Predicting Success in the Embryology Lab: The Use of Algorithmic Technologies in Knowledge Production

Abstract

This article analyzes local algorithmic practices resulting from the increased use of time-lapse (TL) imaging in fertility treatment. The data produced by TL technologies are expected to help professionals pick the best embryo for implantation. The emergence of TL has been characterized by promissory discourses of deeper embryo knowledge and expanded selection standardization, despite professionals having no conclusive evidence that TL improves pregnancy rates. Our research explores the use of TL tools in embryology labs. We pay special attention to standardization efforts and knowledge-creation facilitated through TL and its incorporated algorithms. Using ethnographic data from five UK clinical sites, we argue that knowledge generated through TL is contingent upon complex human–machine interactions that produce local uncertainties. Thus, algorithms do not simply add medical knowledge. Rather, they rearrange professional practice and expertise. Firstly, we show how TL changes lab routines and training needs. Secondly, we show that the human input TL requires renders the algorithm itself an uncertain and situated practice. This, in turn, raises professional questions about the algorithm’s authority in embryo selection. The article demonstrates the embedded nature of algorithmic knowledge production, thus pointing to the need for STS scholarship to further explore the locality of algorithms and AI.

Publication DOI: https://doi.org/10.1177/01622439211057105
Divisions: College of Business and Social Sciences > School of Social Sciences & Humanities > Sociology and Policy
College of Business and Social Sciences > School of Social Sciences & Humanities
Additional Information: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). Funder: Wellcome Trust 108577/Z/15/Z
Publication ISSN: 1552-8251
Full Text Link:
Related URLs: https://journal ... 622439211057105 (Publisher URL)
PURE Output Type: Article
Published Date: 2021-11-15
Published Online Date: 2021-11-15
Accepted Date: 2021-10-15
Authors: Geampana, Alina (ORCID Profile 0000-0001-7388-7181)
Perrotta, Manuela

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