person:alex rosenblat

  • The Gnawing Anxiety of Having an Algorithm as a Boss - Bloomberg
    https://www.bloomberg.com/news/articles/2019-06-26/the-gnawing-anxiety-of-having-an-algorithm-as-a-boss

    I recently got the internet in my apartment fixed, and my technician had an unusual request. I’d get an automated call after he left asking me how satisfied I was with the service, he explained, and he wanted me to rate him 9 out of 10. I asked why, and he said there was a glitch with the system that recorded any 10 rating as a 1, and it was important for him to keep his rating up.

    Since then, a couple of people have told me that technicians working for the company have been making this exact request for at least two years. A representative for Spectrum, my internet provider, said they were worrying over nothing. The company had moved away from the 10-point rating system, he said, adding that customer feedback isn’t “tied to individual technicians’ compensation.”

    But even if the Spectrum glitch exists only in the lore of cable repairmen, the anxiety it’s causing them is telling. Increasingly, workers are impacted by automated decision-making systems, which also affects people who read the news, or apply for loans, or shop in stores. It only makes sense that they’d try to bend those systems to their advantage.

    There exist at least two separate academic papers with the title “Folk Theories of Social Feeds,” detailing how Facebook users divine what its algorithm wants, then try to use those theories to their advantage.

    People with algorithms for bosses have particular incentive to push back. Last month, a local television station in Washington covered Uber drivers who conspire to turn off their apps simultaneously in order to trick its system into raising prices.

    Alex Rosenblat, the author of Uberland, told me that these acts of digital disobedience are essentially futile in the long run. Technology centralizes power and information in a way that overwhelms mere humans. “You might think you’re manipulating the system,” she says, but in reality “you’re working really hard to keep up with a system that is constantly experimenting on you.”

    Compared to pricing algorithms, customer ratings of the type that worried my repairman should be fairly straightforward. Presumably it’s just a matter of gathering data and calculating an average. But online ratings are a questionable way to judge people even if the data they’re based on are pristine—and they probably aren’t. Academics have shown that customer ratings reflect racial biases. Complaints about a product or service can be interpreted as commentary about the person who provided it, rather than the service itself. And companies like Uber require drivers to maintain such high ratings that, in effect, any review that isn’t maximally ecstatic is a request for punitive measures.

    #Travail #Surveillance #Algorithme #Stress #Société_contrôle

  • Revolt of the gig workers: How delivery rage reached a tipping point - SFChronicle.com
    https://www.sfchronicle.com/business/article/Revolt-of-the-gig-workers-How-delivery-rage-13605726.php

    Gig workers are fighting back.

    By their name, you might think independent contractors are a motley crew — geographically scattered, with erratic paychecks and tattered safety nets. They report to faceless software subroutines rather than human bosses. Most gig workers toil alone as they ferry passengers, deliver food and perform errands.

    But in recent weeks, some of these app-wielding workers have joined forces to effect changes by the multibillion-dollar companies and powerful algorithms that control their working conditions.

    Last week, Instacart shoppers wrung payment concessions from the grocery delivery company, which had been using customer tips to subsidize what it paid them. After outcries by workers on social media, in news reports and through online petitions, San Francisco’s Instacart said it had been “misguided.” It now adds tips on top of its base pay — as most customers and shoppers thought they should be — and will retroactively compensate workers who were stiffed on tips.

    New York this year became the first U.S. city to implement a minimum wage for Uber and Lyft, which now must pay drivers at least $17.22 an hour after expenses ($26.51 before expenses). Lyft, which sued over the requirement, last week gave in to driver pressure to implement it.

    For two years, drivers held rallies, released research, sent thousands of letters and calls to city officials, and gathered 16,000 petition signature among themselves. The Independent Drivers Guild, a union-affiliated group that represents New York ride-hail drivers and spearheaded the campaign, predicted per-driver pay boosts of up to $9,600 a year.

    That follows some other hard-fought worker crusades, such as when they persuaded Uber to finally add tipping to its app in 2017, a move triggered by several phenomena: a string of corporate scandals, the fact that rival Lyft had offered tipping from the get-go, and a class-action lawsuit seeking employment status for workers.

    “We’ll probably start to see more gig workers organizing as they realize that enough negative publicity for the companies can make something change,” said Alexandrea Ravenelle, an assistant sociology professor at New York’s Mercy College and author of “Hustle and Gig: Struggling and Surviving in the Sharing Economy.” “But companies will keep trying to push the envelope to pay workers as little as possible.”

    The current political climate, with tech giants such as Facebook and Google on hot seats over privacy, abuse of customer data and other issues, has helped the workers’ quests.

    “We’re at a moment of reckoning for tech companies,” said Alex Rosenblat, a technology ethnographer at New York’s Data & Society Research Institute and author of “Uberland: How Algorithms Are Rewriting the Rules of Work.” “There’s a techlash, a broader understanding that tech companies have to be held accountable as political institutions rather than neutral forces for good.”

    The climate also includes more consumer awareness of labor issues in the on-demand economy. “People are realizing that you don’t just jump in an Uber and don’t have to think about who’s driving you and what they make,” Ravenelle said. “There’s a lot more attention to gig workers’ plight.”

    Instacart customers were dismayed to discover that their tips were not going to workers on top of their pay as a reward for good service.

    Sage Wilson, a spokesman for Working Washington, a labor-backed group that helped with the Instacart shoppers’ campaign, said many more gig workers have emerged with stories of similar experiences on other apps.

    “Pay transparency really seems to be an issue across many of these platforms,” he said. “I almost wonder if it’s part of the reason why these companies are building black box algorithmic pay models in the first place (so) you might not even know right away if you got a pay cut until you start seeing the weekly totals trending down.”

    Cases in point: DoorDash and Amazon also rifle the tip jar to subsidize contractors’ base pay, as Instacart did. DoorDash defended this, saying its pay model “provides transparency, consistency, and predictability” and has increased both satisfaction and retention of its “Dashers.”

    But Kristen Anderson of Concord, a social worker who works part-time for DoorDash to help with student loans, said that was not her experience. Her pay dropped dramatically after DoorDash started appropriating tips in 2017, she said. “Originally it was worth my time and now it’s not,” she said. “It’s frustrating.”

    Debi LaBell of San Carlos, who does weekend work for Instacart on top of a full-time job, has organized with others online over the tips issue.

    “This has been a maddening, frustrating and, at times, incredibly disheartening experience,” said Debi LaBell of San Carlos, who does weekend work for Instacart on top of a full-time job. “When I first started doing Instacart, I loved getting in my car to head to my first shop. These past few months, it has taken everything that I have to get motivated enough to do my shift.”

    Before each shopping trip, she hand-wrote notes to all her customers explaining the tips issue. She and other shoppers congregated online both to vent and to organize.

    Her hope now is that Instacart will invite shoppers like her to hear their experiences and ideas.

    There’s poetic justice in the fact that the same internet that allows gig companies to create widely dispersed marketplaces provided gig workers space to find solidarity with one another.

    “It’s like the internet taketh and giveth,” said Eric Lloyd, an attorney at the law firm Seyfarth Shaw, which represents management, including some gig companies he wouldn’t name, in labor cases. “The internet gave rise to this whole new economy, giving businesses a way to build really innovative models, and it’s given workers new ways to advance their rights.”

    For California gig workers, even more changes are on the horizon in the wake of a ground-breaking California Supreme Court decision last April that redefined when to classify workers as employees versus independent contractors.

    Gig companies, labor leaders and lawmakers are holding meetings in Sacramento to thrash out legislative responses to the Dynamex decision. Options could range from more workers getting employment status to gig companies offering flexible benefits. Whatever happens, it’s sure to upend the status quo.

    Rather than piecemeal enforcement through litigation, arbitration and various government agencies such as unemployment agencies, it makes sense to come up with overall standards, Rosenblat said.

    “There’s a big need for comprehensive standards with an understanding of all the trade-offs,” she said. “We’re at a tipping point for change.”

    Carolyn Said is a San Francisco Chronicle staff writer. Email: csaid@sfchronicle.com Twitter: @csaid

    #USA #Kalifornien #Gig-Economy #Ausbeutung

  • « Uber, c’est une forme radicale de capitalisme de surveillance »
    https://usbeketrica.com/article/uber-c-est-une-forme-radicale-de-capitalisme-de-surveillance

    Pendant quatre ans, l’éthnographe américaine Alex Rosenblat a sillonné les routes avec des chauffeurs Uber. Elle rend compte de cette expérience dans Uberland (Presses universitaires de Californie, 2018), un livre dans lequel elle souligne comment l’entreprise californienne contribue à réécrire les règles du travail, brouillant les frontières entre entrepreneur, employé et client. Diplomée en sociologie, Alex Rosenblat est membre du centre de recherche new-yorkais Data & Society, qui entend (...)

    #Lyft #Uber #algorithme #travail #travailleurs #surveillance

  • Uberland : l’avenir du travail à l’heure des algorithmes | InternetActu.net
    http://www.internetactu.net/2018/12/18/uberland-lavenir-du-travail-a-lheure-des-algorithmes
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    Dans Uberland : comment les algorithmes réécrivent les règles du travail (2018, Presse universitaire de Californie, non traduit), la chercheuse Alex Rosenblat (@mawnikr) a synthétisé quatre années de recherche ethnographique avec les conducteurs d’Uber. Un livre où transparaît une vision dystopique du travail où des millions de conducteurs sont gérés par un système technique qui combine à la fois l’autoritarisme du management scientifique à la Frederick Taylor et le leadership cynique d’un Michael Scott (le personnage incarné par Steve Carell dans la série The Office), rapporte Intelligencer.

    (...) Le mot entrepreneur cache de plus en plus souvent un travailleur sans salaire minimum, sans avantages sociaux ni protection. L’absence de hiérarchie signifie que les indépendants sont soumis aux caprices de système de notation anonymes. Dans l’économie du partage, personne n’est licencié, les conducteurs sont « désactivés », sans que ce processus ne soit ni juste ni transparent. Les interactions humaines authentiques que vantaient les plateformes ont surtout créé de la paupérisation. L’évolution de l’économie du partage, comme de l’industrie de la techno, a commencé par un rêve utopique et s’achève dans un cauchemar dystopique. Les entreprises qui annonçaient vouloir changer le monde, comme Airbnb et Uber, ont visiblement été construites sur des idéaux qui ont atteint leur date d’expiration, conclut Mike Bulajewski. Si l’ubérisation n’est peut-être pas encore tout à fait morte, la lutte contre ses effets, elle, ne cesse de s’organiser.

    #futur_du_travail #économie #algorithme #économie_du_partage

  • We’ve Been Trapped in ‘Uberland’
    https://www.citylab.com/life/2018/10/uberland-and-how-ride-hailing-changed-work/573586

    In her new book, Alex Rosenblat talked with drivers in 25 cities to trace the story of how ride-hailing redefined the nature of work. In 2009, Uber was born out of a simple idea : Tap a button, get a ride. As it grew popular, the platform, and the ride-hailing model it helped pioneer, seemed like it would go beyond just meeting a transportation need : It seemed to have the potential to solve problems of transit access and cater to people whom cab drivers may have discriminated against in (...)

    #Uber #travail

  • Uberland : l’avenir du travail à l’heure des #algorithmes
    http://www.internetactu.net/2018/12/18/uberland-lavenir-du-travail-a-lheure-des-algorithmes

    Dans Uberland : comment les algorithmes réécrivent les règles du travail (2018, Presse universitaire de Californie, non traduit), la chercheuse Alex Rosenblat (@mawnikr) a synthétisé quatre années de recherche ethnographique avec les conducteurs d’Uber. Un livre où transparaît une vision dystopique du travail où des millions de conducteurs sont gérés par (...)

    #Articles #Services #Economie_et_marchés #eDémocratie #ubérisation

  • High score, low pay : why the gig economy loves gamification | Business | The Guardian
    https://www.theguardian.com/business/2018/nov/20/high-score-low-pay-gamification-lyft-uber-drivers-ride-hailing-gig-econ

    Using ratings, competitions and bonuses to incentivise workers isn’t new – but as I found when I became a Lyft driver, the gig economy is taking it to another level.

    Every week, it sends its drivers a personalised “Weekly Feedback Summary”. This includes passenger comments from the previous week’s rides and a freshly calculated driver rating. It also contains a bar graph showing how a driver’s current rating “stacks up” against previous weeks, and tells them whether they have been “flagged” for cleanliness, friendliness, navigation or safety.

    At first, I looked forward to my summaries; for the most part, they were a welcome boost to my self-esteem. My rating consistently fluctuated between 4.89 stars and 4.96 stars, and the comments said things like: “Good driver, positive attitude” and “Thanks for getting me to the airport on time!!” There was the occasional critique, such as “She weird”, or just “Attitude”, but overall, the comments served as a kind of positive reinforcement mechanism. I felt good knowing that I was helping people and that people liked me.

    But one week, after completing what felt like a million rides, I opened my feedback summary to discover that my rating had plummeted from a 4.91 (“Awesome”) to a 4.79 (“OK”), without comment. Stunned, I combed through my ride history trying to recall any unusual interactions or disgruntled passengers. Nothing. What happened? What did I do? I felt sick to my stomach.

    Because driver ratings are calculated using your last 100 passenger reviews, one logical solution is to crowd out the old, bad ratings with new, presumably better ratings as fast as humanly possible. And that is exactly what I did.

    In a certain sense, Kalanick is right. Unlike employees in a spatially fixed worksite (the factory, the office, the distribution centre), rideshare drivers are technically free to choose when they work, where they work and for how long. They are liberated from the constraining rhythms of conventional employment or shift work. But that apparent freedom poses a unique challenge to the platforms’ need to provide reliable, “on demand” service to their riders – and so a driver’s freedom has to be aggressively, if subtly, managed. One of the main ways these companies have sought to do this is through the use of gamification.

    Simply defined, gamification is the use of game elements – point-scoring, levels, competition with others, measurable evidence of accomplishment, ratings and rules of play – in non-game contexts. Games deliver an instantaneous, visceral experience of success and reward, and they are increasingly used in the workplace to promote emotional engagement with the work process, to increase workers’ psychological investment in completing otherwise uninspiring tasks, and to influence, or “nudge”, workers’ behaviour. This is what my weekly feedback summary, my starred ratings and other gamified features of the Lyft app did.

    There is a growing body of evidence to suggest that gamifying business operations has real, quantifiable effects. Target, the US-based retail giant, reports that gamifying its in-store checkout process has resulted in lower customer wait times and shorter lines. During checkout, a cashier’s screen flashes green if items are scanned at an “optimum rate”. If the cashier goes too slowly, the screen flashes red. Scores are logged and cashiers are expected to maintain an 88% green rating. In online communities for Target employees, cashiers compare scores, share techniques, and bemoan the game’s most challenging obstacles.
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    But colour-coding checkout screens is a pretty rudimental kind of gamification. In the world of ride-hailing work, where almost the entirety of one’s activity is prompted and guided by screen – and where everything can be measured, logged and analysed – there are few limitations on what can be gamified.

    Every Sunday morning, I receive an algorithmically generated “challenge” from Lyft that goes something like this: “Complete 34 rides between the hours of 5am on Monday and 5am on Sunday to receive a $63 bonus.” I scroll down, concerned about the declining value of my bonuses, which once hovered around $100-$220 per week, but have now dropped to less than half that.

    “Click here to accept this challenge.” I tap the screen to accept. Now, whenever I log into driver mode, a stat meter will appear showing my progress: only 21 more rides before I hit my first bonus.

    In addition to enticing drivers to show up when and where demand hits, one of the main goals of this gamification is worker retention. According to Uber, 50% of drivers stop using the application within their first two months, and a recent report from the Institute of Transportation Studies at the University of California in Davis suggests that just 4% of ride-hail drivers make it past their first year.

    Before Lyft rolled out weekly ride challenges, there was the “Power Driver Bonus”, a weekly challenge that required drivers to complete a set number of regular rides. I sometimes worked more than 50 hours per week trying to secure my PDB, which often meant driving in unsafe conditions, at irregular hours and accepting nearly every ride request, including those that felt potentially dangerous (I am thinking specifically of an extremely drunk and visibly agitated late-night passenger).

    Of course, this was largely motivated by a real need for a boost in my weekly earnings. But, in addition to a hope that I would somehow transcend Lyft’s crappy economics, the intensity with which I pursued my PDBs was also the result of what Burawoy observed four decades ago: a bizarre desire to beat the game.

    Former Google “design ethicist” Tristan Harris has also described how the “pull-to-refresh” mechanism used in most social media feeds mimics the clever architecture of a slot machine: users never know when they are going to experience gratification – a dozen new likes or retweets – but they know that gratification will eventually come. This unpredictability is addictive: behavioural psychologists have long understood that gambling uses variable reinforcement schedules – unpredictable intervals of uncertainty, anticipation and feedback – to condition players into playing just one more round.

    It is not uncommon to hear ride-hailing drivers compare even the mundane act of operating their vehicles to the immersive and addictive experience of playing a video game or a slot machine. In an article published by the Financial Times, long-time driver Herb Croakley put it perfectly: “It gets to a point where the app sort of takes over your motor functions in a way. It becomes almost like a hypnotic experience. You can talk to drivers and you’ll hear them say things like, I just drove a bunch of Uber pools for two hours, I probably picked up 30–40 people and I have no idea where I went. In that state, they are literally just listening to the sounds [of the driver’s apps]. Stopping when they said stop, pick up when they say pick up, turn when they say turn. You get into a rhythm of that, and you begin to feel almost like an android.”

    In their foundational text Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers, Alex Rosenblat and Luke Stark write: “Uber’s self-proclaimed role as a connective intermediary belies the important employment structures and hierarchies that emerge through its software and interface design.” “Algorithmic management” is the term Rosenblat and Stark use to describe the mechanisms through which Uber and Lyft drivers are directed. To be clear, there is no singular algorithm. Rather, there are a number of algorithms operating and interacting with one another at any given moment. Taken together, they produce a seamless system of automatic decision-making that requires very little human intervention.

    For many on-demand platforms, algorithmic management has completely replaced the decision-making roles previously occupied by shift supervisors, foremen and middle- to upper- level management. Uber actually refers to its algorithms as “decision engines”. These “decision engines” track, log and crunch millions of metrics every day, from ride frequency to the harshness with which individual drivers brake. It then uses these analytics to deliver gamified prompts perfectly matched to drivers’ data profiles.

    To increase the prospect of surge pricing, drivers in online forums regularly propose deliberate, coordinated, mass “log-offs” with the expectation that a sudden drop in available drivers will “trick” the algorithm into generating higher surges. I have never seen one work, but the authors of a recently published paper say that mass log-offs are occasionally successful.

    Viewed from another angle, though, mass log-offs can be understood as good, old-fashioned work stoppages. The temporary and purposeful cessation of work as a form of protest is the core of strike action, and remains the sharpest weapon workers have to fight exploitation. But the ability to log-off en masse has not assumed a particularly emancipatory function.

    After weeks of driving like a maniac in order to restore my higher-than-average driver rating, I managed to raise it back up to a 4.93. Although it felt great, it is almost shameful and astonishing to admit that one’s rating, so long as it stays above 4.6, has no actual bearing on anything other than your sense of self-worth. You do not receive a weekly bonus for being a highly rated driver. Your rate of pay does not increase for being a highly rated driver. In fact, I was losing money trying to flatter customers with candy and keep my car scrupulously clean. And yet, I wanted to be a highly rated driver.
    How much is an hour worth? The war over the minimum wage
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    And this is the thing that is so brilliant and awful about the gamification of Lyft and Uber: it preys on our desire to be of service, to be liked, to be good. On weeks that I am rated highly, I am more motivated to drive. On weeks that I am rated poorly, I am more motivated to drive. It works on me, even though I know better. To date, I have completed more than 2,200 rides.

    #Lyft #Uber #Travail #Psychologie_comportementale #Gamification #Néo_management #Lutte_des_classes

  • Réguler le partage : « une tâche difficile, mais cruciale »
    https://linc.cnil.fr/fr/reguler-le-partage-une-tache-difficile-mais-cruciale

    Asymétrie d’information, défaut de loyauté et contrôle des données… les chercheurs Ryan Calo et Alex Rosenblat s’attaquent au "share washing" et à la nécessaire régulation des plateformes de l’économie collaborative. Des travaux dans la même thématique que le cahier IP "Partage !", publié en juin 2016. Ryan Calo et Alex Rosenblat publiaient en ce début de mois de mars 2017, « The Taking Economy : Uber, Information, and Power », faisant le constat du manque de « critique fondamentale de l’économie (...)

    #Uber #algorithme #données #data #discrimination #géolocalisation #domination