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Accepted Paper:
Storylistening: why SF matters for public reasoning about AI
Sarah Dillon
(University of Cambridge)
SF stories are anticipatory narrative models that enable surrogative reasoning about possible futures and the pathways to those futures. Gathering narrative evidence through storylistening can help inform decision-making around AI technologies by governments, businesses and civil society.
Paper long abstract:
Whilst futures studies and practices engage with stories and narrative methods to a certain extent, neither futurists nor decision-makers fully engage with the vast body of SF. This paper presents insights of particular relevance to SF and AI from the co-authored book 'Storylistening: Narrative Evidence and Public Reasoning'. It focuses on the ‘applied’ role of science fiction in the context of public reasoning. SF stories are anticipatory narrative models that enable surrogative reasoning about possible futures and the pathways to those futures. Gathering narrative evidence through storylistening can help inform decision-making around AI technologies by governments, businesses and civil society. Narrative models can aid decision-making with the same caveats and cautions with which scientific models also need to be used. The paper addresses scepticism about such a method, on the one hand driven by fears about the possible lack of robustness of such a method compared with forms of more commonly used evidence or knowledge, on the other driven by fear of 'reducing' SF to 'merely' its cognitive value. It argues that much is lost by not drawing effectively on this important resource for thinking about the future and acting in the present. Focusing on AI, the paper discusses what a rigorous conceptual and practical framework for taking SF stories seriously looks like; how to embed their study into public reasoning alongside other forms of evidence and anticipation; and the kind of changes that are needed to make this happen at every level, from the local to the global.