Neither this nor that but our own: AI as a ‘third space’ for Eritrean female genital mutilation survivors in the UK

Eritrean women face a difficult tension between the cultural context in which they experienced female genital mutilation (FGM) in their country of origin, where it was seen as social and moral protection, and the perspective in the UK, where it is considered harmful and a crime. This dissonance may create psychological stress and barriers for women in accessing FGM-related healthcare in the UK. While artificial intelligence (AI) is used widely in healthcare, little attention has been paid to how it could help women navigate these complex cross-cultural and emotional realities of FGM. This paper presents the first exploration of how AI could support Eritrean women who have experienced FGM and are living in the UK in accessing healthcare.As the UK National Health Service promises to become the 'most AI-enabled health system in the world', this conceptual paper argues that AI could create a 'third space' for survivors: an environment existing between these conflicting cultural frameworks, offering a safe and culturally sensitive way for women to disclose their experiences, access support and regain a sense of agency over their health. However, AI interventions also carry significant risks, including the potential for reproducing a new form of medical colonialism in digital spaces, amplifying existing harmful biases in algorithms and raising privacy concerns that could deter survivors from seeking support. Ultimately, this paper calls for urgent co-design of culturally responsive AI interventions with FGM survivors and deeper explorations of how AI can be conceptualised as a 'third space' in cross-cultural healthcare.

Authors

Publication Details

Journal
Medical Humanities
Published
2026-03-11
DOI
https://doi.org/10.1136/medhum-2025-013619
Primary Topic
Female Genital Mutilation/Cutting Issues
Type
article
Field-Weighted Citation Impact
0.00
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article

Neither this nor that but our own: AI as a ‘third space’ for Eritrean female genital mutilation survivors in the UK

Mary Amanuel
Medical Humanities
Female Genital Mutilation/Cutting Issues
article

Neither this nor that but our own: AI as a ‘third space’ for Eritrean female genital mutilation survivors in the UK

Mary Amanuel
article en

Abstract

Eritrean women face a difficult tension between the cultural context in which they experienced female genital mutilation (FGM) in their country of origin, where it was seen as social and moral protection, and the perspective in the UK, where it is considered harmful and a crime. This dissonance may create psychological stress and barriers for women in accessing FGM-related healthcare in the UK. While artificial intelligence (AI) is used widely in healthcare, little attention has been paid to how it could help women navigate these complex cross-cultural and emotional realities of FGM. This paper presents the first exploration of how AI could support Eritrean women who have experienced FGM and are living in the UK in accessing healthcare.As the UK National Health Service promises to become the 'most AI-enabled health system in the world', this conceptual paper argues that AI could create a 'third space' for survivors: an environment existing between these conflicting cultural frameworks, offering a safe and culturally sensitive way for women to disclose their experiences, access support and regain a sense of agency over their health. However, AI interventions also carry significant risks, including the potential for reproducing a new form of medical colonialism in digital spaces, amplifying existing harmful biases in algorithms and raising privacy concerns that could deter survivors from seeking support. Ultimately, this paper calls for urgent co-design of culturally responsive AI interventions with FGM survivors and deeper explorations of how AI can be conceptualised as a 'third space' in cross-cultural healthcare.

Medical Humanities
Gender equality
Openalex Percentile: Top 28%
Female Genital Mutilation/Cutting Issues
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