Algorithmic Management

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Description

Sarah O'Connor:

"Algorithmic management solves a problem: how to instruct, track and evaluate a crowd of casual workers you do not employ, so they deliver a responsive, seamless, standardised service.

Those deploying algorithmic management say it creates new employment opportunities, better and cheaper consumer services, transparency and fairness in parts of the labour market that are characterised by inefficiency, opacity and capricious human bosses. But a summer of wildcat strikes in London’s gig economy shows that some workers are beginning to chafe against the contradictions of being “their own boss” yet tightly managed by the smartphones in their pockets. They might be free to choose when to work but not how to work or, crucially, how much they are paid." (https://www.ft.com/content/88fdc58e-754f-11e6-b60a-de4532d5ea35?)

History

Taylorism

"Algorithmic management” might sound like the future but it has uncanny echoes from the past. A hundred years ago, a new theory called “scientific management” swept through the factories of America. It was the brainchild of Frederick W Taylor, the son of a well-to-do Philadelphia family who dropped his preparations for Harvard to become an apprentice in a hydraulics factory. He saw a haphazard workplace where men worked as slowly as they could get away with while their bosses paid them as little as possible. Taylor wanted to replace this “rule of thumb” approach with “the establishment of many rules, laws and formulae which replace the judgment of the individual workman”. To that end, he sent managers with stopwatches and notebooks on to the shop floor. They observed, timed and recorded every stage of every job, and determined the most efficient way that each one should be done. “Perhaps the most prominent single element in modern scientific management is the task idea,” Taylor wrote in his 1911 book The Principles of Scientific Management. “This task specifies not only what is to be done but how it is to be done and the exact time allowed for doing it.” (https://www.ft.com/content/88fdc58e-754f-11e6-b60a-de4532d5ea35?)


Example

Sarah O'Connor:

"Kyaw, a boyish-looking 30-year-old, has been working for Deliveroo for about nine months. He works 40-50 hours a week over six days and makes £400-£450 before tax, motorbike insurance and maintenance. In most parts of London, Deliveroo schedules shifts, which couriers agree a week in advance. They must work at least two of Friday, Saturday and Sunday evenings (though Deliveroo says shifts can be moved around when necessary). They are paid £7 an hour plus £1 a delivery, tips and petrol.

Kyaw whips out his phone. The app expects him to respond to new orders within 30 seconds. The screen shows a map and address for the local Carluccio’s, an Italian restaurant chain. A swipe bar says “Accept delivery”. That is the only option. The algorithm will not tell him the delivery address until he has picked up the food from Carluccio’s. Deliveroo couriers are assigned fairly small geographic areas but Kyaw says sometimes the delivery address is way outside his allocated zone. You can only decline an order by phoning the driver support line. “They say, ‘No, you have to do it, you already collected the food.’ If you want to return the food to the restaurant they mark it as a driver refusal — that’s bad.”

Bad how? Deliveroo’s algorithm monitors couriers closely and sends them personalised monthly “service level assessments” on their average “time to accept orders”, “travel time to restaurant”, “travel time to customer”, “time at customer”, “late orders” and “unassigned orders”. The algorithm compares each courier’s performance to its own estimate of how fast they should have been. An example from one of Kyaw’s assessments: “Your average time to customer was less than our estimate, which means you are meeting this service-level criterion. Your average difference was -3.1 minutes.” Deliveroo confirmed it performs the assessments but said its “time-related requirements” took into account reasonable delays and riders were “never against the clock for an order”.

Drivers for Uber’s ride-hailing app, of which there are about a million around the world, are subject to similar algorithmic control. They choose when to work but once they log on to the app, they only have 10-20 seconds to respond to “trip requests” routed to them by the algorithm. They are not told the customer’s final destination until they have picked them up. If drivers miss three trip requests in a row, they are logged out automatically for two minutes. Uber sends drivers a weekly report including their confirmation rate and average customer rating (out of 5)." (https://www.ft.com/content/88fdc58e-754f-11e6-b60a-de4532d5ea35?)