Science after humans: an ethnography of a 'science automation lab'

Student thesis: Phd


This thesis is an ethnographic exploration of the emerging field of scientific research known as 'science automation'. Taking advantage of technological developments in computing and engineering, science automation researchers aim to automate, in part or in full, the scientific process itself. Drawing on twelve months of fieldwork carried out at the Manchester Automation Lab, I look at the Lab's development and deployment of a 'robot scientist' – a system which mixes artificial intelligence and robotics with the intention of producing a 'robot' capable of autonomously carrying out an entire experimental cycle in microbiology. By looking closely at the work carried out by members of this and of partner laboratories, as well as the broader context in which their research is situated, I demonstrate how this project is circumscribed by human conditions intrinsic to scientific practice, despite its intention to automate scientific processes. In this thesis, I highlight the facets of human embeddedness encountered by this project. From the project's history and composition, through its routine research practices, to the political economy in which these practices are situated, I show how the research carried out at the Manchester Automation Lab is influenced by both internal and external circumstances. To that end, this thesis addresses questions at the heart of science automation itself: what are the practices, rationales, structures, and constraints fundamental to the development of a 'robot scientist'? What are the challenges, obstacles, opportunities, and solutions faced by researchers engaged in such a project? In addressing these questions, this thesis engages with and contributes to discussions in science and technology studies and in the anthropology of science, addressing broader issues such as post-humanism, science funding, and the timescales of laboratory settings. Further, this thesis adds ethnographic depth to the understanding of issues such as the production and circulation of data, scientists' understanding of material causality, and analyses of failure and success in scientific settings.
Date of Award1 Aug 2021
Original languageEnglish
Awarding Institution
  • The University of Manchester
SupervisorJeanette Edwards (Supervisor) & Penelope Harvey (Supervisor)


  • robotics
  • anthropology of science
  • science and technology studies
  • artificial intelligence

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