Assessing employment and labour issues implicated by using AI
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arXiv
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| Format: | Preprint |
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2025
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| author | Willems, Thijs Hotan, Darion Jin Tang, Jiawen Cheryl Norhashim, Norakmal Hakim bin Poon, King Wang Goh, Zi An Galvyn Vinod, Radha |
| author_facet | Willems, Thijs Hotan, Darion Jin Tang, Jiawen Cheryl Norhashim, Norakmal Hakim bin Poon, King Wang Goh, Zi An Galvyn Vinod, Radha |
| contents | This chapter critiques the dominant reductionist approach in AI and work studies, which isolates tasks and skills as replaceable components. Instead, it advocates for a systemic perspective that emphasizes the interdependence of tasks, roles, and workplace contexts. Two complementary approaches are proposed: an ethnographic, context-rich method that highlights how AI reconfigures work environments and expertise; and a relational task-based analysis that bridges micro-level work descriptions with macro-level labor trends. The authors argue that effective AI impact assessments must go beyond predicting automation rates to include ethical, well-being, and expertise-related questions. Drawing on empirical case studies, they demonstrate how AI reshapes human-technology relations, professional roles, and tacit knowledge practices. The chapter concludes by calling for a human-centric, holistic framework that guides organizational and policy decisions, balancing technological possibilities with social desirability and sustainability of work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06322 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessing employment and labour issues implicated by using AI Willems, Thijs Hotan, Darion Jin Tang, Jiawen Cheryl Norhashim, Norakmal Hakim bin Poon, King Wang Goh, Zi An Galvyn Vinod, Radha Computers and Society Artificial Intelligence Human-Computer Interaction This chapter critiques the dominant reductionist approach in AI and work studies, which isolates tasks and skills as replaceable components. Instead, it advocates for a systemic perspective that emphasizes the interdependence of tasks, roles, and workplace contexts. Two complementary approaches are proposed: an ethnographic, context-rich method that highlights how AI reconfigures work environments and expertise; and a relational task-based analysis that bridges micro-level work descriptions with macro-level labor trends. The authors argue that effective AI impact assessments must go beyond predicting automation rates to include ethical, well-being, and expertise-related questions. Drawing on empirical case studies, they demonstrate how AI reshapes human-technology relations, professional roles, and tacit knowledge practices. The chapter concludes by calling for a human-centric, holistic framework that guides organizational and policy decisions, balancing technological possibilities with social desirability and sustainability of work. |
| title | Assessing employment and labour issues implicated by using AI |
| topic | Computers and Society Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2504.06322 |