UCLA

Graph neural networks (GNNs) and large language models (LLMs) have emerged as popular machine learning (ML) models for powering applications such as social recommendation on social media platforms and chat-based assistants, respectively. GNNs and LLMs are both contextual models: GNNs operate on social context in social networks, while LLMs process syntactic and semantic context in language. In conjunction with the proliferation of GNNs and LLMs, there is decreasing trust in the fairness of ML. Unfair ML models cause real-world harm, such as the reinforcement of stereotypes and discrimination in hiring. The unfairness of GNNs is exacerbated by social context (e.g., graph structure, message passing). However, this aspect is not explored in research on the fairness of traditional ML models and requires a deeper principled understanding. Moreover, the open-ended nature of LLM generations can make automatic evaluations of syntactic and semantic context-dependent unfairness difficult.

This dissertation tackles technical challenges in addressing the unfairness of GNNs and LLMs. In the first part, we theoretically and empirically investigate different forms of GNN unfairness (i.e., imputation bias, preferential attachment bias, degree bias), and how they are affected by graph structure and the choice of graph filter. We further propose principled metrics and methods to alleviate GNN unfairness. In the second part of this dissertation, we assess the measurement validity of evaluations of LLM misgendering. In the final part, we return to the relatively simple setting of feedforward neural networks, and even in this setting, we identify and tackle major challenges in obtaining a precise analytical theory of how model design choices and data properties contribute to unfairness. Such a theory for GNNs and LLMs could aid in interpreting model outputs and designing stronger evaluation and mitigation methods for unfairness. Overall, this dissertation develops a principled understanding of and addresses the unfairness of modern ML models, towards preventing the further entrenchment of social inequalities and promoting justice.