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

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

Cameras in the Classroom: Facial Recognition Technology in Schools

Facial recognition (FR) technology was long considered science fiction, but it is now part of everyday life for people all over the world. FR systems identify or verify an individual’s identity based on a digitized image alone, and are commonly used for identity verification, security, and surveillance in a variety of settings including law enforcement, commerce, and transportation. Schools have also begun to use it to track students and visitors for a range of uses, from automating attendance to school security. FR can be used to identify people in photos, videos, and in real time, and is usually framed as more efficient and accurate than other forms of identity verification. However, a growing body of evidence suggests that it will erode individual privacy and disproportionately burden people of color, women, people with disabilities, and trans and gender non-conforming people. In this report, we focus on the use of FR in schools because it is not yet widespread and because it will impact particularly vulnerable populations. We analyze FR’s implications using an analogical case comparison method. Through an iterative process, we developed historical case studies of similar technologies, and analyzed their social, economic, and political impacts, and the moral questions that they raised. This method enables us to anticipate the consequences of using FR in schools; our analysis reveals that FR will likely have five types of implications: exacerbating racism, normalizing surveillance and eroding privacy, narrowing the definition of the “acceptable” student, commodifying data, and institutionalizing inaccuracy. Because FR is automated, it will extend these effects to more students than any manual system could. On the basis of this analysis, we strongly recommend that use of FR be banned in schools. However, we have offered some recommendations for its development, deployment, and regulation if schools proceed to use the technology.http://deepblue.lib.umich.edu/bitstream/2027.42/191755/1/cameras_in_the_classroom_full_report.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/191755/2/cameras_in_the_classroom_executive_summary.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/191755/3/cameras_in_the_classroom_one-pager_0.pdfDescription of cameras_in_the_classroom_full_report.pdf : Full ReportDescription of cameras_in_the_classroom_executive_summary.pdf : Executive Summary- Cameras in the ClassroomDescription of cameras_in_the_classroom_one-pager_0.pdf : One-pager: Cameras in the ClassroomSEL

In Communities We Trust Institutional Failures and Sustained Solutions for Vaccine Hesitancy

In winter 2020, a novel coronavirus (SARS- CoV-2) that caused COVID-19 started its spread across the globe, and by July 2020, over 500,000 people worldwide had died of the disease. By March 2021, there were over 120 million cases and over 2.8 million deaths. To combat the pandemic and return to “normalcy”, experts estimate that at least 80% of the world’s population needs to be resistant to the virus, and most of the world’s population will require vaccination. This will be a challenge. In addition to facilitating widespread distribution, governments will need to combat “vaccine hesitancy”: an individual’s reluctance to get vaccinated or vaccinate their children. In the United States, 71% of the adult population says it is willing to get vaccinated, and the numbers are much lower in Europe (Ipsos & World Economic Forum, 2020; Summers, 2021). Contrary to popular belief, not all vaccine hesitancy is the same. Nor is it simply the result of ignorance or antipathy towards science. At its root, vaccine hesitancy is about institutional mistrust. Communities question whether their governments, and scientific, technological, and medical institutions, really represent their needs and priorities. Long legacies of mistreatment of marginalized communities further fuels this mistrust. In this report, we examine analogical case studies that help us understand the roots of institutional distrust and ultimately, vaccine hesitancy. This method allows us to systematically analyze previous examples of the relationships between science, technology, policy, and society to understand the consequences and challenges of new technology. Our analysis identifies sources of public mistrust and anticipates better approaches for establishing community trust, especially for those from marginalized or disadvantaged backgrounds. We reveal two main causes of public mistrust: 1. limitations and failures in scientific and technical institutions, and 2. institutionalized mistreatment of marginalized communities. Both, we argue, ultimately help to legitimate the circulation of false information and sow vaccine hesitancy. On the basis of this analysis, we provide recommendations to help restore public trust, and use additional model cases to describe how they might be implemented.http://deepblue.lib.umich.edu/bitstream/2027.42/191756/1/vaccine-hesitancy-STPP-TAP-2021-v5.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/191756/2/vaccine-hesitancy-executive-summary-STPP-TAP-2021-v2-1.pdfDescription of vaccine-hesitancy-STPP-TAP-2021-v5.pdf : In Communities We Trust Institutional Failures and Sustained Solutions for Vaccine Hesitancy (Full Report)Description of vaccine-hesitancy-executive-summary-STPP-TAP-2021-v2-1.pdf : Executive Summary: In Communities We TrustSEL

LLM Implications- one-pager

LLMs have tremendous potential to empower communities and democratize knowledge. But given the concentrated development landscape and the datasets on which they are based, LLMs are unlikely to achieve these goals.Alfred P. Sloan Foundation (grant #G-2021-16769)http://deepblue.lib.umich.edu/bitstream/2027.42/191727/1/large-language-models-one-pager STPP-TAP-2022-v3.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/191727/2/UM TAP Large Language Models Executive Summary 2022.pdfDescription of large-language-models-one-pager STPP-TAP-2022-v3.pdf : one-pagerDescription of UM TAP Large Language Models Executive Summary 2022.pdf : Executive SummarySEL

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