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Towards Explainable Neural Network Fairness, Mengdi Zhang

Dissertations and Theses Collection (Open Access)

Neural networks are widely applied in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug, non-transparent and subject to fairness issues. Discrimination has been observed in various machine learning models, including Large Language Models (LLMs), which calls for systematic fairness evaluation (i.e., testing, verification or even certification) before their deployment in ethic-relevant domains. If a model is found to be discriminating, we must apply systematic measure to improve its fairness. In the literature, multiple categories of fairness improving methods have been discussed, including pre-processing, in-processing and post-processing.
In this dissertation, …