I research medical technologies and material discursive practices of the body. Employing theoretical tools from feminist science studies, I find my academic home at the juncture of STS and medical sociology.
Ericka Johnson
Deputy Head of Department, Professor
My research explores how the world becomes data. With a background in Science & Technology Studies and medical humanities, I’m looking at the nexus of ontologies, epistemologies and AI. What happens when the data that represents the world meets AI?
Refracting bodies through technology
Technologies refract discourses. They articulate silent understandings, highlight cultural values, emphasize subtle ideas. Studying technological artefacts can help trace the discursive contours within which we live. And give us words to challenge them.
Publications
2025
Continue to DOI
2024
Continue to DOI
Books
A Cultural Biography of the Prostate
What contemporary prostate angst tells us about how we understand masculinity, aging, and sexuality.
About the book
We are all suffering an acute case of prostate angst. Men worry about their own prostates and those of others close to them; women worry about the prostates of the men they love. The prostate—a gland located directly under the bladder—lurks on the periphery of many men's health issues, but as an object of anxiety it goes beyond the medical, affecting how we understand masculinity, aging, and sexuality. In A Cultural Biography of the Prostate, Ericka Johnson investigates what we think the prostate is and what we use the prostate to think about, examining it in historical, cultural, social, and medical contexts.
Research
Social Complexity and Fairness in Synthetic Medical Data
Medical research is increasingly using big data and powerful computers. But one problem with this is that the powerful computer methods we have now for dealing with big data make it easy to figure out who is who in a data set, which is especially bad for the privacy issues related to sensitive medical data. A solution is to use machine learning to generate synthetic data from the raw data, that is, to make a fake data set that still represents important elements of the data.
While this is good in theory, early results from this process indicates that machine learning generated datasets may over-represent majority elements and diminish representation of minority elements. When applied to medical data, this would mean that synthetic datasets probably have an over-representation of ‘standard’ patients, i.e. white, middle class, 35-yr old men, despite decades of regulation and research practice that has tried to include other patients and bodies in medical research. Additionally, we are discovering the production of difficult to detect intersectional hallucinations in synthetic data.
Funder: WASP-HS (NetX)
Representative and equitable synthetic data: ML algorithms and working practices
This 12-month, 3-partner project aims to develop a combined sociotechnical tool and process to ensure that Swedish industry engages with synthetic data that is fair, equitable and representative of edge cases. Our work both develops technical solutions for representative synthetic data and explores the market to discover what alignment needs Swedish industry has for ML and synthetic data. We are integrating understandings of intersectional hallucinations in this work.
Funder: Vinnova
Funder: Vinnova
Research projects
News
In media
Gendering Drugs: Feminist Studies of Pharmaceuticals
On the frontier of feminist technoscience research, Ericka Johnson’s collaborative project Gendering Drugs: Feminist Studies of Pharmaceuticals (Palgrave Macmillan, 2017) explores how the gendered body is produced in and by medical technologies".
Ericka Johnson interviewed by Taylor Fox-Smith, Macquarie University and the United States Studies Centre in Sydney, Australia in a podcast, New Books Network.
