  The advancement of large language models (LLMs) has demonstrated strong
capabilities across various applications, including mental health analysis.
However, existing studies have focused on predictive performance, leaving the
critical issue of fairness underexplored, posing significant risks to
vulnerable populations. Despite acknowledging potential biases, previous works
have lacked thorough investigations into these biases and their impacts. To
address this gap, we systematically evaluate biases across seven social factors
(e.g., gender, age, religion) using ten LLMs with different prompting methods
on eight diverse mental health datasets. Our results show that GPT-4 achieves
the best overall balance in performance and fairness among LLMs, although it
still lags behind domain-specific models like MentalRoBERTa in some cases.
Additionally, our tailored fairness-aware prompts can effectively mitigate bias
in mental health predictions, highlighting the great potential for fair
analysis in this field.
