Posts Tagged ‘the algorithm’

“The phone rings and an eerie voice mimicking a woman says: “Your shift is about to start, please log in and make sure you are in your designated starting area.”

This automatic reminder to promptly start the shift was, for me, the most tangible reminder that couriers working for Foodora and similar “platform capitalism” companies operate via the control of algorithms. While getting a call from a robot urging to start your shift is uncanny, the algorithms control the couriers’ work in more subtle, and more important, ways.

Dispatching, namely the assignment of orders to couriers is done automatically at Foodora. Through the application that the couriers use to receive orders, the algorithm tracks the couriers’ location, average speed, how quickly the courier delivers the food and how much time they spend at the customer. Based on an unknown weighing of these factors, the algorithm assigns a specific order to a given courier.

The dispatcher, who distributes orders, plays probably the single most important part in a courier’s job. The courier plans their own routes, but the dispatcher gives the orders, sets the pace and provides the information the courier needs to do their job. No matter how fast a courier rides or how well they navigate the city, if dispatching does not work, nothing works. Conversely, when dispatchers and couriers work well together and communicate with each other, they deliver orders quickly and efficiently. When the dispatcher is a courier themselves, this cooperation usually works best, because the dispatcher knows what can be expected from a cyclist, how the weather, the load and distance affects the courier.

Foodora has human dispatchers, who oversee couriers in a given city. However, in the working process designed by Foodora, human dispatchers ideally don’t interact with couriers, who should get their orders automatically. Presumably as a cost-saving measure, Foodora centralized its dispatching to Berlin and the dispatchers overseeing say Helsinki know nothing about the city. Thus the dispatchers are not able to help couriers in problem cases and sometimes the results are just plain bizarre, for example when by mistake an order has registered to a restaurant that is in fact closed and the courier tries to tell disbelieving dispatchers in Berlin that the cannot pick it up, because… well, the restaurant is closed.

The biggest problem however is one of transparency. The provisions paid for the order form a substantial part of the couriers’ income at Foodora, and because of this, those who get more orders earn more. The courier however does not know how and why the algorithm distributes the orders to one courier instead of another. Apparently, the algorithm distributes orders to couriers it deems “effective”. I have seen a situation when a fast courier came exhausted with less than ten minutes of their shift remaining to the office where couriers, who had just started their shifts sat waiting for orders. Then a new order came and algorithm assigned it to the fast courier. Why, nobody knows, but in Foodora’s automatic system once an order is assigned it cannot be changed.

Similarly, the algorithm classes Foodora’s couriers into four “batches”, or groups, based on their performance (as judged by the algorithm). Shifts are made available in steps to these batches so that the first batch, with the “best” couriers, get first pick from all the shifts, then the next and those in the last batch pick any shifts that might be left. How a courier gets shifts obviously directly affects their income. If one can do only a limited amount of hours, one also earns less. Along with this direct effect, how much and how well one works affects also one’s position in the “batch” and the possibility to get shifts in the future.

In short, the algorithms directly control the couriers’ work and their income, but in ways the courier can only guess. Even if the courier was adept in reading the code and reverse-engineering the applications, the systems that manage them are proprietary and not made known to the courier.”

– Tuomas Tammisto,
“When Mr. Robot is Your Boss: Working under algorithms.” The Transnational Courier Federation (#4.2)

When Mr. Robot is Your Boss: Working under algorithms

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“The rise of ride-sharing services has increased traffic deaths by 2% to 3% in the US since 2011, equivalent to as many as 1,100 mortalities a year, according to a new study from the University of Chicago and Rice University.

How it was calculated: Researchers took statistics from the National Highway Traffic Safety Administration and compared them with the dates Uber or Lyft launched in a specific city. Then they checked accident rates in those cities relative to vehicle miles traveled. That rate shot up in San Francisco after Uber launched in 2010, a phenomenon that was replicated in other cities.

Deadheading: The increase in congestion is partly because drivers spend 40% to 60% of their time searching for passengers, a practice known as “deadheading.” On average, drivers in New York City traveled 2.8 miles between fares.

Before ride-sharing: Traffic deaths fell to their lowest number just before Uber launched in San Francisco. In 2010 there were 32,885 fatal car accidents nationwide, the lowest number since 1949. This decline halted and then reversed after the introduction of ride-sharing in US cities. However, it “may be too soon to tell whether the effect we document is a short-term adjustment or a longer-term pattern,” the researchers said.

Piling up problems: The study adds to a growing body of research on ride-sharing companies. Recent studies have found they increase congestion and cut the use of public transport. Cities are starting to respond to harms, perceived or otherwise. New York’s city council introduced a cap on ride-sharing in August, for example.”

– Charlotte Jee, “Uber and Lyft are behind a sharp rise in US traffic deaths.” The Download, MIT Technology Review. October 25, 2018.

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“The necessary condition for the reign of the GAFA (Google, Apple, Facebook, Amazon) is that beings, places, fragments of the world remain without any real contact. Where the GAFA claim to be “linking up the entire world,” what they’re actually doing is working toward the real isolation of everybody. By immobilizing bodies. By keeping everyone cloistered in their signifying bubble.

The power play of cybernetic power is to give everyone the impression that they have access to the whole world when they are actually more and more separated, that they have more and more “friends” when they are more and more alone. The serial crowd of public transportation was always a lonely crowd, but people didn’t transport their personal bubble along with them, as they have done since smartphones appeared. A bubble that immunizes against any contact, in addition to constituting a perfect snitch.

This separation engineered by cybernetics pushes in a non-accidental way in the direction of making each fragment into a little paranoid entity, towards a drifting of the existential continents where the estrangement that already reigns between individuals in this “society” collectivizes ferociously into a thousand delirious little aggregates.

In the face of all that, the thing to do, it would seem, is to leave home, take to the road, go meet up with others, work towards forming connections, whether conflictual, prudent, or joyful, between the different parts of the world. Organizing ourselves has never been anything else than loving each other.”

comité invisible, “50 Nuances of Breakage,” from Now. 2017.

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“The ongoing force of racism cannot be denied and the liberal carceral state is not an exception, as it provides ample evidence that its very structure is contingent on and advances a racist, particularly anti-black agenda. However, the carceral logic of capitalism has become increasingly focused on the most vulnerable, who, more often than not, are also the poorest. Along this line, you write that “the use of debt as a mechanism of dispossession requires that subjects first be incorporated into the capitalist system as borrowers,” and also introduce the concept of “racialized accumulation by dispossession.” How would you summarize the link between racialized mass incarceration and the debt economy legitimized by morality tropes such as deserving and undeservingborrowers?

One might ask, why include a chapter on the debt economy in a book about prisons and police? Perhaps I was trying to rethink how debt has been conceptualized, and show that expropriative credit instruments are also carceral instruments, insofar as the creditor owns the future of the debtor. In other words, I wanted to think of debt as a form of unfreedom that is unequally distributed (because race, class, and gender structure the forms of credit one has access to, as well as the perceived creditworthiness of the subject). But to label the use of credit as an instrument of capitalist accumulation a “carceral technique” is not merely metaphorical. In my chapter on municipal finance, I examine the chain of indebtedness produced by debt-financed governance. Municipalities have certain financial responsibilities to their creditors that they often offload onto their constituents. Thus, the creditworthiness of municipalities that are struggling fiscally (which determines their ability to access cheap credit) becomes dependent on their ability to loot residents. The financialization of governance and the emergence of new “exotic” credit instruments produce new modes of extraction that are carried out by the criminal justice system. You are also right to point out that both the debt economy and racialized mass incarceration are propped up by a moral economy that fractures the population into the deserving and undeserving.

You argue that the court system and police play an increasingly important role in the generation of revenue via municipal fines, as debt is imposed on residents (especially black Americans, already segregated and seen as potential offenders) through a variety of criminal proceedings that transform the residents’ space into a carceral one, marked by unrelenting austerity measures, hyper-policing, and fines farming. What are the traits of the carceral municipality as opposed to, let’s say, an ideally free city, where mobile, insurgent nonwhite sociality would not be regulated or punished?

In the carceral municipality you are followed in your car by a police officer as you drive to your shit job simply because you are not white. While you are being given a ticket for $300 the cop realizes there is a warrant out for your arrest for an unpaid fine for the length of your grass being three inches too long (though you cannot recall having ever received such a fine). In jail, you call your aunt to bail you out, but she doesn’t have the money and it takes her a day to secure your release through a commercial bondsman. Since your aunt lacked financial assets, she had to list her car as collateral. When she misses a payment due to low-waged and precarious employment, she will be charged additional fees by the bondsman. After you are released from jail, you are reprimanded by your boss for missing work without calling in, and you are written up. Because your license has been revoked for traffic violations and an unpaid ticket, you now have to use the unreliable and underfunded public transportation system to get to work. You arrive late on the day you have been summoned to appear in court because the bus did not arrive on time, and thus you are forced to reschedule your court appearance and pay an additional fee. This scenario could go on and on and on …

What would an alternative look like? I invoke Fred Moten toward the end of the chapter on municipal finance because he reminds me that in the cracks of the carceral society, insurgent socialities already exist. People have an urge toward life, a need to gather, to jam, to conduct experiments in care when the welfare state and health-care system have failed us. It could be comrades taking turns to take the poet Anne Boyer to the hospital while she undergoes cancer treatment, or the creation of mental health collectives, or things more quotidian, not necessarily bound up with our brokenness and deteriorating bodies. It could be the sociality created in the Baltimore Feminist Reading group I was part of, the different mode of engagement we invented there, based on friendship and not the performance of mastery found in the academic seminar. This is not to glorify informal structures of care that emerge in the crucible of a capitalist system that would grind us all to pulp if it weren’t for our friends. But this is the unexpected underside of social precarity: its production of need and dependence can sometimes be socially binding.

Still, some people fall through the cracks. These informal structures are not always sustainable or functional. We don’t always have the resources to catch each other when we fall, when someone is laid off from their job or evicted. I would like a world where housing and food are not commodities, where everyone has health care and guaranteed basic income rather than compulsory debt, and everyone is free to move (without being policed or surveilled) and travel using reliable green transportation infrastructure. As for the city, it should not consist solely of commercial space, but also include true commons: public space for people to gather, for teens to loiter to their heart’s content. Who knows what will be created when congregation is not met with regulation.

Following the 1990s construction of the juvenile “superpredator” by John Dilulio Jr., racialized juvenile defenders became less and less distinct from their adult counterparts, while also being regarded as incapable of self-government and self-determination. How exactly did they earn the right to be punished as adults in the first place?

In the media they “earned” the right to be punished as adults by committing crimes that were cast as socially unforgivable (i.e., violent crimes such as murder). Essentially, the concept of the superpredator produces a type of subject that is incapable of “redemption,” insofar as they are considered constitutionally antisocial and psychopathic. In this view, the only way to protect the social body from the ungovernable juvenile hordes is to permanently confine the so-called superpredators.

Assumingly unbiased and neutral algorithmic/predictive policing uses assumingly error-free data to provide knowledge about where and when the next crime will occur. Why is it important to question who gathers data and how data is gathered in the first place?

Great question. There are some techno-critics who are also techno-optimists, in that they believe algorithmic bias can be corrected through the collection of clean, accurate data. Dirty data would be, say, the data on sexual violence manipulated by the Baltimore Police Department in order to bolster their appearance of being efficacious and responsive. Good datasets would consist of data that gives us some kind of accurate snapshot of the world based on records that have not been tampered with. When it comes to policing, I don’t think it makes sense to uncritically make appeals for better data collection (unless it’s on police conduct!), as such appeals will necessarily expand the domain of policing, and create a more totalizing surveillance state.

As I mention in the book, populations that are not heavily policed fail to generate reams of data. Who collects data, what they will use the data for, what their motivations are, what categories are being used for data collection — all of these factors reveal that data is always-already political. Why is it that only the rich have maintained their right to opacity? Maybe if the context in which data collection took place was not defined by capitalism and white supremacy, we could start thinking about other uses for data — we could use data to determine social needs and resource redistribution rather than punishment and profits. The system in which new technologies appear tends to structure how these technologies are used.”

–  M. Buna interviews Jackie Wang, “Carceral Capitalism: A Conversation with Jackie Wang.” LA Review of Books, May 13, 2018.

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“"This is the news of the millennium!“ said the story on WorldPoliticus.com. Citing unnamed FBI sources, it claimed Hillary Clinton will be indicted in 2017 for crimes related to her email scandal.

"Your Prayers Have Been Answered,” declared the headline.

For Trump supporters, that certainly seemed to be the case. They helped the baseless story generate over 140,000 shares, reactions, and comments on Facebook.

Meanwhile, roughly 6,000 miles away in a small town in the former Yugoslav Republic of Macedonia, a young man watched as money began trickling into his Google AdSense account.

Over the past year, the Macedonian town of Veles (population 45,000) has experienced a digital gold rush as locals launched at least 140 US politics websites. These sites have American-sounding domain names such as WorldPoliticus.com, TrumpVision365.com, USConservativeToday.com, DonaldTrumpNews.co, and USADailyPolitics.com. They almost all publish aggressively pro-Trump content aimed at conservatives and Trump supporters in the US.

The young Macedonians who run these sites say they don’t care about Donald Trump. They are responding to straightforward economic incentives: As Facebook regularly reveals in earnings reports, a US Facebook user is worth about four times a user outside the US. The fraction-of-a-penny-per-click of US display advertising — a declining market for American publishers — goes a long way in Veles. Several teens and young men who run these sites told BuzzFeed News that they learned the best way to generate traffic is to get their politics stories to spread on Facebook — and the best way to generate shares on Facebook is to publish sensationalist and often false content that caters to Trump supporters.

As a result, this strange hub of pro-Trump sites in the former Yugoslav Republic of Macedonia is now playing a significant role in propagating the kind of false and misleading content that was identified in a recent BuzzFeed News analysis of hyperpartisan Facebook pages. These sites open a window into the economic incentives behind producing misinformation specifically for the wealthiest advertising markets and specifically for Facebook, the world’s largest social network, as well as within online advertising networks such as Google AdSense.

“Yes, the info in the blogs is bad, false, and misleading but the rationale is that ‘if it gets the people to click on it and engage, then use it,’” said a university student in Veles who started a US politics site, and who agreed to speak on the condition that BuzzFeed News not use his name.

Using domain name registration records and online searches, BuzzFeed News identified over 100 active US politics websites being run from Veles. The largest of these sites have Facebook pages that boast hundreds of thousands of followers.

BuzzFeed News also identified another 40 US politics domains registered by people in Veles that are no longer active. (An April report from the Macedonian website Meta.mk identified six pro-Trump sites being run from Veles. A Guardian report identified 150 politics sites.)

Their reasons for launching these sites are purely financial, according to the Macedonians with whom BuzzFeed News spoke.

“I started the site for a easy way to make money,” said a 17-year-old who runs a site with four other people. “In Macedonia the economy is very weak and teenagers are not allowed to work, so we need to find creative ways to make some money. I’m a musician but I can’t afford music gear. Here in Macedonia the revenue from a small site is enough to afford many things.”

Most of the posts on these sites are aggregated, or completely plagiarized, from fringe and right-wing sites in the US. The Macedonians see a story elsewhere, write a sensationalized headline, and quickly post it to their site. Then they share it on Facebook to try and generate traffic. The more people who click through from Facebook, the more money they earn from ads on their website.”


Craig Silverman & Lawrence Alexander,

“How Teens In The Balkans Are Duping Trump Supporters With Fake News.” BuzzFeed News, November 3, 2016. 

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