Enterprise AI Analysis
Research on the rights protection mechanism of gig workers under algorithmic management based on Citespace
This research utilizes quantitative analysis and CiteSpace to visually analyze international literature (2015-2025) on gig workers' rights protection under algorithmic management. It identifies a shift in research focus from basic labor issues to multi-dimensional social equity, algorithm ethics, mental health, and digital human rights. Key deficiencies include algorithm transparency, legal framework, and labor participation. The study proposes solutions focusing on legal frameworks, technical governance, management innovation, and social support systems to guide effective rights protection.
Key Insights & Impact
Understanding the core challenges and opportunities in protecting gig workers' rights through advanced analytical methods.
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Topics Covered
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Proposed Solutions
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Introduction
Research Methodology
International Research Trends
Recommendations
The gig economy is booming, with China expecting 200 million flexible workers by 2024. While it offers flexible jobs and reduces costs, it faces significant challenges like blurred labor relations, insufficient rights protection, and algorithmic bias (e.g., gender discrimination in ride-hailing drivers' income). Existing research often falls short in areas like work-related injury insurance, labor relation definitions, and effective appeal channels against algorithmic decisions. This study aims to fill these gaps by analyzing global research trends using CiteSpace from 2015 to 2025.
This study employs quantitative analysis methods and the CiteSpace literature analysis tool. CiteSpace, a scientometric tool, visualizes literature to identify research hotspots, trends, and knowledge structures. Its advantage lies in visualizing macro-development patterns based on existing literature. The analysis covers keyword co-occurrence, cluster analysis, and timeline analysis of research literature on platform workers' labor rights protection under algorithmic management, using data from the Web of Science (WOS) core collection with a search time of 2015-2025.
International research on gig worker rights under algorithmic management shows a clear phased evolution. Initially (2018-2019), it focused on 'algorithmic despotism' and 'digitalization of the labor process,' exploring how algorithms exert soft control via task allocation and price fluctuations. Later (2019 onwards), the focus shifted to data privacy protection and platform power, addressing issues like colonial use of worker data and GDPR compliance. More recently (2022-2025), themes include health/safety, crisis response, human rights frameworks, and global governance, highlighting a paradigm shift from 'criticizing algorithm control' to 'building system rights protection.'
The study proposes several recommendations: Government involvement in policy-making, transforming from 'regulator' to 'dynamic governance coordinator' by enforcing algorithm registration, real-time fairness monitoring, and audit tools. Establishing an 'algorithm fuse mechanism' for emergencies (e.g., minimum income guarantee) and promoting multi-party co-governance (platforms + workers + technical experts) are crucial. Furthermore, clarifying platform 'employer-like responsibilities' through legislation and cross-border supervision is essential. Worker consciousness advancement is needed to improve data literacy and bargaining power through collective actions. Finally, strengthening social care involves evolving from 'one-way' to 'interactive governance' by promoting algorithm transparency, third-party audits, and worker participation in algorithm design and iteration. Building social support networks with legal and psychological aid, and welfare funds, will provide timely relief.
200M
Flexible Workers in China by 2024
Enterprise Process Flow
Traditional Regulator
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Dynamic Governance Coordinator
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Enforce Algorithm Transparency
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Promote Multi-party Co-governance
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Strengthen Social Support
| Challenge | Traditional Approach | AI-Enhanced Solution |
|---|---|---|
| Algorithm Opacity | Limited visibility into decision-making |
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| Labor Relations Ambiguity | Difficulty in defining employer responsibilities |
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| Worker Vulnerability | Income insecurity, lack of social security |
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Case Study: Addressing Algorithmic Bias in Ride-Hailing
Platform data in ride-hailing shows that female gig workers often earn less than male counterparts, with limited time for orders due to family responsibilities. This leads to lower platform scores and income, indicating inherent gender bias in algorithm design that favors 'ideal employees.' The study highlights that structural contradictions arise from the interweaving of algorithm technology and social/cultural factors. Addressing this requires not just technical fixes but also social and legal interventions to promote gender-neutral dispatch and fair income distribution.
Calculate Your Potential ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by optimizing algorithmic management and worker rights protection.
Estimated Annual Savings
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Implementation Roadmap
A structured approach to integrating advanced algorithmic governance and rights protection into your enterprise.
Phase 1: Discovery & Assessment
Conduct a comprehensive audit of existing algorithmic management practices and their impact on gig workers. Identify areas of non-compliance and potential bias.
Phase 2: Strategy & Design
Develop a tailored algorithmic governance framework, including transparency protocols, fairness metrics, and worker participation mechanisms. Integrate legal and ethical guidelines.
Phase 3: Technology Integration & Pilot
Implement necessary technical tools for algorithmic auditing, real-time monitoring, and dispute resolution. Pilot the new system with a subset of your workforce for feedback.
Phase 4: Scaling & Continuous Improvement
Roll out the full solution across your enterprise, establish ongoing training for workers and managers, and set up a feedback loop for continuous algorithmic refinement and policy adaptation.
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