Regulating Algorithmic Management: A Multi-Stakeholder Study of Challenges in Aligning Software and the Law for Workplace Scheduling

• URL: http://arxiv.org/abs/2505.02329v3
• Date: Tue, 01 Jul 2025 20:12:44 GMT
• Title: Regulating Algorithmic Management: A Multi-Stakeholder Study of Challenges in Aligning Software and the Law for Workplace Scheduling
• Authors: Jonathan Lynn, Rachel Y. Kim, Sicun Gao, Daniel Schneider, Sachin S. Pandya, Min Kyung Lee,
• Abstract summary: Algorithmic management (AM)'s impact on worker well-being has led to calls for regulation.<n>Little is known about the effectiveness and challenges in real-world AM regulation across the regulatory process.
• Score: 17.35427389352792
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 software introduces when used to regulate AM. These findings underscore the importance of a sociotechnical approach to AM regulation, which considers organizational and collaborative contexts alongside the inherent attributes of software. We offer future research directions and implications for technology policy and design.

Related papers

• From Legal Text to Tech Specs: Generative AI's Interpretation of Consent in Privacy Law [0.0]
  This study employs a three-step pipeline that involves using an LLM to classify software use cases for compliance.<n>By benchmarking LLMs against real-world use cases, this research provides insights into leveraging AI-driven solutions to enhance legal compliance of software.
  arXiv  Detail & Related papers  (2025-07-05T23:36:05Z)
• Media and responsible AI governance: a game-theoretic and LLM analysis [61.132523071109354]
  This paper investigates the interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems.<n>Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes.
  arXiv  Detail & Related papers  (2025-03-12T21:39:38Z)
• Mapping the Regulatory Learning Space for the EU AI Act [0.8987776881291145]
  The EU AI Act represents the world's first transnational AI regulation with concrete enforcement measures.<n>It builds on existing EU mechanisms for regulating health and safety of products but extends them to protect fundamental rights.<n>We argue that this will lead to multiple uncertainties in the enforcement of the AI Act.
  arXiv  Detail & Related papers  (2025-02-27T12:46:30Z)
• What we learned while automating bias detection in AI hiring systems for compliance with NYC Local Law 144 [0.0]
  New York City's Local Law 144 requires employers to conduct independent bias audits for any automated employment decision tools (AEDTs) used in hiring processes.<n>The law outlines a minimum set of bias tests that AI developers and implementers must perform to ensure compliance.<n>We have collected and analyzed audits conducted under this law, identified best practices, and developed a software tool to streamline employer compliance.
  arXiv  Detail & Related papers  (2024-12-13T14:14:26Z)
• Regulatory Requirements Engineering in Large Enterprises: An Interview Study on the European Accessibility Act [16.39543253517196]
  The European Accessibility Act (EAA) impacts the engineering of software products and services. Enterprises conduct Regulatory Impact Analysis (RIA) to consider the effects of regulations on software products offered and formulate requirements at an enterprise level. Despite its practical relevance, we are unaware of any studies on this large-scale regulatory RE process.
  arXiv  Detail & Related papers  (2024-09-11T14:42:35Z)
• RIRAG: Regulatory Information Retrieval and Answer Generation [51.998738311700095]
  We introduce a task of generating question-passages pairs, where questions are automatically created and paired with relevant regulatory passages.<n>We create the ObliQA dataset, containing 27,869 questions derived from the collection of Abu Dhabi Global Markets (ADGM) financial regulation documents.<n>We design a baseline Regulatory Information Retrieval and Answer Generation (RIRAG) system and evaluate it with RePASs, a novel evaluation metric.
  arXiv  Detail & Related papers  (2024-09-09T14:44:19Z)
• Exterior Penalty Policy Optimization with Penalty Metric Network under Constraints [52.37099916582462]
  In Constrained Reinforcement Learning (CRL), agents explore the environment to learn the optimal policy while satisfying constraints. We propose a theoretically guaranteed penalty function method, Exterior Penalty Policy Optimization (EPO), with adaptive penalties generated by a Penalty Metric Network (PMN) PMN responds appropriately to varying degrees of constraint violations, enabling efficient constraint satisfaction and safe exploration.
  arXiv  Detail & Related papers  (2024-07-22T10:57:32Z)
• Open Problems in Technical AI Governance [102.19067750759471]
  Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI.<n>This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
  arXiv  Detail & Related papers  (2024-07-20T21:13:56Z)
• Co-Matching: Towards Human-Machine Collaborative Legal Case Matching [69.21196368715144]
  Successful legal case matching requires tacit knowledge of legal practitioners, which is difficult to verbalize and encode into machines. We propose a collaborative matching framework called Co-Matching, which encourages both the machine and the legal practitioner to participate in the matching process. Our study represents a pioneering effort in human-machine collaboration for the matching task, marking a milestone for future collaborative matching studies.
  arXiv  Detail & Related papers  (2024-05-16T16:50:31Z)
• Law and the Emerging Political Economy of Algorithmic Audits [1.3654846342364302]
  Digital Services Act (DSA) and Online Safety Act (OSA) have established the framework within which technology corporations and (traditional) auditors will develop the practice' of algorithmic auditing. This paper systematically reviews the auditing provisions in the DSA and the OSA in light of observations from the emerging industry of algorithmic auditing. We warn that ambitious research ideas and technical projects of/for algorithmic auditing may end up crashed by the standardising grip of traditional auditors.
  arXiv  Detail & Related papers  (2024-04-03T19:45:30Z)
• "The Law Doesn't Work Like a Computer": Exploring Software Licensing Issues Faced by Legal Practitioners [7.323456975282423]
  We conducted a survey with 30 legal practitioners and related occupations. We identified different aspects of OSS license compliance from the perspective of legal practitioners. We discuss the implications of our findings.
  arXiv  Detail & Related papers  (2024-03-22T03:07:11Z)
• Legal Requirements Analysis [2.3349787245442966]
  We explore a variety of methods for analyzing legal requirements and exemplify them on representations. We describe possible alternatives for creating machine-analyzable representations from regulations.
  arXiv  Detail & Related papers  (2023-11-23T09:31:57Z)
• The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
  The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
  arXiv  Detail & Related papers  (2023-11-03T12:51:37Z)
• Regulation and NLP (RegNLP): Taming Large Language Models [51.41095330188972]
  We argue how NLP research can benefit from proximity to regulatory studies and adjacent fields. We advocate for the development of a new multidisciplinary research space on regulation and NLP.
  arXiv  Detail & Related papers  (2023-10-09T09:22:40Z)
• From Legal Contracts to Legal Calculi: the code-driven normativity [0.0]
  We show that interpreting legal contracts as interaction protocols between (untrusted) parties enables the generalisation of formal methods and tools for concurrent systems to the legal setting.
  arXiv  Detail & Related papers  (2022-09-06T10:38:19Z)
• Automated Compliance Blueprint Optimization with Artificial Intelligence [1.90073733366566]
  In banking and healthcare, one of the major hindrances to the adoption of cloud computing is compliance with regulatory standards. This is a complex problem due to many regulatory and technical specification (techspec) documents that the companies need to comply with. We present early results to identify the mapping between techspecs and regulation controls, and discuss challenges that must be overcome for this solution to be fully practical.
  arXiv  Detail & Related papers  (2022-06-22T15:59:16Z)
• Normative Disagreement as a Challenge for Cooperative AI [56.34005280792013]
  We argue that typical cooperation-inducing learning algorithms fail to cooperate in bargaining problems. We develop a class of norm-adaptive policies and show in experiments that these significantly increase cooperation.
  arXiv  Detail & Related papers  (2021-11-27T11:37:42Z)
• Tag-based regulation of modules in genetic programming improves context-dependent problem solving [62.997667081978825]
  We introduce and experimentally demonstrate tag-based genetic regulation. Tag-based genetic regulation extends existing tag-based naming schemes. We find that tag-based regulation improves problem-solving performance on context-dependent problems.
  arXiv  Detail & Related papers  (2020-12-16T19:49:28Z)
• Imposing Regulation on Advanced Algorithms [0.0]
  The book examines universally applicable patterns in administrative decisions and judicial rulings. It investigates the role and significance of national and indeed supranational regulatory bodies for advanced algorithms. It considers ENISA, an EU agency that focuses on network and information security, as an interesting candidate for a European regulator of advanced algorithms.
  arXiv  Detail & Related papers  (2020-05-16T20:26:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.