The team at Data & Society have long been on the case of algorithmic management – I still remember how excited I was reading the seminal 2017 paper on Uber’s control over drivers by Alex Rosenblat and Luke Stark. In this explainer, Alexandra Mateescu and Aiha Nguyen map the use of AI in workplace management in industries from the gig economy to domestic work and retail; identifying key challenges in four broad areas – surveillance and control, transparency, bias and discrimination, and accountability.
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 work forces. 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 optimise 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.