Regulating Algorithmic Management: A Multi-Stakeholder Study of Challenges in Aligning Software and the Law for Workplace Scheduling
Jonathan Lynn, Rachel Y. Kim, Sicun Gao, Daniel Schneider, Sachin S. Pandya, Min Kyung Lee

TL;DR
This study examines the challenges and effectiveness of regulating algorithmic management in workplace scheduling through a multi-stakeholder approach, highlighting institutional, practical, and software-related barriers.
Contribution
It provides empirical insights into real-world challenges of implementing and enforcing AM regulations, emphasizing a sociotechnical perspective.
Findings
Institutional constraints hinder law encoding into AM software.
On-the-ground software use influences compliance.
Software-regulatory context mismatches impede enforcement.
Abstract
Algorithmic management (AM)'s impact on worker well-being has led to calls for regulation. However, little is known about the effectiveness and challenges in real-world AM regulation across the regulatory process -- rule operationalization, software use, and enforcement. Our multi-stakeholder study addresses this gap within workplace scheduling, one of the few AM domains with implemented regulations. We interviewed 38 stakeholders across the regulatory process: regulators, defense attorneys, worker advocates, managers, and workers. Our findings suggest that the efficacy of AM regulation is influenced by: (i) institutional constraints that challenge efforts to encode law into AM software, (ii) on-the-ground use of AM software that shapes its ability to facilitate compliance, (iii) mismatches between software and regulatory contexts that hinder enforcement, and (iv) unique concerns that…
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