Overview

This research project evaluates Large Language Models (LLMs) for mental health applications, with a comprehensive focus on bias and fairness across different demographic groups and clinical scenarios.

Key Objectives

• Bias Detection: Systematically identify and measure bias in LLM responses related to mental health topics
• Fairness Assessment: Evaluate fairness across various demographic groups including age, gender, ethnicity, and socioeconomic status
• Clinical Accuracy: Assess the accuracy and appropriateness of mental health-related responses
• Safety Evaluation: Examine potential risks and safety concerns in mental health applications

Methodology

• Dataset Creation: Curated comprehensive test sets covering various mental health scenarios and demographic contexts
• Multi-Model Evaluation: Tested multiple state-of-the-art LLMs including GPT-4, Claude, and specialized mental health models
• Bias Metrics: Applied established bias detection metrics and developed custom evaluation criteria for mental health contexts
• Expert Review: Collaborated with mental health professionals for clinical validation

Technologies Used

• Python: Primary programming language for data analysis and model evaluation
• Machine Learning Libraries: PyTorch, Transformers, scikit-learn for model interaction and analysis
• Statistical Analysis: Advanced statistical methods for bias measurement and significance testing
• Visualization: Custom dashboards for presenting bias patterns and evaluation results

Key Findings

The research revealed significant bias patterns across different demographic groups and highlighted the importance of careful consideration when deploying LLMs in mental health contexts. The findings contribute to the development of more responsible AI systems in healthcare.

Impact

This work contributes to the growing body of research on responsible AI in healthcare, providing frameworks and metrics that can be used by other researchers and practitioners working on mental health AI applications.