iMANAGE - Rethinking Employment Law for a World of Algorithmic Management

A project funded by the European Research Council from April 2021 to March 2026

Amidst important debates about the gig economy and the automation of jobs, employment law has paid scant attention to the rise of algorithmic management: the increasingly pervasive reliance on monitoring technology and sophisticated algorithms to measure, control, and sanction workers. This poses a fundamental threat to the legal regulation of our labour markets. Automated management allows the exercise of hitherto impossibly granular employer control. At the same time, however, the absence of clear decisions and traditional management structures appears to disperse responsibility ‘into the cloud’.

How can employment law respond to a world in which automation has not replaced workers—but their bosses? This pioneering project will develop the first systematic account of the challenges and potential of algorithmic management, examine its implications for legal regulation, and develop concrete solutions to avoid harmful path-dependencies. It tackles a risky challenge that goes right to the core of employment law’s existing structures, with technology developing at unprecedented and unpredictable scale.

Looking at a range of jurisdictions across the European Union and beyond, iMANAGE requires the development of novel, interdisciplinary methodology at the intersection of data science and employment law. In articulating the underlying structures of automated management control, it develops a new and positive role for employment law in ensuring algorithmic accountability, and shaping the responsible use of technology in the workplace. This is a challenge we cannot shy away from: both the theoretical foundations of employment law, and its practical operation across different jurisdictions, depend on understanding and regulating the radically different organisation of the workplace of tomorrow.

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 947806).

Funded by the European Research Council