Genevieve Liveley is Reader in Classics at the University of Bristol, where her research and teaching centres upon narratologically inflected studies of the ancient world. Her most recent book, Narratology (Oxford University Press) exposes the dynamic (mis)appropriation of ancient scripts that gives modern narratology its shape. Her new research, on the ancient and future (hi)stories of AI and robots, builds on this work, and seeks a better understanding of the frames, schemata, and scripts that programme cultural narratives about human interaction with artificial humans, automata, and AI. She has published a significant body of original work: three single-authored monographs, a co-edited collection of essays, more than 20 single-authored academic papers in journals and collections, including (among others) articles on the classical tradition, chaos theory, and cyborgs.

Research interests

The exciting vision of this research is to help inform and transform the design and deployment of human-facing AI by bringing the humanities into conversations about human/machine interactions and relationships. By analysing 3000 years of enduring and changing preferences and antipathies in robot and AI stories, it aims to produce new knowledge about the narrative scripts and frames that are deployed when humans and autonomous/intelligent machines interact. In so doing, it will help to inform and shape the design of AI services that are tailored to people's individual needs and situations and inform the technological narrative of the future. This new research will also help to provide proof of concept for the feasibility (and utility) of large-scale diachronic digital data analysis of story-form and build the first open access, searchable/expandable database of AI representation in the Western canon. In so doing, the project will highlight what we can learn about the future of AI in society from the history of AI in stories – from ancient myths to modern movies and media representations. Preliminary research indicates that public attitudes to AI in society are coded by their experience of AI in fiction. So, by allowing a better understanding of the narrative dynamics shaping such coding – that is, the narrative scripts and frames that programme human responses to AI – could help create a step-change in the design and deployment of AI in a range of personalised social contexts.