Towards Trustworthy Mental Health AI: Depression Detection Using Language Models and Explainable AI (XAI)

A trustworthy mental health AI that detects depression using language models and explainable AI.

A trustworthy mental health AI that detects depression using language models and explainable AI. This reference project walks through the whole job, from the problem and the design to the build and the evaluation, so that a developer can study how it works, run it and extend it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are explanations of each result (feature importance or visual evidence), text or speech data preparation, model training, fine-tuning or prompting, evaluation with task-appropriate metrics, and a chat or web interface to use it. The reference build uses LLM APIs, SHAP, Python, Hugging Face Transformers, and spaCy, with a clear project structure, setup instructions and sample data so that it can be run and extended.