When craft kicks back: Embryo culture as knowledge production in the context of the transnational fertility industry

Abstract

The multibillion-dollar fertility industry promotes standardization in in vitro fertilization laboratories. Transnational pharmaceutical and biotechnological giants distribute a wide range of fertility products, from embryo culture mediums and incubator technologies to add-ons such as time-lapse embryo monitoring. These technologies are designed to standardize and automate knowledge production regarding embryonic viability. More effective knowledge production enables the more effective selection of embryos for transfer, which in turn leads to more future babies and enables economic scaling-up. Drawing on two multi-sited ethnographic studies at eight fertility clinics in Finland during 2013–2020, this article discusses how knowledge about embryos is produced in the processes and practices of embryo culture. We argue that automation and standardization in clinical practice are not always perceived as economically desirable. Sometimes standard technologies do not replace hands-on knowledge production, although they may transform it. The technologies are also perceived as modifying the object of knowledge itself in undesired or unnecessary ways. In such cases, concerns are raised regarding the best interests of patients, embryos and future babies, who might be better served by masterful laboratory craftwork. We conclude that embryo culture is not only a site of knowledge production – one that aims to make babies and parents through standard and craftwork knowledge practices – but also a site of multiple bio-economies of assisted reproduction, some of which resist automation and standardization.

Artificial intelligence and assisted reproductive technology: Applying a reproductive justice lens

“…Furthermore, the transnational fertility industry is also expanding rapidly, and is being reshaped by forces such as globalisation, the significance of patenting, and increasing consolidation. Consolidation manifests itself in several ways, including the merging of fertility clinics into larger international chains, the rise of private equity investments, and the expansion of company portfolios to incorporate every step of the fertility treatment journey and wider ranges of fertility products (Helosvuori and Homanen, 2022; Van de Wiel, 2019). It is likely that AI-based reproductive medicine will be centred around a few of these big fertility companies and health groups; further consolidating the market.…”

“…Uncritical enthusiasm over AI solutions resolving (fertility) health problems (Polonski, 2018) may also further reinforce power dynamics where authority to know (best) is shifted from both intended parents and clinicians to machines (Ho, 2023). Reproductive decision making in clinics often involves intended parents, in some cases even in the laboratory (Helosvuori and Homanen, 2022). Information about embryo development and selection are shared routinely with intended parents, who are also given a role in making decisions about embryo selection (Helosvuori, 2019; Helosvuori and Homanen, 2022).…”

“…Reproductive decision making in clinics often involves intended parents, in some cases even in the laboratory (Helosvuori and Homanen, 2022). Information about embryo development and selection are shared routinely with intended parents, who are also given a role in making decisions about embryo selection (Helosvuori, 2019; Helosvuori and Homanen, 2022). Through information-sharing, patients become involved in the process of knowledge production and, sometimes, even in making decisions about interventions that render previous conceptualisations of embryo viability inaccurate.…”

“…Broadly defined, AI here refers machines mimicking human decisions and tasks and ML is a subset of AI technology, which uses statistical methods to enable machines to learn from experience. 1 In the field of assisted reproduction, AI/ML applications (from here on simply, AI) and related technologies have been hailed as (potentially) significant and ground-breaking, not least because they promise standardisation and automation in IVF ( in-vitro fertilisation ) clinics – a precondition for scaling up and branching out in the fertility bioindustry (Franklin, 2013; Helosvuori and Homanen, 2022). AI data-driven algorithms promise time- and cost-effective selection of ‘high-quality’ reproductive cells (gametes/embryos) and successful personalised treatments (through prediction of IVF outcome based on patient data; for example, Ivshin et al, 2022).…”