Article Excerpt: LLMs, such as generative pre-trained transformers (GPT), have rapidly transformed various healthcare applications, including mental health support. These models have been explored for their potential to alleviate clinician burnout and expand access to mental health services, particularly as psychological distress has risen, especially among minority groups.
While previous research has demonstrated LLMs’ success in tasks like risk prediction and cognitive reframing, concerns about their ethical deployment have emerged.
Notably, recent failures, such as the death of a Belgian man after interacting with a GPT-based chatbot and harmful dieting advice from the Tessa chatbot, highlight the risks of automated mental health care.
Most prior work on automated psychotherapy has focused on rule-based or retrieval-based approaches. However, the potential for bias in LLM responses, particularly in terms of race and demographics, has not been adequately addressed.
Although existing studies have highlighted biases in artificial intelligence (AI) systems, the impact of these biases on mental health support in diverse populations remains underexplored.
Full Article: https://tinyurl.com/bffbhwxn
Article Source: AZO Robotics