algorithmic management
In plain wordsWhen software, rather than a human manager, assigns work, sets pay, tracks performance and even disciplines or fires workers, as in ride-hailing, delivery and warehouse jobs.
The use of software, data and increasingly AI to carry out managerial functions over workers: allocating tasks and shifts, setting prices and pay, monitoring activity and productivity, evaluating performance through metrics and customer ratings, and issuing nudges, warnings, deactivations or dismissals with little or no human involvement. Pioneered by gig platforms for ride-hailing and delivery, it has spread to warehouses, call centers, retail scheduling and office work. Research documents efficiency gains alongside opacity, intensified surveillance, speed-up, difficulty contesting decisions and the shifting of risk onto workers. The EU Platform Work Directive (Directive (EU) 2024/2831), which member states must transpose by 2 December 2026, requires platforms to be transparent about automated monitoring and decision-making systems, to ensure human oversight of them, and to give workers human review of significant decisions such as account suspension.
Under algorithmic management, a driver's falling acceptance rate can end their account without any person reviewing it.
Origin
Coined by Min Kyung Lee, Daniel Kusbit, Evan Metsky and Laura Dabbish in 'Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers' (CHI 2015), a study of Uber and Lyft drivers; Alex Rosenblat and Luke Stark's 2016 study of Uber drivers helped spread it.
Related
- AI surveillanceAI surveillance, n.: Using AI to watch, identify, track or profile people on a scale no team of humans could manage, for example by scanning camera feeds or analyzing phone and online data.
- ghost workghost work, n.: The hidden human labor behind AI: people, often poorly paid and far away, who label data, check outputs and handle cases the software cannot, while the product appears fully automatic.
- automated decision-makingautomated decision-making, n.: When a computer system, rather than a person, makes or effectively makes a decision about someone — such as approving credit, screening job applicants or flagging possible welfare fraud.
- human oversighthuman oversight, n.: Keeping people able to watch, understand, question and, if necessary, stop or overrule an AI system, rather than letting it act unchecked.
- deskillingdeskilling, n.: The loss of people's skills when machines take over the work that used to keep those skills sharp, or when jobs are redesigned so that less skill is needed.
- automationautomation, n.: Using machines or software to do work that people would otherwise do, from factory robots to programs that sort email or write reports.
Sources
- Lee, M. K., Kusbit, D., Metsky, E. & Dabbish, L. (2015). Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers. Proc. CHI 2015, 1603–1612.
- Rosenblat, A. & Stark, L. (2016). Algorithmic Labor and Information Asymmetries: A Case Study of Uber's Drivers. International Journal of Communication 10, 3758–3784.
Mentioned in
AI surveillanceAI surveillance, n.: Using AI to watch, identify, track or profile people on a scale no team of humans could manage, for example by scanning camera feeds or analyzing phone and online data., deskillingdeskilling, n.: The loss of people's skills when machines take over the work that used to keep those skills sharp, or when jobs are redesigned so that less skill is needed., ghost workghost work, n.: The hidden human labor behind AI: people, often poorly paid and far away, who label data, check outputs and handle cases the software cannot, while the product appears fully automatic.