THE MINORIA PROJECT
MINORITY-AI
Minorities & Artificial Intelligence:
Towards an advanced mental health assessment system in minority languages
What is a Minority Language ?
A minority language is an idiom spoken by a minority of the population in a given territory, in competition with one or more other majority or dominant languages: provincial, regional, national or ethnic linguistic minorities.
A minoritized language is an idiom that has suffered marginalization, persecution or even prohibition at some point in the history of its speakers.
Mental Health in a Minority Language
Mental health may be linked to cultural identity and vary according to local psychosocial factors. Some minority languages may have specific forms of expression or terms to describe distress and associated emotions, which do not always have equivalents in majority languages.
Similarly, the way in which stress is expressed may be influenced by psycholinguistic norms. For example, some cultures may encourage open expression of emotions, while others may value restraint.
Finally, minority language communities may have specific rituals, practices or strategies for expressing and managing stress or anxiety, such as ceremonies, songs or stories.
AI for mental health in minority languages
The potential of artificial intelligence in terms of language models and machine learning can be put at the service of health professionals to improve the consideration of diversity and the assessment of mental health difficulties directly in minority languages, without going through translation or interpretation, for the sake of greater precision and better inclusion.
Explore how we bridge the gap in mental healthcare
- AI-Driven Evaluation: Leverage Small Language Models (SLMs) to enhance PTSD screening accuracy and reduce diagnostic disparities 4.
- Cultural Sensitivity: Models trained on diverse datasets to address unique challenges faced by minority populations 7.
- Clinician Collaboration: Tools designed to support—not replace—mental health professionals 3.
