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Gender and AI

How does the use of data-driven systems exacerbate inequalities in access to primary healthcare for transgender and non-binary people in the UK?

Project lead
Kavya Kartik

Project background

Transgender and non-binary people experience many barriers to healthcare access, which can have a huge impact on their health and wellbeing. The use of data-driven systems further entrenches existing inequalities in healthcare. A London Assembly Health Committee investigation found, among other things, that NHS IT systems are not able to record trans status consistently and inclusively, which affects the care received by transgender and non-binary people.

Data-driven systems are often designed for a ‘typical’ user and, consequently,  as fixed and concrete (such as ‘male’, ‘female’, and ‘other’ or simply ‘transgender’). This normative, binary understanding of gender is embedded in various data-driven systems in the UK, and results in the exclusion of people who do not fit any of these categories/identities. The often opaque design of these systems means that there are still many unknowns around how they work – both for people seeking care as well as those entrusted with delivering it.

In order to assess the consequences of these gendered systems, we need to meaningfully consider and document the lived experiences of the transgender and non-binary people who regularly interact with them. We also need to understand how, when and where assumptions about gender enter the development of these data-driven systems.

This project’s research question is: How does the use of data-driven systems, which rely on and reinforce binary data categorisation, exacerbate inequalities in access to primary healthcare for transgender and non-binary people in the UK?

Project aims

We will examine the challenges faced by transgender and non-binary people face in providing information that is ‘legible’ to data-driven systems, and parallelly, the challenges of collecting and storing data about ‘non-typical’ users. An intersectional feminist lens will be adopted, focusing on the co-production of knowledge with those people most affected by these data-driven systems.

The key objectives for this project:

  1. Better understand transgender and non-binary people’s experiences of data-driven systems used in primary healthcare in the UK.
  2. Analyse how the gendered design of these systems makes it challenging to record information about those outside of what the system understands as ‘typical’.
  3. Co-develop, with transgender and non-binary people, a framework of recommendations for embedding values of gender equality and inclusivity in the design of public sector data-driven systems.

Image credit: Orbon Alija