AI对就业的影响不能只看自动化率，还要看工作关系与专业能力的重塑。
Assessing employment and labour issues implicated by using AI
- 用系统视角看任务、角色与工作环境的相互依赖
- 案例显示AI改变人机关系与隐性知识实践
- 适合政策制定者和组织管理者参考
本文批判了当前人工智能与工作研究中将任务和技能简化为可替代单元的主流方法，主张采用系统性视角，强调任务、角色与工作情境之间的相互依存关系。提出两种互补方法：一是注重语境的民族志研究，揭示AI如何重构工作环境与专业知识；二是基于关系的任务分析，连接微观工作描述与宏观劳动力趋势。作者认为，有效的AI影响评估应超越自动化率预测，纳入伦理、福祉与专业能力相关问题。通过实证案例，展示了AI如何重塑人机关系、职业角色与隐性知识实践。最后呼吁建立以人为本、全面融合的技术框架，指导组织与政策决策，在技术可能性与社会可接受性及工作可持续性之间取得平衡。
原文摘要 · Abstract (English)
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.
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