- The Recruiting Life
- When Working for the Man Means Working for a Machine
When Working for the Man Means Working for a Machine
How Algorithms Manage Workforces with Surveillance and Data
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Chart: The Most Profitable Companies in the World
When Working for the Man Means Working for a Machine
Would you work for a machine? Some people are working for a machine right now, although they may not be aware of it. Could you be one of them? According to the Data and Society Research Institute, “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.” Translation: Instead of working for the man, you are working for the machine. Here are some examples…
Services like Uber and Lyft exert what some call “continuous, soft surveillance” through data collection of drivers’ behaviors, which is fed into automated performance reports. While drivers have the freedom to log in or log out of work at will, once they’re online, their activities on the platform are heavily monitored. For instance, drivers’ movements are tracked using GPS location, and other behaviors such as acceleration, working hours, and braking habits are monitored through their phones. All of that data is not only used to evaluate drivers but also to influence their behavior. For example, Uber’s “surge pricing” system. At certain times, in certain locations, both riders and drivers receive notification that rides will be provided at higher rates, thus nudging more drivers to be available in a high-demand location. Such a system reveals how algorithms can cause disaggregated work forces, supposedly independent and flexible, to behave in ways that are good for the company as a whole.
In 2016, UPS drivers began receiving driving directives from ORION (On-Road Integrated Optimization and Navigation), an algorithm developed internally by UPS to optimize delivery routes by finding the most time-and cost-effective trip routes for a delivery. The company claims the algorithm has reduced unnecessary delivery truck travel by 100 million miles annually.
Percolata is a company that installs sensors in shops that measure the volume and type of customers flowing in and out, combines that with data on the amount of sales per employee, and calculates what it describes as the “true productivity” of a shop worker: a measure it calls “shopper yield”, or sales divided by traffic. Percolata then gives management a list of employees ranked from lowest to highest by shopper yield. Its algorithm builds profiles on each employee — when do they perform well? When do they perform badly? It learns whether some people do better when paired with certain colleagues, and worse when paired with others. It uses weather, online traffic and other signals to forecast customer traffic in advance. Then it creates a schedule with the optimal mix of workers to maximise sales for every 15-minute slot of the day. Managers press a button and the schedule publishes to employees’ personal smartphones. People with the highest shopper yields are usually given more hours.
According to a study by Bucher et al., Upwork uses algorithmic management to govern its platform, which involves using algorithms to monitor and control worker behavior. The study also notes that Upwork's algorithmic management system can lead to anticipatory compliance by workers, where they modify their behavior to conform to the expectations of the algorithm.
Deliveroo, the food delivery platform, uses algorithms to manage its delivery drivers, including assigning orders, tracking driver performance, and optimizing delivery routes.
Algorithms are increasingly being used by fast food restaurants and grocery stores to manage scheduling and performance metrics for workers in these industries.
Algorithmic management has both benefits and drawbacks. Some of the benefits of algorithmic management include:
Increased productivity and efficiency: Algorithms can automate tasks that were previously done manually, allowing companies to complete tasks faster and more accurately.
Improved decision-making: Algorithms can analyze large amounts of data and provide insights that humans may not be able to see, leading to better decision-making.
Cost savings: Algorithmic management can reduce labor costs by automating tasks that were previously done by humans.
Job creation: Algorithmic management can create new jobs in areas such as data analysis and algorithm development.
However, there are also some drawbacks to algorithmic management, including:
Reduced worker autonomy: Workers may feel that they have less control over their work when algorithms are making decisions about their tasks and performance.
Increased work intensity: Algorithms can increase work intensity by setting high performance targets and monitoring worker performance in real-time.
Negative impact on worker well-being: Algorithmic management can increase stress and anxiety for workers who feel uncertain and insecure about their performance.
Ethical concerns: Algorithmic management raises ethical concerns around issues such as privacy, bias, and discrimination.
Negative psychological effects: Workers who don't understand how an algorithm makes its decisions can feel uncertain and insecure about their performance, which can lead to negative psychological effects.
Adverse impact on total rewards practices: Algorithmic management can have an indirect influence on workplace well-being through negative impact on total rewards practices.
Companies that focus solely on efficiency: Companies that focus solely on efficiency and treat workers like programmable "cogs in a machine" can lower employee satisfaction and performance over the long term. Such companies include UPS, which equips trucks with sensors that monitor drivers' every move to increase efficiency.
There are several potential solutions to mitigate the negative impacts of algorithmic management on workplace well-being. Here are some examples:
Involve workers in the design process: Workers should have input into the design of algorithmic management systems and the ability to opt-out of certain features.
Redesign jobs: Companies can redesign jobs to ensure that both the human and technical aspects of the sociotechnical system are enhanced, which can ensure that algorithmic management is used to complement human decision-making, rather than replace it entirely.
Provide training and support: Companies can provide training and support to workers to help them understand how algorithms make decisions and how to work effectively with algorithmic management systems.
Ensure transparency and fairness: Companies should ensure that algorithmic management systems are transparent and fair, and that workers understand how decisions are made.
Prioritize worker well-being: Companies should prioritize the well-being of their workers and ensure that algorithmic management is used in an ethical and responsible manner.
So, should we all relax about the machines taking over the workplace and just, get used to it? Well, yes, as long as human beings are part of the process. The moment you remove people from the decision- making process and put your trust solely in machines and algorithms; then you have legitimate cause to be concerned about the future of work. At least, I think so. What do you think? Leave a comment, I want to hear from you.
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