What is it about?
Artificial Intelligence has been achieving significant advancements aimed at improving human life. For example, ChatGPT, a large language model, can respond to any user's queries. However, it's essential to consider whether AI comprehends the underlying meaning of its recommendations or generated sentences. While this might not be a pressing concern for individuals engaging in casual conversations with AI, it becomes crucial for users dealing with mental health concerns. These users need AI to elucidate the reasons behind specific recommendations. This research delves deeply into the process of grounding AI in mental health and ensuring that it aligns well with human expectations for explanations. The central concept revolves around incorporating the clinical decision-making process as a mechanism to fine-tune AI for decision-making and subsequent explanation provision.
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Why is it important?
This research holds significant relevance at the present moment due to recent concerns surrounding language models. These concerns include inconsistency, dependability, interpretability, and safety. These problems can't be ignored for Mental Health.
Perspectives
The tutorial seeks to integrate symbolic knowledge represented by procedural questions from the Mental Health Questionnaire with the data-driven neural knowledge found in the current pre-trained transformer-based language model. This fusion gives rise to what is known as NeuroSymbolic AI, which emerges as a more robust AI system specializing in the specific domain of mental health.
Manas Gaur
University of Maryland Baltimore County
Read the Original
This page is a summary of: Tutorial: Neuro-symbolic AI for Mental Healthcare, October 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3564121.3564817.
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