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Abstract Search engine algorithms are increasingly subjects of critique, with evidence indicating their role in driving polarization, exclusion, and algorithmic social harms. Many proposed solutions take a top-down approach, with experts proposing bias-corrections. A more participatory approach may be possible, with those made vulnerable by algorithmic unfairness having a voice in how they want to be “found.” By using a mixed methods approach, we sought to develop search engine criteria from the bottom-up. In this project we worked with a group of 16 African American artisanal entrepreneurs in Detroit Michigan, with a majority female and all from low-income communities. Through regular in-depth interviews with select participants, they highlighted their important services, identities and practices. We then used causal set relations with natural language processing to match queries with their qualitative narratives. We refer to this two-step process-- deliberately focusing on social groups with unaddressed needs, and carefully translating narratives to computationally accessible forms--as a “content aware” approach. The resulting content aware search outcomes place themes that participants value, in particular greater relationality, much earlier in the list of results when compared with a standard Web search. More broadly, our use of participatory design with “content awareness” adds evidence to the importance of addressing algorithmic bias by considering who gets to address it; and, that participatory search engine criteria can be modeled as robust linkages between interviews and semantic similarity using causal set relations. 

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 microscale; localized and sustainable value chains at the mesoscale; and networks of these localized and sustainable producers at the macroscale. 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.