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Short abstract:

This paper examines analogical case study (ACS) analysis as a method for anticipating the implications of emerging technologies and offering recommendations to minimize the harms while maximizing the benefits. It also reflects on the public and policy response to ACS as a form of knowledge.

Long abstract:

In this talk, I provide an overview of the analogical case study (ACS) approach—which is based on the idea that the way that societies have managed technologies in the past gives us important insights into how they might respond in the future. We use historical data across sectors to anticipate how an emerging technology might evolve. I show how we have developed the ACS approach through reports on facial recognition technology in primary schools, COVID-19 vaccine hesitancy, large language models/generative AI and now small modular nuclear reactors. All include recommendations for policymakers and technology developers, executive summaries, and supplemental materials to generate public attention. We have found that we were able to accurately predict many of the impacts of these technologies. I will also discuss how we tried to package and publicize our findings to maximize our impact on public and policy discourse, and reflect on how journalists, civil society groups, and policymakers have responded to our findings as an example of the politics of evidence in policymaking. While some have been puzzled by our “meta-analysis” and lack of quantitative data, most appreciate our clear and comprehensive analysis and recommendations. In the case of large language models in particular, where there was (and still is) very little analysis or evidence of its social impacts, we have successfully been able to shape the debate, including shaping research, advocacy, and policy agendas.