Automation for the Artisanal Economy

• Eglash, R., Robert, L. P., Bennett, A., Robinson, K. P., Lachney, M. Babbitt, W., (accepted in 2019). Automation for the Artisanal Economy: Enhancing the Economic and Environmental Sustainability of Crafting Professions with Human-Machine Collaboration, AI & Society,

URL = https://deepblue.lib.umich.edu/handle/2027.42/150492

"the paper details the possibilities of utilizing AI to support hybrid forms of human-machine production at the micro-scale; localized and sustainable value chains at the meso-scale; and networks of these localized and sustainable producers at the macro scale".

Abstract

"Artificial intelligence (AI) is poised to eliminate millions of jobs, from finance to truck driving. But artisanal products—(e.g. handmade textiles) are valued precisely because of their human origins, and thus have some inherent “immunity” from AI job loss. At the same time, artisanal labor, combined with technology, could potentially help to democratize the economy, allowing independent, small scale businesses to flourish. Could AI, robotics and related automation technologies enhance the economic viability and environmental sustainability of these beloved crafting professions, perhaps even expanding their niche to replace some job loss in other sectors? In this paper we compare the problems created by the current mass production economy, and potential solutions from an artisanal economy. In doing so, the paper details the possibilities of utilizing AI to support hybrid forms of human-machine production at the micro-scale; localized and sustainable value chains at the meso-scale; and networks of these localized and sustainable producers at the macro scale. In short, a wide range of automation technologies are potentially available for facilitating and empowering an artisanal economy. Ultimately, it is our hope that this paper will facilitate a discussion on a future vision for more “generative” economic forms in which labor value, ecological value and social value can circulate without extraction or alienation."

Contents

"Artisanal labor, combined with technology, could potentially help to democratize the economy, allowing independent, small scale businesses to flourish (Diez and Posada 2013). Finally, many artisans strive to be more environmentally sustainable, using “green" supply chains and techniques. Could AI, robotics and related automation technologies enhance the economic viability and environmental sustainability of these beloved crafting professions, perhaps even expanding their niche to replace some job loss in other sectors?

Part 2 of this paper will compare the problems created by the current mass production economy, and potential solutions from an artisanal economy. We show that mass production problems may be exacerbated by automation, and that these are generally problems of extraction. The problems can be generally classified as the extraction of labor value from workers; the extraction of ecological value from nature; and the extraction of social value from civic activity. We then review the potential for solutions in an artisanal economy: replacing extraction with a generative network in which value circulates in unalienated forms. Hence the need for new forms of automation that can scale up these generative alternatives.

Parts 3, 4, and 5 of this paper details the possibilities of automation technologies for facilitating and empowering an artisanal economy at three scales:

• Part 3: At the economic micro-scale, we examine how human-machine collaboration can

sustain and empower the kinds of “unalienated” (enjoyable, meaningful) labor tasks that make artisanal jobs attractive. In particular, our findings show distinctly different outcomes from that of Gombolay et al (2015). In their scenario, workers preferred to cede task control to automated machines, which modelled context of mass production. Our initial experiments with humanmachine collaborations situated in African American, African, and Native American artisanal traditions (Eglash et al. forthcoming; Lachney et al. forthcoming) show distinctly different preferences depending on the context.

• Part 4: At the meso-scale, we examine how automation technologies--in particular AIbased pattern recognition--could be used to help consumers authenticate product origins and producers improve fabrication sustainability.

• Part 5: Finally, at the macro-scale, we provide a brief review of the ways that natural

language processing, network optimization algorithms and related technologies might be deployed to develop a robust technosocial ecosystem for the artisanal economy as a whole.

In the conclusion of this paper, we will summarize the above analysis, and provide some directions forward. It is our hope that this research will move discussions beyond the exclusive focus on “green tech” often occurring in literature on “circular economies” or “industrial symbiosis”. We propose that AI could play a transformative role towards futures in which unalienated labor value, unalienated ecological value, and unalienated social value circulate in mutually supporting networks; what we have defined elsewhere (Eglash 2015) as generative justice."

Excerpts

Cobots, i.e. Collaborative Robots

Ron Eglash et al.:

"Recent literature on human-machine work collaboration has frequently focused on collaborative robots (“cobots”), where humans and robots work together side by side to accomplish shared work goals (Colgate et al. 1996; Peshkin and Colgate 1999; You et al. 2018). Human-robot work collaboration is being offered as a potential solution to the fear of massive job losses due to automation. But, as we detail below, our preliminary research shows two different faces to this work. As an example of public-facing discourse, Rethink Robotics has produced TED talks and other media showing how “cobots” allow workers to continue in a new collaborative role.

But in their industry-facing discourse, CEO Scott Eckert’s blog (Eckert 2016) explicitly positions cobots as solving “the rising cost of labor”, implying massive layoffs. Indeed robots are expected to replace nearly half the human workforce in 10‒20 years (Ackerman. 2014, Owais et al. 2014; Webster, 2014). In many cases robots will entirely replace their human counterparts. For example, robotic process automation (RPA) provides “digital workers” which can both perform the work of humans and manage other “digital workers.” (Lacity & Willcocks, 2016; Le Clair, 2017)." (https://deepblue.lib.umich.edu/handle/2027.42/150492)