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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. 

One of the challenges facing AI governance is the need for multiple scales. Universal human rights require a global scale. If someone asks AI if education is harmful to women, the answer should be “no” regardless of their location. But economic democratization requires local control: if AI’s power over an economy is dictated by corporate giants or authoritarian states, it may degrade democracy’s social and environmental foundations. AI democratization, in other words, needs to operate across multiple scales. Nature allows the multiscale flourishing of biological systems through fractal distributions. In this paper, we show that key elements of the fractal scaling found in nature can be applied to the AI democratization process. We begin by looking at fractal trees in nature and applying similar analytics to tree representations of online conversations. We first examine this application in the context of OpenAI’s “Democratic Inputs” projects for globally acceptable policies. We then look at the advantages of independent AI ownership at local micro-levels, reporting on initial outcomes for experiments with AI and related technologies in community-based systems. Finally, we offer a synthesis of the two, micro and macro, in a multifractal model. Just as nature allows multifractal systems to maximize biodiverse flourishing, we propose a combination of community-owned AI at the micro-level, and globally democratized AI policies at the macro-level, for a more egalitarian and sustainable future. 

The Latin roots of the word reparations are “re” (again) plus “parere” which means “to give birth to, bring into being, produce”. Together they mean “to make generative once again”. In this sense, the extraction processes that cause labor injustice, ecological devastation, and social degradation cannot be repaired by simply transferring money. Reparations need to take on the full sense of “restorative”: the transition to a decolonial system that can support value generators in the control of their own systems of production, protect the value they create from extraction, and circulate value in unalienated forms that benefit the human and non-human communities that produced that value. With funding from the National Science Foundation, we have developed a research framework for this process that starts with “artisanal labor”: employee-owned business and worker collectives that have people doing what they love, despite low incomes. Focusing primarily on Detroit's Black-owned urban farms, artisanal textile businesses, Black hair salons, worker collectives, and other community-based production, with additional connections to Indigenous and other communities, we have introduced digital fabrication technologies, sensors, artificial intelligence, server-side apps and other computational support for a transition to unalienated circular value flow. We will report on our investigations with the challenges at multiple scales. At each level, we show how computational supports can act as restorative mechanisms for lost circular value flows, and thus address both past and ongoing disenfranchisement. 

Artificial Intelligence (AI) can be a threat to creative arts and design, taking data and images without permission or compensation. But with AI becoming a global portal for human knowledge access, anyone resisting inclusion in its data inputs will become invisible to its outputs. This is the AI double bind, in which the threat of exclusion forces us to give up any claims of ownership to our creative endeavors. To address such problems, this project develops an experimental platform designed to return value to those who create it, using a case study on African arts and design. If successful, it will allow African creatives to work with AI instead of against it, creating new opportunities for funding, gaining wider dissemination of their work, and creating a database for machine learning that results in more inclusive knowledge of African arts and design for AI outputs.