Toward understanding the impact of artificial intelligence on labor
Toward understanding the impact of artificial intelligence on labor
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DOI：
10.1073/pnas.1900949116
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发表时间：
2019-04-02
影响因子：
11.1
通讯作者：
Rahwan, Iyad
中科院分区：
文献类型：
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作者：
Frank, Morgan R.;Autor, David;Rahwan, Iyad
人工智能（AI）和自动化技术的快速发展有可能严重扰乱劳动力市场。虽然人工智能和自动化可以提高一些工人的生产力，但它们可以取代其他人所做的工作，并可能至少在某种程度上改变几乎所有职业。自动化程度的提高发生在经济不平等日益加剧的时期，这引发了人们对大规模技术失业的担忧，并再次呼吁采取政策措施来应对技术变革的后果。在本文中，我们讨论了阻碍科学家衡量人工智能和自动化对未来工作的影响的障碍。这些障碍包括缺乏关于工作性质的高质量数据（例如，职业的动态要求），缺乏关键微观过程的经验信息模型（例如，技能替代和人机互补），以及对认知技术如何与更广泛的经济动态和体制机制（例如，城市移民和国际贸易政策）相互作用的理解不足。克服这些障碍需要改进数据的纵向和空间分辨率，以及改进有关工作场所技能的数据。这些改进将使多学科研究能够定量地监测和预测与技术进步相结合的工作的复杂演变。最后，考虑到预测技术变革的基本不确定性，我们建议开发一个决策框架，除了关注一般均衡行为外，还关注对意外情况的弹性。
Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human-machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.