From the families we choose to the families we find online: media technology and queer family making

Since the mid-2000s, a number of Western countries have witnessed an increase in the number of children born into ‘alternative’ or ‘queer’ families. Parallel with this queer baby boom, online media technologies have become intertwined with most people’s intimate lives. While these two phenomena have appeared simultaneously, their integration has seldom been explored. In an attempt to fill this gap, the present article explores the ways in which contemporary queer reproduction is interwoven with online media practices. Importantly, the article does not understand online media as a technology that simply facilitates queer kinship; rather, it argues that online media technology is a reproductive technology in its own right. Drawing on empirical examples of media practices of kinning, such as online shopping for donor sperm and locating ‘donor siblings’ via online fora such as Facebook, the article analyses the merging and intersection of online media and queer kinship. These analyses serve as a foundation for an exploration of contemporary kinship and the development of a new theoretical framework for contemporary queer reproduction. Empirically, the examples are from single women’s (i.e. solo mothers) and lesbian couples’ family making. Using Weston's work on ‘chosen families’ as a backdrop for discussion, the article describes families of choice in light of new online kinship connections. In particular, the article focuses on online-initiated connections between donor siblings and how such connections can re-inscribe biology as important to queer kinship. Furthermore, it closely examines how media technology guides queer reproduction in particular directions and how technology causes becoming as a family.

Differences between sperm sharing and egg sharing are morally relevant

Sperm sharing arrangements involve a man (‘the sharer’) allowing his sperm to be used by people seeking donor sperm (‘the recipients’) in exchange for reduced price in vitro fertilisation. Clinics in the UK have offered egg sharing since the 1990s and the arrangement has been subjected to regulatory oversight and significant ethical analysis. By contrast, until now no published ethical or empirical research has analysed sperm sharing. Moreover the Human Fertilisation and Embryology Authority (HFEA) does not record the number of sperm sharing arrangements taking place.This paper describes the sperm sharing process providing an analysis of all the UK clinics advertising sperm sharing services. The ethical rationale for egg sharing is described: reducing the number of women exposed to the risks of stimulation and retrieval. This advantage is absent in sperm sharing where donation has no physical drawbacks. The key adverse social and emotional outcome of gamete sharing arises when the sharer’s own treatment is unsuccessful and the recipient’s is successful. This outcome is more likely in sperm sharing than in egg sharing given sperm from sharers can be used by up to 10 families whereas shared eggs only go to one other family.Given its morally relevant differences from egg sharing, sperm sharing requires its own ethical analysis. The HFEA should begin recording sperm sharing arrangements in order to enable meaningful ethical and policy scrutiny.

Adoption of artificial intelligence in precision matching of donor sperm during assisted reproduction process (Preprint)

UNSTRUCTURED An artificial intelligence (AI) based sperm donor humanized matching system was launched to meet infertile patients' requirements on personalized physical appearance of the expected sperm donor such as blood type, origin, ethnicity, height, weight, body build, skin color, hair, face shape, nose bridge, eyelids, iris color, lips, etc. Relying on high-speed 5G networks, the AI matching information in an encoded pattern is fed back to patients in real time and ranked according to similarity. To date, the highest similarity is up to 96%. This system can provide high efficiency and accuracy and avoid the drawbacks of previous manual operations which were tedious, slow and error-prone. In addition, the system helps patients carrying genetic mutations (including thalassemia, spinal muscular atrophy) avoid off-springs’ genetic diseases by matching donors who are qualified by further genetic testing. This system sets a good example of the smart medical market which can also play an important role in addressing patients' personalized medical requirements in addition to aiding in the diagnosis and treatment of diseases.

Situated Talk: A method for a reflexive encounter with #donorconceived on TikTok

There is a pressing need to facilitate sensitive conversations between people with differing or opposing views. On video-sharing app TikTok, the diverse experiences of donor-conceived people and recipient parents sit uneasily alongside each other, coalescing in hashtags like #donorconceived. This article describes a method ‘Situated Talk’ which uses TikToks to facilitate a reflexive encounter, drawing on three areas of scholarship: media ethnography and elicitation, researcher reflexivity and duoethnography/collaborative autoethnography. We describe how we, as a donor-conceived adult (Giselle) and a queer woman who would need donor sperm to have a child (Clare), employed TikToks from #donorconceived as prompts to facilitate a sensitive conversation and elicit situated insights. We explore three central insights from applying our method: (1) discomfort as a productive tension; (2) unresolved dilemmas; and (3) discovering parallels in experience. Using TikToks as stimuli, ‘Situated Talk’ contributes an innovative method for generating grounded social media insights.

Adoption of artificial intelligence in precision matching of donor sperm during assisted reproduction process (Preprint)

UNSTRUCTURED An artificial intelligence (AI) based sperm donor humanized matching system was launched to meet infertile patients' requirements on personalized physical appearance of the expected sperm donor such as blood type, origin, ethnicity, height, weight, body build, skin color, hair, face shape, nose bridge, eyelids, iris color, lips, etc. Relying on high-speed 5G networks, the AI matching information in an encoded pattern is fed back to patients in real time and ranked according to similarity. To date, the highest similarity is up to 96%. This system can provide high efficiency and accuracy and avoid the drawbacks of previous manual operations which were tedious, slow and error-prone. In addition, the system helps patients carrying genetic mutations (including thalassemia, spinal muscular atrophy) avoid off-springs’ genetic diseases by matching donors who are qualified by further genetic testing. This system sets a good example of the smart medical market which can also play an important role in addressing patients' personalized medical requirements in addition to aiding in the diagnosis and treatment of diseases.