iManage | Rethinking Employment Law for a world of Algorithmic Management

Summary
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.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/947806
Start date: 01-04-2021
End date: 31-03-2026
Total budget - Public funding: 1 496 131,00 Euro - 1 496 131,00 Euro
Cordis data

Original description

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.

Status

SIGNED

Call topic

ERC-2020-STG

Update Date

27-04-2024
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Horizon 2020
H2020-EU.1. EXCELLENT SCIENCE
H2020-EU.1.1. EXCELLENT SCIENCE - European Research Council (ERC)
ERC-2020
ERC-2020-STG