1. 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?)
2. ALEXANDRA MATEESCU and AIHA NGUYEN:
"Algorithmic management is a diverse set of technological tools and techniques to remotely manage workforces, relying on data collection and surveillance of workers to enable automated or semi-automated decision-making. Many of the characteristics of algorithmic management—such as consumer-sourced rating systems and automated “nudges”—were developed by companies of the “sharing” or “gig” economy. These practices have spurred debates over employee classification, as “gig” economy companies classify workers as independent contractors even as they use technology to exert control over their workforces.
And algorithmic management is becoming more common in other work contexts beyond “gig” platforms. Within delivery and logistics, companies from UPS to Amazon to grocery chains are using automated systems to optimize delivery workers’ daily routes. Domestic workers and hotel housekeepers are increasingly remotely tracked and managed through software. In retail and service industries, automated scheduling is replacing managers’ discretion over employee schedules, while the work of evaluating employees is being transferred to consumer-sourced rating systems." (https://datasociety.net/wp-content/uploads/2019/02/DS_Algorithmic_Management_Explainer.pdf?)
ALEXANDRA MATEESCU and AIHA NGUYEN:
"Algorithmic management can describe systems of varying degrees of complexity, but they typically include:
• Prolific data collection and surveillance of workers through technology; • Real-time responsiveness to data that informs management decisions; • Automated or semi-automated decision-making; • Transfer of performance evaluations to rating systems or other metrics; and • The use of “nudges” and penalties to indirectly incentivize worker behaviors. "
Effects on Labor
ALEXANDRA MATEESCU and AIHA NGUYEN:
"Adoption of these technologies is generating new challenges for workers’ rights in four broad areas:
• Surveillance and control: Technology-enabled surveillance can generate new speed and efficiency pressures for workers and may lock workers out from important aspects of decision making, such as being able to use personal discretion.
• Transparency: Algorithmic management can create power imbalances that may be difficult to challenge without access to how these systems work as well as the resources and expertise to adequately assess them.
• Bias and discrimination: If used to make decisions about workers, tools like consumer-sourced rating systems can introduce biased and discriminatory practices towards workers.
• Accountability: Algorithmic management can be used to distance companies from the effects of their business decisions, obscuring specific decisions made about how a system should function."
"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?)
"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?)
- Report: EXPLAINER: Algorithmic Management in the Workplace. By ALEXANDRA MATEESCU and AIHA NGUYEN. Data & Society February 2019
URL = datasociety.net pdf