Assessing employment and labour issues implicated by using AI
This work addresses the problem of understanding AI's broader effects on work for researchers and policymakers, but it is incremental as it builds on existing critiques without introducing a new paradigm.
The authors critique the reductionist approach in AI and work studies, advocating for a systemic perspective that emphasizes task interdependence and workplace contexts, and propose two complementary methods to assess AI's impact on employment and labor issues.
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.