Towards an International Standard for Regulating Algorithmic Management: A Blueprint

8th Conference of the Regulating for Decent Work Network, International Labour Office Geneva, Switzerland, 10-12 July 2023

19 Pages Posted: 30 Jan 2024

See all articles by Jeremias Adams-Prassl

Jeremias Adams-Prassl

University of Oxford - Faculty of Law

Sangh Rakshita

University of Oxford, Faculty of Law

Halefom H. Abraha

University of Oxford

M. Six Silberman

Oxford University Faculty of Law; London College of Political Technology

Date Written: July 10, 2023

Abstract

Algorithmic management systems have been deployed all over the economy in recent years: software on knowledge workers’ computers monitor their keystrokes and mouse movements, take screenshots of their screens, and take photos through their webcams; the movements of in-person workers are monitored with fine-grained location tracking; and workers in warehouses and logistics face algorithmically-enforced work quotas. Algorithmic systems have also become crucial to hiring: industry research suggests over 95% of the Fortune 500 have adopted automated systems that rank applicants by scanning their CVs; some companies have also adopted machine learning-based video interview software. While these technologies can help employers process large quantities of information, they are new and in some cases flawed. They may therefore not always deliver the hoped-for benefits; and they also pose serious risks.

We report three main findings from a two-year interdisciplinary review of literature on algorithmic management in economics, policy, and law, including investigative journalism and legislative developments. First, algorithmic management poses significant new risks to workers, managers, employers, decent work, and labour market institutions. We identify the dynamics producing these risks: increased privacy harms; widening of information asymmetries; and loss of human agency—especially, but not only, managerial agency—in workplace decision-making. Second, existing regulations do not adequately address these risks, even in jurisdictions with robust data protection regulation. Third, a collection of policies can serve as interlocking elements of a regulatory strategy for addressing these risks, including: prohibitions on specific practices, including automated termination; restriction of legal bases of workplace data processing; individual and collective notice obligations and data access rights; rights to explanation and human intervention; and impact assessment and information and consultation obligations.

These findings raise the question of whether an international labour standard on workplace data processing and algorithmic management may be desirable. The paper outlines some elements of a research agenda for answering this question.

Keywords: algorithmic management, artificial intelligence, fundamental rights, international labour standards, workplace data processing and privacy

JEL Classification: J5, J58, J59, J7, J71, J78, J8, J80, J83, J88, J89, K24, K31, M5, M51, M52, M54, M59, O33, O38

Suggested Citation

Adams-Prassl, Jeremias and Rakshita, Sangh and Abraha, Halefom and Silberman, M. Six, Towards an International Standard for Regulating Algorithmic Management: A Blueprint (July 10, 2023). 8th Conference of the Regulating for Decent Work Network, International Labour Office Geneva, Switzerland, 10-12 July 2023, Available at SSRN: https://ssrn.com/abstract=4684947 or http://dx.doi.org/10.2139/ssrn.4684947

Jeremias Adams-Prassl (Contact Author)

University of Oxford - Faculty of Law ( email )

Magdalen College
Oxford, OX1 4AU
United Kingdom

HOME PAGE: http://www.law.ox.ac.uk/people/jeremias-adams-prassl

Sangh Rakshita

University of Oxford, Faculty of Law ( email )

Oxford
United Kingdom

Halefom Abraha

University of Oxford ( email )

St Cross Building St.Cross Rd
Oxford, Oxford OX1 3UL
United Kingdom

HOME PAGE: http://https://www.law.ox.ac.uk/

M. Six Silberman

Oxford University Faculty of Law ( email )

London College of Political Technology ( email )

133-135 Bethnal Green Road
London, London E2 7DG
United Kingdom

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