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Paper short abstract

This paper reflects on five AI methods used by the author over the past five years. Together, the paper considers the success of deploying them in different research and pedagogic settings, and the value of using them to test and critique an array of AI phenomena from outputs to supply-chains.

Paper long abstract

This paper is a reflection on five AI methods used by the author in different scenarios over the past five years, from empirical work studying AI competitions and autonomous driving, to postgraduate seminars studying AI controversies and PhD workshops exploring creative engagement with generative AI tools. Together, the paper details five AI methods I refer to as: creative AI methods, LLMs as topic modeller, AI technography, operational methods, and public AI repositories. Collectively, the approaches contribute to innovative methodological work in STS, media studies, computer science, and associated fields that have, in recent times, been developed to use, test, and critique the huge array of AI tools that have made their way into the public and academic consciousness since 2022, from the slick user interfaces of genAI products like ChatGPT to eminently usable topic modelling LLMs like BERTopic. Drawing on academic traditions and trajectories that have used AI as a method or methodological approach, I argue that each method constructs a specific version, and vision, of AI. I understand these different versions as constructing AI as: user interface, automated tool, innovation practice, developmental process, and public controversy, respectively. I reflect on the success of testing them in research and pedagogic settings, and the relative value of using such approaches for critiquing – amongst other things – AI outputs, discourses, supply-chains, and effects. The paper concludes by offering suggestions for how AI methods may be further developed to aid the use, testing, and critique of AI phenomena in the future.