Racial Equity and the Future of Work
Racial Equity and the Future of Work
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种族平等和工作的未来
DOI：
10.1080/24751448.2020.1705711
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Branch, Enobong H.
中科院分区：
文献类型：
--
作者：
Renski, Henry;Smith-Doerr, Laurel;Wilkerson, Tiamba;Roberts, Shannon C.;Zilberstein, Shlomo;Branch, Enobong H.
自动化正在以深刻影响社会和建筑环境的方式改变工作的性质。[1]最近的一项研究估计，47%的美国工作在不久的将来会面临计算机化的风险（Frey and Osborne 2017）。报告和新闻文章描述了扼杀工作的机器人的发展，他们对人工智能（AI）是否意味着工作的结束发表了看法（Kessler 2018; Rifkin 1995）。尽管确实有理由担心，但这种可怕的预测往往没有考虑到自动化和其他形式的节省劳动力的技术并不简单地取代人类;它们还使人类能够进行新的活动并创造全新的工作形式（Autor et al. 2019; Woods 1994）。例如，办公任务的自动化（例如接听电话或复印）导致对秘书的需求下降，同时增加了高管的工作量（Rifkin 1995）。自动化机器人导致工厂车间的工人减少，但为软件工程师创造了对机器人进行编程的工作岗位（NASEM 2017）。同样，自动化是驾驶方面的计算机化，减少了对长途卡车司机的需求，同时增加了对后备工程师的需求，以监控自动化（Fagnant和Kockelman 2015）。相关讨论本质上并不是关于工作的结束，而是关于在人工智能改造的劳动力中谁赢谁输（Autor等人，2019）。学者和政策制定者才刚刚开始考虑自动化对公平的影响。特别令人感兴趣的是新兴技术是否会加剧种族，民族和阶级不平等的社会问题，或者有助于减少这些问题（NASEM 2017）。我们有充分的理由感到担忧。特别是西班牙裔，在自动化风险高的工作中所占比例过高，如运输，生产，行政支持和食品准备（图1）。同样，非裔美国人和西班牙裔美国人在科学、工程和知识工作中的代表性都很低，这些工作不仅最不容易受到自动化的影响，而且主要负责设计潜在的替代技术（图2）。空间隔离和新兴的知识经济地理使这些问题更加复杂，知识经济有利于技能丰富的地区，而使弱势群体进一步孤立。这并不是说所有代表性不足的人口都将受到技术发展的不利影响。例如，人工智能和相关技术的新发展为许多身体和认知挑战的人提供了更多的机会，这些挑战在历史上阻碍了他们充分参与劳动力市场。[2]然而，在这篇论文和我们的相关研讨会中，我们特别关注种族划分，并在较小程度上关注民族或阶级划分。为了帮助解决这一缺陷，作者在2018年春季召集了一系列研讨会，由美国国家科学基金会（NSF）资助。前两次研讨会汇集了社会科学、计算科学和工程学方面的学术专家。其目标是阐明一个研究议程，以了解与自动化和工作的未来有关的挑战，并设想如何塑造新兴技术，为更广泛的工人带来“好”工作。然后，我们提出了我们的初步发现，
Automation is changing the nature of work in ways that are profoundly impacting the social and built environments. 1 One recent study estimates that 47% of US jobs are at near-future risk of becoming computerized (Frey and Osborne 2017). Reports and news articles profile the development of job-killing robots, and they pontificate on whether artificial intelligence (AI) means the end of work (Kessler 2018; Rifkin 1995). Although there is truly cause for concern, such dire predictions often fail to consider that automation and other forms of labor-saving technologies do not simply replace humans; they also enable humans to perform new activities and create entirely new forms of work (Autor et al. 2019; Woods 1994). For example, the automation of office tasks (eg, answering phones or making copies) led to a decline in the need for secretaries while increasing work for executives (Rifkin 1995). Automated robots led to fewer workers on the factory floor but created jobs for software engineers to program the robots (NASEM 2017). Similarly, automation is computerizing aspects of driving, reducing the need for long-haul truck drivers while increasing the need for a backup engineer to monitor the automation (Fagnant and Kockelman 2015). The relevant discussion is not about the end of work, per se, but rather who wins and loses in the AI-transformed workforce (Autor et al. 2019). Scholars and policy makers are only beginning to consider the equity implications of automation. Of special interest are questions about whether emergent technologies will exacerbate societal problems of racial, ethnic, and class inequality or help reduce them (NASEM 2017). There is good cause for concern. Hispanics, in particular, are overrepresented in jobs at high risk of automation, such as transportation, production, administrative support, and food preparation (Figure 1). Likewise, African Americans and Hispanics are both highly underrepresented in the science, engineering, and knowledge jobs that are not only the least susceptible to automation but also largely responsible for designing the potentially displacing technologies (Figure 2). These problems are compounded by spatial segregation and the emergent geography of the knowledge economy that favors skill-rich areas while leaving vulnerable populations further isolated. This is not to say that all underrepresented populations will be adversely affected by technological developments. For example, new developments in AI and related technologies are increasing opportunities for many with physical and cognitive challenges that have historically prevented them from fully participating in the labor market. 2 However, in this paper and our related workshop, we focused specifically on racial and, to a lesser extent, ethnic or class divisions.To help address this deficiency, the authors convened a series of workshops, funded by the National Science Foundation (NSF), during the spring of 2018. The first two workshops brought together academic experts in the social sciences, computational sciences, and engineering. The goal was to articulate a research agenda for understanding the challenges pertaining to automation and the future of work and for envisioning ways to shape emergent technologies that result in “good” jobs for a wider range of workers. We then presented our initial findings before
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