (It raises spirits, but little else.) We’re forever resting on the laurels of feel-good symbolic outcry rather than the material victories that make our day-to-day lives better. It suits the ruling class just fine.

In his 2009 barnburner Capitalist Realism: Is There No Alternative?, Fisher diagnosed this rut as an acceptance of our own political futility:

Since [the anti-capitalist movement] was unable to posit a coherent alternative political economic model to capitalism, the suspicion was that the actual aim was not to replace capitalism but to mitigate its worst excesses; and, since the form of its activities tended to be the staging of protests rather than political organization, there was a sense that the anti-capitalism movement consisted of making a series of hysterical demands which it didn’t expect to be met.

These lines come from the second chapter, titled “What If You Held a Protest and Everyone Came?” The Women’s Strike listed in its platform: “An End to Gender Violence,” “Reproductive Justice for All,” “Labor Rights,” “Full Social Provisioning,” and “Environmental Justice for All.” If those are the expectations of the Women’s Strike, they are exactly of the kind Fisher describes—the sort you never expect to be met. Conversely, if the platform wasn’t listing demands, it was a strike without demands, which means it was not a strike at all, but a rally.

Rallies are fine. I’m not suggesting we retire the rally, but let’s remember what political theater actually does and does not accomplish: marches are for morale, protests are for pathos, but strikes? Strikes are for getting the goods, and that requires organizing workers. The hub of political power is not academia; it is not the internet; it is not the media, or comedy, or romance, or friendship, or art, or theory. It’s the workplace. And however “deviant” or unwanted this message may be, there are workers—mostly ignored by the broader left—who are nonetheless transmitting it loud and clear.

The Deft and the Militant

It was difficult getting ahold of Bhairavi Desai. She’s busy. The New York Taxi Workers Alliance is very active, very understaffed, and very underfunded, but it’s a force to be reckoned with, even though the majority of its more than 19,000 rank-and-file drivers feel the exacerbated sting of institutionalized racism under Trump.

On January 27, Trump issued an executive order crookedly titled “Protecting the Nation from Foreign Terrorist Entry into the United States.” The NYTWA was administering an exam for a driving class at the time, and around forty members were gathered in the office, alarmed by what they immediately recognized as a Muslim ban. Phone calls poured in. Drivers were angry and scared; they turned to their union first. The next day the union tweeted “NO PICKUPS @ JFK Airport 6 PM to 7 PM today. Drivers stand in solidarity with thousands protesting inhumane & unconstitutional #MuslimBan.” With a useful tweet—uncharacteristic of the medium—the strike went public.

The anti-ban rally at JFK was a big story, and with good reason—it was an uplifting sight. Somewhat underreported, though, was the labor action that helped stir the beautiful chaos. Any New Yorker will tell you getting to or from an airport is an absolute nightmare—taxis are essential. In less than twenty-four hours, the NYTWA threw a fat wrench in the daily functioning of an international airport, marooning travelers in a rapidly expanding and unruly crowd. Off-duty drivers even showed up to hold down the lot. Uber tried to scab, of course, but everyone saw through it, and a mass of customers deleted their app in response. It was only an hour, but an hour was all it took. The protest combined with the taxi strike is what broke JFK.

Though the protest got the brightest spotlight, the Taxi Workers did get plenty of attention on social media that night. When I asked Desai if she was shocked by the response, she said, “We were so caught up in the protest, we didn’t think anybody knew. It wasn’t until we got home late [that] night, after we had lost our voices, that we knew people were talking about it. We were blown away.”

Desai was shocked because NYTWA members are used to working unnoticed, even when they win (they do), and even when they fight hard (they always do). Still, it should surprise no one that they’ve shut down the airport before. In September 2015, a driver was assaulted by a dispatcher, and the union staged a sit-down. The Port Authority came with dogs, but the drivers refused to get up or move their cars, saying, “We move when the union tells us to move.”

On paper, the NYTWA looks like it’s poised to be devoured by neoliberalism. After a mere two years of organizing, it was officially formed in 1998—a younger union, born under the Clinton administration, hardly a golden era for organized labor. And 19,000 drivers may sound like a large membership, but compared to the big unions it’s a blip. What’s more, NYTWA members had been vulnerable workers even before Trump. For one thing, none of the drivers are legally classified as “employees,” making the NYTWA the only non-employee union with an AFL-CIO charter since the United Farm Workers of the 1960s. Without employee status, they aren’t guaranteed employee rights. Nor was the charter a windfall for the NYTWA; it was only supposed to protect them from other larger unions that might try to “poach” their drivers. Unfortunately, the charter raised the Taxi Workers’ profile, and they’ve experienced even more interference from other unions—one incident of a competing union organizing on their turf completely derailed a campaign in a large metropolitan area. Desai asked that I not name names so as not to exacerbate tensions between the two organizations.

The hub of political power is not academia; it is not the internet; it is not the media, or comedy, or romance, or friendship, or art, or theory. It’s the workplace.

Add to this the rise of “the sharing economy.” Not only do service apps like Uber spend millions to fight labor regulation, but Uber itself has run an attack campaign against the NYTWA, going so far as to send their drivers misinformation about the union thugs who might try to woo them. Meanwhile, other unions have cozied up to Silicon Valley by throwing their principles out the window. The Machinists Union struck a deal to form the Independent Drivers Guild, a pseudo-union that is in fact unilaterally operated by Uber itself.

Despite all of this, the NYTWA is vibrant and growing. Where others see walls, Desai sees challenges. We fret over the rise of the millennial “precariat,” but the nonemployee status of the drivers did not faze Desai—“organizers have to be creative,” she said. They are fighting Uber’s propaganda with their own information campaign, and they’re watching their backs, guarding against raids from other short-sighted, opportunistic unions. Most impressively, they flex labor muscle against state power, which is now firmly in the hands of the right.

It’s true that it was inspiring to see so many people standing against racism at an airport, which is essentially purgatory with Starbucks and duty-free booze. But what about the deft and militant saboteurs who monkey-wrenched everyone’s ride home? Watching the live feeds, my head was spinning, and the world was filled with promise again: We can still shut down ports. What else can we do?

A More Perfect Union

I have spent a lot of time trying to find a novel way of delivering this very cold take, but there’s just nothing new about the one and only prescription for socialism, however aberrant it sounds.

More than Twitter-style rhetoric, amputated “strikes,” and academic posturing, the left needs radical, militant unions with a political vision beyond the protection of their own rank and file. When the Muslim ban was declared, the drivers of the NYTWA immediately turned to their union, because they know it’s how they fight; that’s what unions need to be.

We need to be able to build labor coalitions and strike for real—meaning we can shut everything down until our demands are met. Sometimes this will be illegal. Sometimes workers will have to camp out and occupy workplaces. Sometimes they will have to sabotage machinery or bully the boss. Maybe there are gains to be had in local elections, but even as Bernie Sanders is currently the most popular politician, the Democrats still seem hell-bent on fighting winners and running losers. If we’re ever going to have any sway in electoral politics, we need union muscle. For his part, the Marxist NYU professor, writer, and sociologist Vivek Chibber put it nicely in “Why We Still Talk About the Working Class”:

The working class is unlike any other social grouping in the non-capitalist section of modern society. However penurious it is, however dominated it is, however atomized it is, it is the goose that lays the golden egg. It is the source of profits, because unless workers show up to do their work every day and create profits for their employers, that principle of profit maximization cannot be carried out. It remains a dead letter.

It’s true that many traditional labor unions are backward or weak; some will need an overhaul. After a notoriously failed strike effort, the Communications Workers of America cleaned house, replaced an incompetent leadership, assessed their failure, and regrouped. (It led to a successful strike against Verizon in 2016, one that yielded 1,300 new jobs and a 10.5 percent raise over four years.) Other unions, like the aforementioned Machinists, must be gutted entirely, their membership reorganized into new institutions. Mostly, though, we need to start organizing the unorganized (i.e., most workers) and focus heavily on strategic points of employment. As much as it would flatter my ego to believe otherwise, I am not at a particularly strategic point; I’m an adjunct professor at a private university, and even when we all strike, it’s only a problem for our little university microcosm.

But take heart, fellow atomized and expendable neoliberal subjects: there is a place for us in the coming wars! The microcosms still need to be organized (every bit helps), and established unions can be refreshed and steered toward radical ends. Nevertheless, I regret to inform you that much of this endeavor will be quite dull. Organizing is not usually as invigorating as rallying; it’s mostly meetings, planning, phone calls, emails, spreadsheets—you know, women’s work. There are a lot of tedious administrative tasks that go into forming and maintaining a union, and the work is rarely as romantic or cinematic as a bunch of taxi drivers locking down JFK. But those moments do happen. They’re sustaining, and they compound one another. Only labor can make it happen. Only workers can shut down production. Only workers can close the ports. Only workers can take capital hostage and make the whole world stand still.

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eviatarbach
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As Google Fights Fake News, Voices on the Margins Raise Alarm

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“They’re really skating on thin ice,” said Michael Bertini, a search strategist at iQuanti, a digital marketing agency. “They’re controlling what users see. If Google is controlling what they deem to be fake news, I think that’s bias.”

Despite Google’s insistence that its search algorithm undergoes a rigorous testing process to ensure that its results do not reflect political, gender, racial or ethnic bias, there is growing political support for regulating Google and other tech giants like public utilities and forcing it to disclose how exactly its arrives at search results.

Most people have little understanding of how Google’s search engine ranks different sites, what it chooses to include or exclude, and how it picks the top results among hundreds of billions of pages. And Google tightly guards the mathematical equations behind it all — the rest of the world has to take their word that it is done in an unbiased manner.

“The complexity of ranking and rating is always going to lead to some lack of understanding for people outside of the company,” said Frank Pasquale, an information law professor at the University of Maryland. “The problem is that a lot of people aren’t willing to give them the benefit of the doubt.” In his book, “The Black Box Society,” Mr. Pasquale warned about the potential risks from an overreliance on secret algorithms that control what information we see and how critical decisions are made.

As the dominant search engine, with an estimated 90 percent global market share, Google was criticized by both the right and the left of the political world during the 2016 election.

In June 2016, a video from the pop culture site SourceFed accused Google of manipulating automatically completed search suggestions to favor Hillary Clinton. Google denied the claim, but right-wing media seized on the video as an example that the company was tipping the scales in her favor.

In the days after the election, the top Google search results for “final election vote count 2016” was a link to a story that wrongly stated that Mr. Trump, who won the Electoral College, had also defeated Mrs. Clinton in the popular vote.

In the research that led to the creation of Project Owl, Google found that a small fraction of its search results — about 0.25 percent of daily traffic — were linking to intentionally misleading, false or offensive information. For a company that aims to deliver the most relevant information for all queries, that constituted a crisis.

Google said it had added more detailed examples of problematic pages into the guidelines used by human raters to determine what is a good search result and what is a bad one. Google said its global staff of more than 10,000 raters do not determine search rankings, but their judgments help inform how the algorithm performs in the future.

Google has often said that it cannot reveal too much or people would use that information to try to game the rankings. The opacity around Google’s algorithm has given birth to a cottage industry of search engine optimization experts who dissect the company’s comments.

To assuage criticism about that lack of transparency, Google made public its guidelines for search quality in 2013. Pandu Nayak, a Google fellow who focuses on search quality, said disclosing the guidelines is more meaningful.

“The actual algorithm is not as important as what the algorithm is trying to do,” said Mr. Nayak. “Being completely transparent of what you’re trying to achieve is the central goal because how you accomplish that can change.”

Google said hundreds of factors go into its search algorithm and the formula is also constantly evolving. The company said it conducted 150,000 search experiments and implemented 1,600 changes last year.

This is why it’s hard to pinpoint exactly why search traffic plummets for a site like the World Socialist Web Site, which calls itself the “online newspaper of the international Trotskyist movement.” Mr. North, the site’s chairman, said traffic coming in from search is down 70 percent since April, citing data from Alexa, a web traffic analytics firm owned by <a href="http://Amazon.com" rel="nofollow">Amazon.com</a>.

In an open letter to Google last month, Mr. North traced his site’s traffic decline to Project Owl. Mr. North said he believed that Google was blacklisting the site, using concerns over fake news as a cover to suppress opinions from socialist, antiwar or left-wing websites and block news that Google doesn’t want covered.

In mid-April, a Google search for “socialism vs. capitalism” brought back one of the site’s links on the first results page but, by August, that same search didn’t feature any of its links. The site said 145 of the top 150 search terms that had redirected people to the site in April are now devoid of its links.

“They should be asked to explain how they’re doing it,” Mr. North said. “If they say we’re not doing anything, that’s simply not credible.”

Mr. North said that Google has not responded to his claims. Google declined to comment on the World Socialist Web Site.

Mr. North argued the drop-off in traffic is the result of Google directing users toward mainstream media organizations, including The New York Times. The World Socialist Web Site claimed that search referral traffic had fallen since April at a variety of other left-wing, progressive, socialist or antiwar publications like AlterNet and Consortiumnews.

The New York Times could not find the same level of traffic declines at all of those publications, based on data from SimilarWeb, a web analytics firm. Traffic coming from search engines for the World Socialist Web Site was down 34 percent during the months of May to July, compared with the preceding three months, according to SimilarWeb. Traffic that did not come from search was up 1 percent during the same period.

Mr. North said his site provides critical analysis for current events and it has nothing in common with sites peddling blatantly untrue stories. But he said he is opposed to any actions taken by Google under the pretext of stopping fake news.

“I’m against censorship in any form,” he said. “It’s up to people what they want to read. It’s not going to stop with the World Socialist Web Site. It’s going to expand and spread.”

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At least 761 injured as Spanish police try to stop vote on Catalan independence

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Clashes between police and protesters broke out Sunday as the Spanish National Police attempted to quash the Catalonia region’s referendum on independence, injuring hundreds of Catalan voters and protesters.

Voters throughout Catalonia went to the polls Sunday to participate in a vote for independence that the Spanish government has deemed illegal. The northeastern region of Spain, which includes Barcelona and houses 7.5 million people, is responsible for 20% of Spain’s economic output and has its own distinctive language and culture, Al Jazeera noted.

Pro-independence residents of the “autonomous community” say that the region provides more financial support to the Spanish government in Madrid than it receives in return. This is the second referendum on the question of Catalan independence, after a non-binding, unofficial vote in 2014.

“I have come to vote to defend the rights of my country, which is Catalonia,” 73-year-old retiree Joaquim Bosch told the Associated Press. “I vote because of the mistreatment of Catalonia by Spain for many years.”

Independence supporters take part in a rally to support Catalonia’s secession referendum, in Bilbao, northern Spain, on Sept. 30.

Source: Alvaro Barrientos/AP

According to the Guardian, 60% of Catalonia’s 5.3 million eligible voters were expected to turn out to Sunday’s vote. Although over 70% of the population is in favor of the referendum, however, their opinions on independence are more divided. Surveys conducted two months ago cited by the Guardian reveal that 49.4% of voters were in favor of remaining part of Spain, while 41.1% backed independence.

The Spanish government has been working to prevent the vote from taking place, saying that the referendum would be illegal as Spain’s 1978 constitution has no provisions for votes on self-determination. The Guardian reported the Spanish government has not only conducted raids and seized ballot papers, polling station signs and documents for electoral officials in the run-up to the election, but also limited the Catalan government’s finances and sealed off 1,300 of Catalonia’s 2,315 polling locations. The government also dismantled the technology to count the votes and vote online, according to the AP.

“These last-minute operations have allowed us to very definitively break up any possibility of the Catalan government delivering what it promised: a binding, effective referendum with legal guarantees,” Enric Millo, the Spanish government’s senior representative in Catalonia, said Saturday, as quoted by the Guardian.

“That’s what the Catalan government had promised to deliver on 1 October. Today, we can assure people that it will not go ahead.”

When voters showed up to defy the government and vote Sunday, camping out for days in polling locations to avoid having them shut down by the government, Spanish National Police fought back. Police burst into polling places, breaking down doors to forcibly remove voters and seize ballot boxes, the AP reported.

“We were waiting inside to vote when the National Police used force to enter, they used a mace to break in the glass door and they took everything,” Barcelona voter Daniel Riano told the AP, adding that “one policeman put me in a headlock to drag me out, while I was holding my wife’s hand.

“It was incredible. They didn’t give any warning,” Riano continued.

Police fired rubber bullets at Catalans during the scuffle, the AP noted, and the Catalan government reported that 761 people had been injured as a result of the police violence. The police have also been captured on video attacking Catalan firefighters who protected voters.

Spanish National Police push away pro-referendum supporters outside the Ramon Llull school assigned to be a polling station by the Catalan government in Barcelona, Spain, on Oct. 1.

Source: Emilio Morenatti/AP

Spanish National Police clash with pro-referendum supporters in Barcelona on Oct. 1.

Source: Manu Fernandez/AP

The Spanish government’s Interior Ministry also posted a video showing protesters throwing rocks at police vehicles in return, and according to NBC News, the government agency reported that nine officers and two civil guards had been injured.

Nevertheless, voting continued in many locations despite the ongoing police presence. Catalonian government spokesman Jordi Turull said that 96% of polling locations remained open as of 2:00 p.m. local time, NBC News reported, with some left unhindered by police interference.

People queue to vote outside a school assigned to be a polling station by the Catalan government at the Gracia neighborhood in Barcelona, Spain, on Oct. 1.

Source: Bob Edme/AP

A woman casts her vote in a school, assigned to be a referendum polling station by the Catalan government in Sant Julia de Ramis, near Girona, Spain, on Oct. 1.

Source: Francisco Seco/AP

World leaders and politicians have spoken out against the Spanish government’s violent response to Sunday’s referendum. Belgian Prime Minister Charles Michel called for “political dialogue” instead of violence on Twitter, while Scottish National Party leader Nicola Sturgeon wrote “we should all condemn the scenes being witnessed and call on Spain to change course before someone is seriously hurt.”

“Let the people vote peacefully,” Sturgeon continued.

October 1, 2017 2:23 p.m.: This story has been updated.

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eviatarbach
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