Grassroots Innovation Systems for the Post-Carbon World: Promoting Economic Democracy, Environmental Sustainability, and the Public Interest

This article uses a sociotechnical systems approach to advocate for an alternative way of thinking about the role of innovation in international development efforts, specifically those focused on environmental sustainability and a post-carbon world. This approach views technology and society as inextricably linked, highlighting how particular values, norms, individual rights and responsibilities, social practices and relationships, and aspects of political culture are embedded in the design, development, implementation, and use of technology. Using the example of clean cookstoves, this article argues that technologies customarily deployed to achieve international development goals are embedded in particular values, assumptions, and social structures that together make up a “dominant approach to innovation.” This dominant approach, which reflects Western attitudes towards science, technology, and markets, is often inappropriate for developing world circumstances. This article suggests that two grassroots innovation systems developed in India—by the Honeybee Network and the Self-Employed Women’s Association—provide us with some clues as to how we might rethink innovation to achieve development in lower income contexts. These grassroots innovation systems encourage technological development among lower income, often socially marginalized individuals with limited formal education, suggesting that these technologies might be more useful for the local public interest. They also encourage widespread dissemination of these innovative ideas in order to facilitate implementation and encourage innovation within the community. By challenging our traditional understandings of innovation, innovators, and the relationship between technology and societal benefit, these grassroots innovation systems offer a viable path to engage lower income communities in successful innovation for a post-carbon world

WHAT’S IN THE CHATTERBOX? LARGE LANGUAGE MODELS, WHY THEY MATTER, AND WHAT WE SHOULD DO ABOUT THEM

Large language models (LLMs)—machine learning algorithms that can recognize, summarize, translate, predict, and generate human languages on the basis of very large text-based datasets—are likely to provide the most convincing computer-generated imitation of human language yet. Because language generated by LLMs will be more sophisticated and human-like than their predecessors, and because they perform better on tasks for which they have not been explicitly trained, we expect that they will be widely used. Policymakers might use them to assess public sentiment about pending legislation, patients could summarize and evaluate the state of biomedical knowledge to empower their interactions with healthcare professionals, and scientists could translate research findings across languages. In sum, LLMs have the potential to transform how and with whom we communicate.The Technology Assessment Project is supported in part through a generous grant from the Alfred P. Sloan Foundation (grant #G-2021-16769)http://deepblue.lib.umich.edu/bitstream/2027.42/191718/1/large-language-models-TAP-2022-final-051622.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/191718/2/LLMImplicationsforScience.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/191718/3/Large Language Models Executive Summary 2022.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/191718/4/large-language-models-one-pager STPP-TAP-2022-v3.pdfDescription of large-language-models-TAP-2022-final-051622.pdf : What’s in the Chatterbox? Large Language Models, Why They Matter, and What We Should Do About ThemDescription of LLMImplicationsforScience.pdf : Implications for the Scientific Landscape (31 pages)Description of Large Language Models Executive Summary 2022.pdf : Executive Summary- LLMDescription of large-language-models-one-pager STPP-TAP-2022-v3.pdf : One-pager LLMSEL