Work, Wellbeing, and Choice: Empirical Lessons for AI Futures

2026-09-09

AI Summary

研究探讨了AI自动化背景下工作与福祉的关系，通过分析不同人群（如失业者、退休人员等）的情况，识别出影响福祉的三个关键因素：自主选择、替代性活动和系统性背景。

Abstract

Advances in AI-driven automation have raised questions about how humans might find wellbeing in a world where paid employment is less necessary or less available than before. Paid work has been variously characterized as both a contributor and an impediment to human wellbeing. What is already known about the relationship between paid work and wellbeing? What factors influence wellbeing among people who do not work---or who do not need to work? And how might these factors bear upon prospective AI-induced economic transformations? To help provide empirical grounding for these questions, we survey the psychological, sociological, and economic literature that investigates the relationship between wellbeing and work. We draw on evidence from multiple populations, including the unemployed, retirees, lottery winners, and financially dependent spouses. This comparative review draws from studies across OECD countries, China, India, and Gulf states. We identify three key factors that mediate the relationship between work status and wellbeing: (1) agency and choice---whether the exit from work is voluntary or involuntary, as well as long-term agency; (2) the availability of alternative sources of work's latent benefits---such as volunteering, hobbies, or state-provisioned employment; and (3) social and systemic context---including cultural norms around work and the robustness of social safety nets. We draw on these three factors to derive specific implications for different AI automation scenarios, connecting the empirical evidence to concrete policy considerations.

Problem

• AI-driven automation
• wellbeing
• paid employment
• economic transformations

Innovation

• AI-driven automation
• wellbeing and work
• agency and choice
• alternative benefits
• social context