FW-HTF-R: Race, Gender and Class Equity in the Future of Work: Automation for the Artisanal Economy

人工智能有望消除数百万个工作岗位，从金融到卡车驾驶。但手工产品和劳动力--如手工纺织品、家具、装饰品、食品和修理店--正是因为它们的人类起源而受到重视，因此对人工智能的失业具有某种固有的“免疫力”。他们往往更享受。虽然人工智能可以（也应该）取代的许多工作都是枯燥或危险的，但手工劳动处于另一端：一些最令人满意的职业可能。许多工匠努力提高环境可持续性，使用“绿色”供应链和技术。但最重要的是，手工业是少数几个所有权可以在基层找到的行业之一。从美容院到汽车细节，从民族食品到修理店，我们发现种族（黑人，土著人，拉丁裔），性别（女性）和社会经济地位（所有种族的穷人）代表性不足的群体比其他公司更有可能拥有非员工业务。新形式的自动化--人工智能、机器人技术等--现在正在为大规模生产环境开发。这项研究将努力使这些新形式的自动化适应小型非雇员企业的使用，以提高生产率，剧目，产品质量和可持续性，为这个更多样化的人口。它将致力于通过多样化的手工企业生态系统来加强财富公平，利用信息技术创新来促进供应链，营销和其他方面的合作。该项目将开发新的理论和知识，解决两个主要的研究问题。(1)人工智能、机器人技术和相关自动化技术如何通过提高手工生产和服务的能力来增强代表性不足群体的公平性？(2)与基层参与者的合作创新如何扩大他们的利基，使我们更接近循环经济;一个赋予他们劳动价值的经济？这些研究问题将使用四个步骤的方法进行调查。(1)与底特律的工匠合作者一起开发可立即使用的实际应用程序，专注于数字制造。先前的研究表明，许多手工艺实践在其方法中包括计算思维（例如编织中的迭代）。通过模拟这些“遗产算法”，我们可以测试策略，使心爱的文化传统与数字制造相结合，并从那里开发新产品，技能和创新的培训机会和资源。我们的假设是，相反的大规模生产的情况下，没有一个单一的最佳人机任务分配在手工领域。相反，我们假设有各种各样的策略，适用于不同的环境。(2)利用这些经验的反馈，在未来与手工经济网络的合作中进行小规模实验，例如平台合作社和使用人工智能指导可持续消费和供应链。 (3)设计，开发和评估社区资产映射数据库，用于跟踪组织间联盟，供应链，创业孵化和潜在手工经济网络的其他要素的变化。其目的是评估更广泛的愿景，即自动化授权的手工劳动如何帮助向循环、非采掘经济的总体转型。(4)扩大研究范围，包括全国各地的手工艺团体。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

AI is poised to eliminate millions of jobs, from finance to truck driving. But artisanal products and labor—such as handmade textiles, furnishings, adornments, foods, and repair shops—are valued precisely because of their human origins, and thus have some inherent “immunity” from AI job loss. And they are often more enjoyable. While many of the jobs AI can (and should) replace are dull or dangerous, artisanal labor is at the other end of the spectrum: some of the most satisfying professions possible. Many artisans strive to be more environmentally sustainable, using “green" supply chains and techniques. But most importantly, artisanal business is one of the few sectors where ownership can be found at the grassroots. From beauty salons to auto detailing; ethnic foods to repair shops, we find that groups underrepresented by race (Black, Native, Latinx); by gender (women) and by socioeconomic status (poor people of all ethnicities) are more likely to own non-employee businesses than other companies. New forms of automation--AI, robotics, and others--are now being developed for mass-production contexts. This research will work to adapt these new forms of automation for use by small non-employee businesses, in order to enhance production rates, repertoires, product quality and sustainability for this more diverse demographic. It will work toward enhancing wealth equity through a diverse ecosystem of artisanal enterprises, utilizing innovations in information technology to foster collaborations in supply chains, marketing and other dimensions. This project will develop new theory and knowledge addressing 2 primary research questions. (1) How can AI, robotics and related automation technologies enhance equity for underrepresented groups by enhancing the capabilities of artisanal production and services? (2) How can collaborative innovation with grassroots participants expand their niche to move us closer to a circular economy; one that empowers their labor value? These research questions will be investigated using a four step approach. (1) Develop practical applications for immediate use with artisan collaborators in Detroit, focusing on digital fabrication. Prior studies show that many artisanal practices include computational thinking in their approach (iteration in weaving for example). By simulating these “heritage algorithms” we can test strategies to enable the blending of beloved cultural traditions with digital fabrication, and from there develop training opportunities and resources for new products, skills, and innovation. The hypothesis is that, contrary to mass production scenarios, there is no single optimum for human-machine task allocation in the artisanal domain. Instead, we hypothesize a wide diversity of strategies that are optimal for different contexts. (2) Utilize feedback from these experiences to run small scale experiments in the future of work with artisanal economic networks, such as platform cooperatives and the use of AI in guiding sustainable consumption and supply chains. (3) Design, develop, and evaluate a community asset mapping database for tracking changes in inter-organizational alliances, supply chains, entrepreneurship incubation, and other elements of the potential artisanal economy network. The intention is to enable the evaluation of a broader vision for how automation-empowered artisanal labor could aid the general transformation to a circular, non-extractive economy. (4) Expand the research context to include artisanal groups across the nation. This will address the generalizability of the project’s emerging theoretical framework, and support dissemination of its open-source technologies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.