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

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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.