ILO Working Paper 144
Global case studies of social dialogue on AI and algorithmic management
This working paper explores how social dialogue is shaping the use of AI and algorithmic tools in workplaces across five continents. Through case studies, it highlights how worker representatives are influencing AI-related decisions in areas like employment, algorithmic management, and working conditions—pushing for more equitable and inclusive approaches to AI adoption.
Abstract
Employers are adopting and refining artificial intelligence (AI) and algorithm-based tools in the workplace, with wide-ranging implications for work and employment. This working paper examines case studies of social dialogue on AI at national, regional, sectoral, company, and workplace levels in Europe, North America, Asia, South America and the Caribbean, and Africa. Findings are organized around three distinct ‘action fields’ in which worker representatives have sought to influence strategies and outcomes associated with the growing use of AI and algorithms in the workplace. These include the employment and skill impacts of AI, algorithmic management practices, and working conditions and rights in AI value chains. Across these action fields, social dialogue is playing a crucial role in encouraging an alternative, high road approach to AI investments and uses, based on complementing rather than replacing worker skills, empowering rather than controlling the workforce, and embedding rather than displacing new jobs in labor and social protections. Comparative findings suggest that these social dialogue initiatives are more effective where there are constraints on employer exit, support for collective worker voice, and strategies of inclusive labor solidarity.
Additional details
Author(s)
- Virginia Doellgast
- Shruti Appalla
- Dina Ginzburg
- Jeonghun Kim
- Wen Li Thian
References
- DOI: https://doi.org/10.54394/VOQE4924
- ISBN Print: 9789220421673
- ISBN Web PDF: 9789220421680
- ISBN EPUB: 9789220421697
- ISBN HTML: 9789220421703
- ISSN Print: 2708-3438
- ISSN Online: 2708-3446