发表机构
Google DeepMind（谷歌DeepMind）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文综述心理学、社会学和经济学文献，识别能动性、替代益处和社会背景三个关键因素，为AI自动化情景下的工作与福祉关系提供经验依据和政策启示。
AI 中文摘要
AI驱动的自动化进步引发了关于人类在一个有偿就业变得不那么必要或不那么可及的世界中如何寻找福祉的问题。有偿工作一直被不同地描述为人类福祉的贡献者和障碍。关于有偿工作与福祉之间的关系，我们已知什么？哪些因素影响那些不工作——或不需要工作——的人的福祉？这些因素又如何可能影响未来AI引发的经济转型？为了为这些问题提供经验基础，我们综述了研究福祉与工作之间关系的心理学、社会学和经济学文献。我们利用了来自多个群体的证据，包括失业者、退休人员、彩票中奖者和经济依赖型配偶。这一比较性综述借鉴了来自OECD国家、中国、印度和海湾国家的研究。我们识别出三个调节工作状态与福祉之间关系的关键因素：（1）能动性与选择——退出工作是否自愿或非自愿，以及长期能动性；（2）工作潜在益处的替代来源的可及性——例如志愿服务、爱好或国家提供的就业；（3）社会与系统性背景——包括围绕工作的文化规范和社会安全网的稳健性。我们利用这三个因素为不同的AI自动化情景推导出具体启示，将经验证据与具体的政策考量联系起来。
英文摘要
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.
