Bias in Machine Learning (ML) can lead to unfair treatment of certain groups, particularly in areas like healthcare and finance, where disparate outcomes can have life-altering consequences. New training techniques aim to improve fairness while preserving privacy. Federated Learning (FL) is one such approach, allowing models to be trained on data from many devices without centralizing it. Instead of sharing raw data, each device trains a local model and sends model updates (adjustments based on its local data) to a central server, which aggregates them into a global model. This protects privacy while enabling large-scale training, but differences in data quality, representation, or access across devices can reinforce bias, leading to models that work well for some groups but poorly for others. This project tests whether a debiasing system can effectively mitigate bias in FL without sacrificing model performance. To tackle this, I'm adapting a Reinforcement Learning (RL) system, where an agent learns by interacting with an environment and receiving rewards for beneficial actions. The agent evaluates fairness using feedback from client devices and adjusts the central model’s weights before redistributing it for further training. Using fairness metrics and accuracy as its reward signal, the agent continuously refines its strategy, learning how to mitigate bias while preserving performance. I'm solely responsible for designing, building, testing, and analyzing this system, though I've benefited greatly from the guidance of my mentor, Dr. Afra Mashhadi, insights from her graduate students, and tools developed in prior research. Results from prior work suggest this method can reduce bias while maintaining strong model accuracy, highlighting its potential for improving fairness in FL systems. If successful, this approach could be applied in areas like medical diagnostics, risk assessment in insurance, and hiring algorithms, where biased models can lead to significant real-world harm.

This study explores the content and effectiveness of responses in suicide ideation subreddits, comparing human responses to those generated by Large Language Models (LLMs). Mental health discussions on online platforms such as Reddit provide crucial support for individuals in distress, and as AI tools like LLMs become more common, their role in these sensitive discussions needs to be evaluated. Using data from the r/SuicideWatch and r/depression subreddits from 2020, 2023, and 2024, I analyzed 150 human responses and 150 LLM-generated responses for emotional resonance, support styles, and contextual relevance. The findings revealed that human responses were more empathy-driven, often emphasizing emotional validation and shared experiences, while LLM-generated responses were more focused on providing practical advice. A semantic analysis showed that while LLMs aligned well with the contextual content of posts, they fell short in conveying the emotional depth and personal connection inherent in human interactions. This study highlights the strengths and limitations of AI-generated responses in mental health discussions, suggesting that while LLMs can assist in offering guidance, they are not yet capable of fully replicating the emotional complexity and personal understanding found in human responses. These findings will guide future research aimed at improving AI models to better simulate empathy in sensitive contexts such as mental health.