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Paper short abstract

AI systems are rapidly deployed across societal sectors. Yet, the question of what happens after their deployment has remained limitedly studied. In this presentation, we will explore emerging AI maintenance practices to discuss how we can study these often elusive and constantly evolving practices.

Paper long abstract

AI systems are rapidly deployed across societal sectors. Yet, the question of what happens after their deployment has remained limitedly explored; how are these systems maintained, to, for example, avoid ‘model drift’ or ‘performance degradation’ over time? One emerging solution to managing the burden of maintaining AI systems is to deploy additional AI models to maintain existing ones. In this presentation, we will explore this notion through a dual approach of ‘reading computer science papers’ (Amoore et al., 2023) and exploring public and commercial materials that articulate the practices and values of these services for various sectors. With this dual approach, we aim to capture how these emerging maintenance practices are technically and publicly introduced, negotiated, and problematised. Examining technical and public debates in conjunction helps us identify how these two realms inform and reinforce each other, and/or give way to disconnects and frictions between them. Specifically, we ask what forms of value ‘AI to maintain AI’ promises to bring and to whom, and how the act of ‘maintenance’ is being reconfigured in these practices. In doing so, we bring STS scholarship on maintenance and repair into contact with Critical AI scholarship studying the value production in AI economies. With this presentation, we aim to use our ongoing research on AI maintenance practices and the methodological struggles we encountered to discuss how we can study often elusive and constantly evolving emerging AI practices in ways that allow us to question the economic and epistemological premises that underpin them.