Search for: All records

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. 

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. 

Extractive economies pull value from a system without restoring it. Unsustainable extraction of ecological value includes over-fishing, clear-cut logging, etc. Extraction of labor value is similarly objectionable: assembly line jobs for example increase the likelihood of cardiovascular disease, depression, suicide and other problems. Extraction of social value-vacuuming up online personal information, commodification of the public sphere, and so on-constitutes a third form. But all three domains-ecological value, labor value, and social value-can thrive in unalienated forms if we can create a future of work that replaces extraction with generative cycles. AI is a key technology in developing these alternative economic forms. This paper describes some initial experiments with African, African American, and Native American artisans who were willing to experiment with the introduction of computational enhancements to their work. Following our report on these initial results, we map out a vision for how AI could scale up labor that sustains "heritage algorithms", ecologically situated value chains and other hybrid forms that prevent value alienation while flourishing from its robust circulation.