3 ways ‘algorithmic management’ makes work more stressful and less satisfying
Algorithms have become more sophisticated, taking over tasks once the province of human bosses
1. Reduced task variety and skill use
A great example of the way algorithmic management can reduce task variety and skill use is demonstrated by a2017 studyon the use of electronic monitoring to pay British nurses providing home care to elderly and disabled people.
The system under which the nurses worked was meant to improve their efficiency. They had to use an app to “tag” their care activities. They were paid only for the tasks that could be tagged. Nothing else was recognized. The result was they focused on the urgent and technical care tasks — such as changing bandages or giving medication — and gave up spending time talking to their patients. This reduced both the quality of care as well as the nurses’ sense of doing significant and worthwhile work.
Researchsuggestsincreasing use of algorithms to monitor and manage workers will reduce task variety and skill. Call centers, for example, already use technologyto assess a customers’ moodand instruct the call center worker on exactly how to respond, from what emotions they should deeply to how fast they should speak.
2. Reduced job autonomy
Gig workers refer to as the “fallacy of autonomy” that arises from the apparent ability to choose when and how long they work. When the reality is that platform algorithms use things like acceptance rates to calculate performance scores and to determine future assignments.
This loss of general autonomy is underlined by a2019 study that interviewed 30 gig workersusing the “piecework” platforms Amazon Mechanical Turk, MobileWorks, and CloudFactory. In theory, workers could choose how long they worked. In practice, they felt they needed to constantly be on call to secure the best paying tasks.
This isn’t just the experience of gig workers. Adetailed 2013 study of the US truck driving industryshowed the downside of algorithms dictating what routes drivers should take, and when they should stop, based on weather and traffic conditions. As one driver in the study put it: “A computer does not know when we are tired, fatigued, or anything else […] I am also a professional and I do not need a [computer] telling me when to stop driving.”
3. Increased intensity and insecurity
Algorithmic management can heighten work intensity in a number of ways. It can dictate the pace directly, as with Amazon’s use of timers for “pickers” in its fulfillment centers.
But perhaps more pernicious is its ability to ramp up the work pressure indirectly. Workers who don’t really understand how an algorithm makes its decisions feel more uncertain and insecure about their performance. They worry about every aspect of affecting how the machine rates and ranks them.
For example, in a2020 studyof the experience of 25 food couriers in Edinburgh, the riders spoke about feeling anxious and being “on edge” to accept and complete jobs lest their performance statistics be affected. This led them to take risks such as riding through red lights or through busy traffic in heavy rain. They felt pressure to take all assignments and complete them as quickly as possible so as to be assigned more jobs.
Avoiding a tsunami of unhealthy work
The overwhelming extent to which studies show negative psychological outcomes from algorithmic management suggests we face a tsunami of unhealthy work as the use of such technology accelerates.
Currently, the design and use of algorithmic management systems are driven by “efficiency” for the employer. A more considered approach is needed to ensure these systems can coexist with dignified, meaningful work.
Transparency and accountability are key to ensuring workers (and their representatives) understand what is being monitored, and why, and that they can appeal those decisions to a higher, human, power.
Article bySharon Kaye Parker, Australian Research Council Laureate Fellow,Curtin UniversityandXavier Parent-Rocheleau, Professor,HEC Montréal
This article is republished fromThe Conversationunder a Creative Commons license. Read theoriginal article.
Story byThe Conversation
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